Adaptive-optimal control involves re-identification of the machining process and the model obtained is used to calculate the optimal process parameters.
Optimal control characterizes the addiction of the technical and economic indicators to process parameters. Characteristic for performance technical indicators is that their dependence to parameter values of process has a limitative, what leads to one of the following conclusions, appropriately or inappropriately, and therefore can serve as restrictions in optimization problem.
Economic indicators have a continuous dependence of process parameters and therefore they are used as objective functions.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Machinability data systems aim to select the proper cutting speed and feed rate for a machining operation given characteristics of the operation such as the type of machining, machine tool, cutting tool, workpiece, and other parameters besides speed and feed. There are two main types of machinability data systems: database systems which store data from experiments and experience to provide recommendations, and mathematical model systems which go beyond simply retrieving data by attempting to predict optimal cutting conditions through mathematical models.
This document discusses various production scheduling techniques. It begins by explaining the functions of production control and scheduling such as releasing orders, assigning work, sequencing jobs, and monitoring capacity and priority status. It then covers topics like machine loading, loading charts, objectives of loading, and data requirements for production scheduling. Finally, it summarizes different scheduling tools and techniques including master scheduling, aggregate planning, material requirements planning, Gantt charts, perpetual scheduling, Johnson's rule for scheduling jobs on multiple machines, batch production scheduling, and line of balance techniques.
The document discusses various production scheduling concepts and methods. It describes the loading and scheduling process, which involves determining the work required, computing total time needed, and adding it to existing workplans. Scheduling then determines operation start/finish times. Master scheduling and Gantt charts are also referenced. Benefits of scheduling include reduced inventory and setups. MRP, Kanban, dispatching, progress reporting and expediting are additionally summarized.
The document discusses work study and method study. It defines them, outlines their objectives and basic procedures. Work study aims to utilize resources efficiently while method study examines work processes to develop more effective methods. The key steps in method study are selecting work, recording the current method, examining it critically, developing an improved method, defining and installing the new method, and maintaining it. Various charts and diagrams used in method study are also described such as process charts, flow diagrams and string diagrams.
Work measurement involves developing time standards for work tasks. A survey found 95% of manufacturing firms use work measurement for purposes like wage incentives, estimating costs, and production planning. Techniques for setting time standards include direct time studies, using standard data, predetermined systems, experience estimates, and work sampling. Computerized systems provide advantages like reducing the time to set standards, greater accuracy and uniformity, easier maintenance, and improved manufacturing data.
This document discusses statistical process control (SPC), which uses statistical methods to monitor and control processes to improve quality. SPC aims to ensure processes operate efficiently and produce specification-conforming products with less waste. Key SPC tools include control charts, histograms, cause-and-effect diagrams and check sheets. Control charts in particular plot process data over time to identify changes or variability. SPC provides benefits like reduced waste, lower costs, improved customer satisfaction and early problem detection and prevention.
This document discusses production planning and control. It outlines several key objectives of production planning including minimizing costs and inventory while maximizing customer service and production efficiency. The document then describes different types of production systems like continuous, job-based, and intermittent production. It also discusses important aspects of production like product design, development, marketing, functional operations, aesthetics, and profit considerations. Standardization, simplification, and break-even analysis are also covered as important strategies for production.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Machinability data systems aim to select the proper cutting speed and feed rate for a machining operation given characteristics of the operation such as the type of machining, machine tool, cutting tool, workpiece, and other parameters besides speed and feed. There are two main types of machinability data systems: database systems which store data from experiments and experience to provide recommendations, and mathematical model systems which go beyond simply retrieving data by attempting to predict optimal cutting conditions through mathematical models.
This document discusses various production scheduling techniques. It begins by explaining the functions of production control and scheduling such as releasing orders, assigning work, sequencing jobs, and monitoring capacity and priority status. It then covers topics like machine loading, loading charts, objectives of loading, and data requirements for production scheduling. Finally, it summarizes different scheduling tools and techniques including master scheduling, aggregate planning, material requirements planning, Gantt charts, perpetual scheduling, Johnson's rule for scheduling jobs on multiple machines, batch production scheduling, and line of balance techniques.
The document discusses various production scheduling concepts and methods. It describes the loading and scheduling process, which involves determining the work required, computing total time needed, and adding it to existing workplans. Scheduling then determines operation start/finish times. Master scheduling and Gantt charts are also referenced. Benefits of scheduling include reduced inventory and setups. MRP, Kanban, dispatching, progress reporting and expediting are additionally summarized.
The document discusses work study and method study. It defines them, outlines their objectives and basic procedures. Work study aims to utilize resources efficiently while method study examines work processes to develop more effective methods. The key steps in method study are selecting work, recording the current method, examining it critically, developing an improved method, defining and installing the new method, and maintaining it. Various charts and diagrams used in method study are also described such as process charts, flow diagrams and string diagrams.
Work measurement involves developing time standards for work tasks. A survey found 95% of manufacturing firms use work measurement for purposes like wage incentives, estimating costs, and production planning. Techniques for setting time standards include direct time studies, using standard data, predetermined systems, experience estimates, and work sampling. Computerized systems provide advantages like reducing the time to set standards, greater accuracy and uniformity, easier maintenance, and improved manufacturing data.
This document discusses statistical process control (SPC), which uses statistical methods to monitor and control processes to improve quality. SPC aims to ensure processes operate efficiently and produce specification-conforming products with less waste. Key SPC tools include control charts, histograms, cause-and-effect diagrams and check sheets. Control charts in particular plot process data over time to identify changes or variability. SPC provides benefits like reduced waste, lower costs, improved customer satisfaction and early problem detection and prevention.
This document discusses production planning and control. It outlines several key objectives of production planning including minimizing costs and inventory while maximizing customer service and production efficiency. The document then describes different types of production systems like continuous, job-based, and intermittent production. It also discusses important aspects of production like product design, development, marketing, functional operations, aesthetics, and profit considerations. Standardization, simplification, and break-even analysis are also covered as important strategies for production.
This document discusses process planning activities including:
1. Setting process parameters, work holding devices, inspection methods, and considering economics of process planning.
2. Calculating process parameters such as cutting speed, feed rate, and depth of cut which depend on factors like the workpiece and tool materials.
3. Selecting work holding devices like jigs and fixtures to precisely position and hold the workpiece during machining operations in order to increase productivity and part quality.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document discusses various topics related to computer aided process planning and production planning and control, including:
- Process planning and its role in CAD/CAM integration.
- Responsibilities and activities of process planning engineers such as drawing interpretation, process selection, and documentation.
- Production planning activities like aggregate production planning, master production scheduling, material requirements planning, and capacity planning.
- Production control activities including shop floor control, inventory control, and manufacturing resource planning.
The document discusses various production planning and scheduling functions including scheduling, loading, sequencing, expediting, Gantt charts, line of balance, linear scheduling method, batch production scheduling, MRP, kanban, dispatching, progress reporting, and manufacturing lead time. Scheduling determines when operations are performed and works are completed. Loading adds total operation times to planned workstation utilization. Sequencing and dispatching authorize starting operations. Expediting ensures plans meet commitments.
Statistical process control is defined as and use of statistical technique to control a process or production method .It is used in manufacturing or production process to measure how consistently a product perform according to its design specification.
Product planning identifies market requirements to define a product's features. It serves as the basis for pricing, distribution, and promotion decisions. Value analysis aims to increase value, defined as function over cost, by improving function or reducing cost. Lack of product planning can lead to unsatisfied customers, quality issues, and loss of brand name. Process planning involves routing, scheduling, dispatching, and follow up based on product information, processes, capacity, orders, due dates, and resources. Economic batch quantity determines the optimal batch size to minimize average costs by balancing setup costs and inventory holding costs.
The document discusses various production scheduling concepts and methods. It describes the loading and scheduling process, which involves determining the work required, computing total time needed, and adding it to existing workplans. Scheduling then determines operation start/finish times. Master scheduling and Gantt charts are also referenced. Benefits of scheduling include reduced inventory and setups. MRP, Kanban, dispatching, progress reporting and expediting are additionally summarized.
The document discusses work study and method study. It provides details on the basic procedures for method study which include selecting work to study, recording the existing method, examining the facts critically, developing a more efficient method, defining the new method, installing it, and maintaining it. It describes different charts that can be used for process recording like operation process charts, flow process charts, two-handed process charts, and multiple activity charts. The principles of motion economy relating to the human body, work place arrangement, and tool/equipment design are also summarized.
Statistical process control (SPC) is a method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste (rework or scrap). SPC can be applied to any process where the "conforming product" (product meeting specifications) output can be measured. Key tools used in SPC include control charts; a focus on continuous improvement; and the design of experiments. An example of a process where SPC is applied is manufacturing lines.
Statistical process control (SPC) is a method that uses statistical methods to monitor processes and ensure they operate efficiently. Key tools in SPC include control charts, which graph process data over time and establish upper and lower control limits to detect assignable causes of variation. Control charts come in two main types - variables charts that monitor quantitative measurements like weight or temperature, and attributes charts that count defects. The advantages of SPC include increased stability, predictability, and ability to detect attempts to improve processes. SPC has various applications in pharmaceutical manufacturing for monitoring characteristics like drug potency, fill weight, and microbial counts.
Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
This document summarizes a seminar on statistical process control. It discusses key topics like process capability, estimating inherent capability from control charts, and Juran's 10 steps for quality improvement. Process capability refers to a process's ability to achieve results within specifications. It is quantified using indices like Cp and Cpk which measure potential and performance based on process data. Assumptions like statistical control are important for accurately applying these indices. Quality improvement involves building awareness, setting goals, training, implementing projects, and maintaining momentum through regular systems.
Product planning identifies market requirements to define a product's features. It serves as the basis for pricing, distribution, and promotion decisions. Value analysis aims to increase value, defined as function over cost, by improving function or reducing cost. Lack of product planning can lead to unsatisfied customers, quality issues, and loss of brand name. Process planning involves routing, scheduling, dispatching, and follow up based on product information, processes, capacity, orders, due dates, and resources. Economic batch quantity determines the optimal batch size to minimize average costs by balancing setup costs and inventory carrying costs.
The document discusses the application of statistical tools to enhance productivity and quality control in industries. It explains key concepts like process control, process capability indices, acceptance sampling plans, and their use in quality management. Statistical process control techniques like control charts are used to monitor processes and make data-driven decisions about product and process quality. Acceptance sampling balances protecting consumers from defects and encouraging quality production.
Statistical Process Control Training Online - Tonex TrainingBryan Len
It’s all about key concepts behind SPC or statistical process control, a statistically-based family of tools used to monitor, control, and improve processes.
All the attendees of Tonex statistical process control training will learn about the details of SPC, control charting, other procedures and tools to apply them in their projects.
Learn about :
Statistical process control (SPC) terminology & key principals
Learn how SPC integrates into the total quality system
Variation in manufacturing processes such as patterns
Learn about data collection, control charts
Techniques and tools to implement statistical process control
Recognize the fundamentals of process sampling strategy
Differentiate methods and tools to implement and assess SPC
Select and use recommended SPC practices
Course designed for:
Production Engineers, quality managers,
Operators, project managers,
Product process control, analysts,
Quality process, improvement associates
Other people engaged with SPC process
Course Topics :
What is Statistical process control (SPC)?
Introduction to Process Variation
Control Charts
7-QC Tools & 7-SUPP Tools
The Relationship Between Statistical Quality Control and Statistical Process Control
Statistical process control (SPC) Workshop
Want to learn more ?
Visit tonex.com for statistical process control training detail.
Statistical Process Control Training Online - Tonex Training
https://www.tonex.com/training-courses/statistical-process-control-training-spc-training/
This document discusses statistical process control (SPC), including its main concepts, tools, and applications. SPC uses statistical methods to monitor and control manufacturing processes. It involves taking random samples from a process and measuring their characteristics to create control charts, which are used to evaluate if the process is stable and meeting requirements. The key advantages of SPC include improved quality, reduced variability, and higher production efficiency. It can be applied in various industries to minimize defects and costs. Control charts are the most important SPC tool for differentiating common from special causes of variation and determining if a process is in control.
Abstract The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control is largely used in industries for monitoring the process parameters. It is a standard method for visualizing and controlling processes on the basis of measurements of randomly selected samples. The decisions about what needs to be improved, the possible methods to improve it, and the steps to take after getting results from the charts are all made by humans and based on wisdom and experience. The statistical process control described in this paper gives the details about the SPC, its advantages and limitation, applications and information regarding the control charts. Keywords: Statistical Process Control, Control chart, 5M’s, Capability Indices.
Design of an accuracy control system in ship building industryGopalakrishnan P
This document summarizes a study on implementing an Accuracy Control System (ACS) in an Indian shipyard to improve quality and reduce reworks. A pilot study was conducted on the plasma cutting machine. Data on dimensional variations was collected and control charts were developed. The analysis found the process to be out of control. Major causes of variation like fluctuations in power supply voltage and issues with input design data were identified through cause-and-effect analysis and testing. It was recommended to use advanced voltage regulators and calibrate the input design data to minimize variations and improve the plasma cutting accuracy. The ACS is expected to enhance quality and reduce production costs.
This document discusses optimization and tooling requirements for CNC machines. It covers optimization of machining costs and production rates based on cutting speed. It also discusses the role of computerized optimization systems at different levels. For tooling requirements, it describes various tool holding systems like modular quick change systems, tool holder spindle connections, cutting tool clamping systems, milling cutter drivers, side lock chucks, collet chucks, hydraulic chucks, and milling chucks. It also discusses tool magazines and automatic tool changers.
This document discusses process planning activities including:
1. Setting process parameters, work holding devices, inspection methods, and considering economics of process planning.
2. Calculating process parameters such as cutting speed, feed rate, and depth of cut which depend on factors like the workpiece and tool materials.
3. Selecting work holding devices like jigs and fixtures to precisely position and hold the workpiece during machining operations in order to increase productivity and part quality.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document discusses various topics related to computer aided process planning and production planning and control, including:
- Process planning and its role in CAD/CAM integration.
- Responsibilities and activities of process planning engineers such as drawing interpretation, process selection, and documentation.
- Production planning activities like aggregate production planning, master production scheduling, material requirements planning, and capacity planning.
- Production control activities including shop floor control, inventory control, and manufacturing resource planning.
The document discusses various production planning and scheduling functions including scheduling, loading, sequencing, expediting, Gantt charts, line of balance, linear scheduling method, batch production scheduling, MRP, kanban, dispatching, progress reporting, and manufacturing lead time. Scheduling determines when operations are performed and works are completed. Loading adds total operation times to planned workstation utilization. Sequencing and dispatching authorize starting operations. Expediting ensures plans meet commitments.
Statistical process control is defined as and use of statistical technique to control a process or production method .It is used in manufacturing or production process to measure how consistently a product perform according to its design specification.
Product planning identifies market requirements to define a product's features. It serves as the basis for pricing, distribution, and promotion decisions. Value analysis aims to increase value, defined as function over cost, by improving function or reducing cost. Lack of product planning can lead to unsatisfied customers, quality issues, and loss of brand name. Process planning involves routing, scheduling, dispatching, and follow up based on product information, processes, capacity, orders, due dates, and resources. Economic batch quantity determines the optimal batch size to minimize average costs by balancing setup costs and inventory holding costs.
The document discusses various production scheduling concepts and methods. It describes the loading and scheduling process, which involves determining the work required, computing total time needed, and adding it to existing workplans. Scheduling then determines operation start/finish times. Master scheduling and Gantt charts are also referenced. Benefits of scheduling include reduced inventory and setups. MRP, Kanban, dispatching, progress reporting and expediting are additionally summarized.
The document discusses work study and method study. It provides details on the basic procedures for method study which include selecting work to study, recording the existing method, examining the facts critically, developing a more efficient method, defining the new method, installing it, and maintaining it. It describes different charts that can be used for process recording like operation process charts, flow process charts, two-handed process charts, and multiple activity charts. The principles of motion economy relating to the human body, work place arrangement, and tool/equipment design are also summarized.
Statistical process control (SPC) is a method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste (rework or scrap). SPC can be applied to any process where the "conforming product" (product meeting specifications) output can be measured. Key tools used in SPC include control charts; a focus on continuous improvement; and the design of experiments. An example of a process where SPC is applied is manufacturing lines.
Statistical process control (SPC) is a method that uses statistical methods to monitor processes and ensure they operate efficiently. Key tools in SPC include control charts, which graph process data over time and establish upper and lower control limits to detect assignable causes of variation. Control charts come in two main types - variables charts that monitor quantitative measurements like weight or temperature, and attributes charts that count defects. The advantages of SPC include increased stability, predictability, and ability to detect attempts to improve processes. SPC has various applications in pharmaceutical manufacturing for monitoring characteristics like drug potency, fill weight, and microbial counts.
Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
This document summarizes a seminar on statistical process control. It discusses key topics like process capability, estimating inherent capability from control charts, and Juran's 10 steps for quality improvement. Process capability refers to a process's ability to achieve results within specifications. It is quantified using indices like Cp and Cpk which measure potential and performance based on process data. Assumptions like statistical control are important for accurately applying these indices. Quality improvement involves building awareness, setting goals, training, implementing projects, and maintaining momentum through regular systems.
Product planning identifies market requirements to define a product's features. It serves as the basis for pricing, distribution, and promotion decisions. Value analysis aims to increase value, defined as function over cost, by improving function or reducing cost. Lack of product planning can lead to unsatisfied customers, quality issues, and loss of brand name. Process planning involves routing, scheduling, dispatching, and follow up based on product information, processes, capacity, orders, due dates, and resources. Economic batch quantity determines the optimal batch size to minimize average costs by balancing setup costs and inventory carrying costs.
The document discusses the application of statistical tools to enhance productivity and quality control in industries. It explains key concepts like process control, process capability indices, acceptance sampling plans, and their use in quality management. Statistical process control techniques like control charts are used to monitor processes and make data-driven decisions about product and process quality. Acceptance sampling balances protecting consumers from defects and encouraging quality production.
Statistical Process Control Training Online - Tonex TrainingBryan Len
It’s all about key concepts behind SPC or statistical process control, a statistically-based family of tools used to monitor, control, and improve processes.
All the attendees of Tonex statistical process control training will learn about the details of SPC, control charting, other procedures and tools to apply them in their projects.
Learn about :
Statistical process control (SPC) terminology & key principals
Learn how SPC integrates into the total quality system
Variation in manufacturing processes such as patterns
Learn about data collection, control charts
Techniques and tools to implement statistical process control
Recognize the fundamentals of process sampling strategy
Differentiate methods and tools to implement and assess SPC
Select and use recommended SPC practices
Course designed for:
Production Engineers, quality managers,
Operators, project managers,
Product process control, analysts,
Quality process, improvement associates
Other people engaged with SPC process
Course Topics :
What is Statistical process control (SPC)?
Introduction to Process Variation
Control Charts
7-QC Tools & 7-SUPP Tools
The Relationship Between Statistical Quality Control and Statistical Process Control
Statistical process control (SPC) Workshop
Want to learn more ?
Visit tonex.com for statistical process control training detail.
Statistical Process Control Training Online - Tonex Training
https://www.tonex.com/training-courses/statistical-process-control-training-spc-training/
This document discusses statistical process control (SPC), including its main concepts, tools, and applications. SPC uses statistical methods to monitor and control manufacturing processes. It involves taking random samples from a process and measuring their characteristics to create control charts, which are used to evaluate if the process is stable and meeting requirements. The key advantages of SPC include improved quality, reduced variability, and higher production efficiency. It can be applied in various industries to minimize defects and costs. Control charts are the most important SPC tool for differentiating common from special causes of variation and determining if a process is in control.
Abstract The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control is largely used in industries for monitoring the process parameters. It is a standard method for visualizing and controlling processes on the basis of measurements of randomly selected samples. The decisions about what needs to be improved, the possible methods to improve it, and the steps to take after getting results from the charts are all made by humans and based on wisdom and experience. The statistical process control described in this paper gives the details about the SPC, its advantages and limitation, applications and information regarding the control charts. Keywords: Statistical Process Control, Control chart, 5M’s, Capability Indices.
Design of an accuracy control system in ship building industryGopalakrishnan P
This document summarizes a study on implementing an Accuracy Control System (ACS) in an Indian shipyard to improve quality and reduce reworks. A pilot study was conducted on the plasma cutting machine. Data on dimensional variations was collected and control charts were developed. The analysis found the process to be out of control. Major causes of variation like fluctuations in power supply voltage and issues with input design data were identified through cause-and-effect analysis and testing. It was recommended to use advanced voltage regulators and calibrate the input design data to minimize variations and improve the plasma cutting accuracy. The ACS is expected to enhance quality and reduce production costs.
This document discusses optimization and tooling requirements for CNC machines. It covers optimization of machining costs and production rates based on cutting speed. It also discusses the role of computerized optimization systems at different levels. For tooling requirements, it describes various tool holding systems like modular quick change systems, tool holder spindle connections, cutting tool clamping systems, milling cutter drivers, side lock chucks, collet chucks, hydraulic chucks, and milling chucks. It also discusses tool magazines and automatic tool changers.
Statistical process control (SPC) techniques apply statistical methods to measure and analyze variation in manufacturing processes. SPC uses control charts to distinguish between common cause variation inherent to the process and special cause variation that can be assigned to a specific reason. Control charts monitor process data over time against statistical control limits. Process capability analysis compares process variation to product specifications to determine if the process is capable of meeting specifications. Key metrics like Cp, Cpk and Cpm indices quantify a process's capability relative to the specifications. For a process to have a valid capability analysis, it must meet assumptions of statistical control, normality, sufficient representative data, and independence of measurements.
Current State of Battery Technology:
Today, lithium-ion batteries remain the dominant technology for portable devices and electric vehicles, thanks to their high energy density, long lifespan, and improved safety features. However, there are still many challenges facing battery technology, including the need for increased energy density, longer lifespan, and sustainability.
Researchers are working on developing new materials and manufacturing techniques that could lead to significant improvements in battery performance. For example, solid-state batteries, which use a solid electrolyte instead of a liquid one, have the potential to offer higher energy density and improved safety. Other promising technologies include lithium-sulfur batteries and metal-air batteries.
Sustainability is also a major concern for battery technology. The mining and processing of materials used in batteries, such as lithium, cobalt, and nickel, can have significant environmental impacts, including water pollution, deforestation, and greenhouse gas emissions. Researchers are exploring ways to make batteries more sustainable, such as using recycled materials, developing more efficient manufacturing processes, and improving battery recycling techniques.
Current State of Battery Technology:
Today, lithium-ion batteries remain the dominant technology for portable devices and electric vehicles, thanks to their high energy density, long lifespan, and improved safety features. However, there are still many challenges facing battery technology, including the need for increased energy density, longer lifespan, and sustainability.
Researchers are working on developing new materials and manufacturing techniques that could lead to significant improvements in battery performance. For example, solid-state batteries, which use a solid electrolyte instead of a liquid one, have the potential to offer higher energy density and improved safety. Other promising technologies include lithium-sulfur batteries and metal-air batteries.
Sustainability is also a major concern for battery technology. The mining and processing of materials used in batteries, such as lithium, cobalt, and nickel, can have significant environmental impacts, including water pollution, deforestation, and greenhouse gas emissions. Researchers are exploring ways to make batteries more sustainable, such as using recycled materials, developing more efficient manufacturing processes, and improving battery recycling techniques.
Current State of Battery Technology:
Today, lithium-ion batteries remain the dominant technology for portable devices and electric vehicles, thanks to their high energy density, long lifespan, and improved safety features. However, there are still many challenges facing battery technology, including the need for increased energy density, longer lifespan, and sustainability.
Researchers are working on developing new materials and manufacturing techniques that could lead to significant improvements
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses various topics related to Total Quality Management including:
- Control Charts and Process Capability which are tools used to monitor manufacturing processes.
- Concepts of Six Sigma which is a methodology for eliminating defects through continuous process improvement.
- Quality Function Deployment (QFD) which transforms customer needs into engineering characteristics.
- Taguchi's Quality Loss Function which defines quality as losses to society from not meeting requirements or performance.
- Other topics on TPM (Total Productive Maintenance) and performance measures.
This document discusses adaptive control systems for machining. It defines adaptive control as a feedback system that automatically adjusts machining variables like cutting speed and feed rate based on actual process conditions. The three main functions of adaptive control are identification, decision, and modification. Adaptive control systems are classified as adaptive control with optimization, which uses a performance index, or adaptive control with constraints, which maximizes variables within set limits. Benefits include increased production and tool life, while limitations include lack of reliable tool sensors and standardized interfaces with CNC units.
Optimization of process parameter for maximizing Material removal rate in tur...IRJET Journal
This document describes an experiment that used the Taguchi method to optimize process parameters for maximizing material removal rate during CNC turning of EN8 steel. Three parameters (cutting speed, feed rate, and depth of cut) were tested at three levels each in an L9 orthogonal array experiment on a CNC lathe. Material removal rate was measured for each test and analyzed using signal-to-noise ratios to determine the optimal parameter settings for maximizing removal rate. The results identified the best cutting conditions and also provided an equation to predict material removal rate based on the process parameters.
This document discusses adaptive control systems for machining. It begins by defining adaptive control as a feedback system that automatically adjusts machining variables like cutting speed and feed rate based on actual process conditions. It describes the identification, decision, and modification functions of adaptive control systems. The document classifies adaptive control systems into those with optimization, those with constraints, and geometric adaptive control. It outlines the basic structures and examples of these different types of adaptive control systems and discusses their benefits and limitations.
The dimensional inspection is currently consolidated as an important tool in the
improvement of manufacturing processes. The demands of quality and the requirement of parts
with complex specifications propose new challenges, such as the incorporation of integration and
interoperability concepts in measurement operations. An important issue still unresolved in the
inspection process is the dynamic management of information that requires solutions that are at
the technological forefront and contextualized within the requirements of Industry 4.0. In this
perspective, the implementation of a closed-loop inspection supports the measurement
requirements for different manufacturing processes and enables the application for different
purposes. The feedback strategy proposed in this article proposes an alternative as a solution to
integrate the inspection results of a part within a digital manufacturing chain based on STEP-NC
(STandard for the Exchange of Product model data - Numerical Control). The mechanisms for
acquisition, processing, and analysis of information, and the control architecture that complement
the feedback strategy is exposed.
Parametric optimization of centrifugal magnetic force assisted abrasive flow ...eSAT Journals
Abstract: The traditional Taguchi method is widely used for optimizing the process parameters of a single response problem. Optimization of a single response results the non-optimum values for remaining. But, the performance of a machining process is often evaluated by several quality responses. Under such circumstances, multi-characteristics response optimization may be the solution to optimize multi-responses simultaneously. In the present work, a multi-characteristics response optimization model based on Taguchi and Utility concept is used to optimize process parameters, such as magnetic flux, rotational speed of CFG rod, shape of CFG rod, number of cycles, abrasive-to-iron ratio and abrasive particle size on multiple performance characteristics, namely, surface roughness (Ra) and material removal (MR) during polishing of hollow cylindrical brass work-pieces with Centrifugal-Magnetic Force Assisted Abrasive Flow Machining (CMA2FM) Process. Taguchi’s L27 orthogonal array (OA) is selected for experimental planning. The ANOVA and F-tests are used to analyze the results. It is found that all the input parameters significantly improve the Utility function comprising of two quality characteristics (MR and %ΔRa). Further, the confirmation tests are conducted and the results are found to be within the confidence interval.
Keywords: Abrasive Flow Machining (AFM), Centrifugal Force, Magnetic Force, CFG Rod, CMA2FM, Utility Concept,TaguchiMethod,MultiResponseOptimization
Application of Taguchi method in optimization of control parameters of grindi...Carnegie Mellon University
In this study, the control parameters for grinding process are optimized for reduction in cycle time while
maintaining quality standards in a bearing manufacturing company. The Taguchi method which is a powerful tool to
design optimization for quality is used to find the optimal control parameters. An orthogonal array, the signal-to-noise
(S/N) ratio and the analysis of variance (ANOVA) are used to study the performance characteristics of grinding
process of an outer ring of a taper roller bearing. Various control parameters consisting of feed parameters, position
parameters, speed parameters were optimized and also the main parameters that affect the grinding process can be
found out. Experimental results are provided to verify the effectiveness of this approach.
Process planning and cost estimation unit iis Kumaravel
This document discusses process planning activities including setting process parameters, work holding devices, inspection methods, and the economics of process planning. It covers topics such as calculating cutting speed, feed rate, and depth of cut. It also discusses work holding devices, jigs and fixtures, quality assurance methods, and total quality management principles. Process planning aims to optimize the manufacturing process to improve quality and reduce costs.
The document discusses intelligent maintenance strategies using Siemens' SIMATIC PCS 7 Maintenance Station software. It describes how the software provides a centralized interface for maintenance personnel to monitor asset status across an entire automated plant and process chain. Key information on components is standardized and organized hierarchically for easy access. This allows maintenance to move from a reactive to proactive approach of preventing issues before failures occur to improve productivity and availability.
IRJET- Experimental Study of Taguchi Vs GRA Parameters During CNC Boring in S...IRJET Journal
This document describes an experimental study comparing the Taguchi method and Grey Relational Analysis (GRA) parameters for CNC boring of steel plate SS-304. Nine experiments were designed using Taguchi's L9 orthogonal array to evaluate the effects of cutting speed, feed rate, and depth of cut on material removal rate (MRR) and surface roughness (Ra). The experiments were conducted on a CNC lathe and the responses measured. Taguchi analysis and GRA were used to analyze the data and determine the optimal cutting conditions for maximizing MRR and minimizing Ra. The Taguchi method predicted an optimal MRR of 29.233 mm3/min at 110 rpm, 0.08 mm/min feed, and 1.
This document discusses statistical process control (SPC), which uses statistical methods to monitor and control processes to improve quality. SPC aims to ensure processes operate efficiently and produce specification-conforming products with less waste. Key SPC tools include control charts, histograms, check sheets, and cause-and-effect diagrams. Control charts in particular plot process data over time to identify changes or variability. Understanding processes and eliminating assignable causes of variation are important for achieving process stability and meeting customer requirements through continuous improvement.
Process planning determines the most efficient way to manufacture a part from raw materials to the final product. It identifies the necessary manufacturing processes, sequence of operations, tools, and equipment based on the design, available resources, and quality requirements. The output is a process plan that specifies all required production steps and parameters to efficiently convert the raw materials into the finished part.
This document discusses computer aided process planning (CAPP). It outlines the key steps in process planning including drawing interpretation, material and process selection, selecting machines and tools, setting process parameters, quality assurance methods, cost estimating, documentation, and communicating the plan to the shop floor. CAPP aims to reduce errors and improve efficiency over manual planning. The benefits of CAPP include process rationalization, productivity gains, cost reductions, faster response to changes, and incorporating other applications. CAPP systems can be either retrieval-based, recalling plans for similar parts, or generative, creating new plans from scratch.
In order to study the WGS on an industrial scale at a low pressure, the modeling andsimulation of a WGS reactor operating at a pressure close to Patm and processing an industrial charge in the presence of a high temperature shift catalyst (Fe2O3/Cr2O3) were performed. The Profiles of the carbon monoxide conversion, temperature and pressure along the reactor were obtained. The effect of several operating parameters (inlet temperature, H2O/CO ratio) on the conversion of carbon monoxide along the reactor has been determined. The estimated catalytic mass to convert 60.5% of the carbon monoxide contained in the inlet is 170.76 t. The pressure drops in the reactor are not negligible and the maximum temperaturereached is without any harmful effect on the catalyst. The choice of an optimal inlet temperature and a high H2O/CO ratio improves the conversion of carbon monoxide.
As we are all aware,therecent discovery of the Higgs boson has revealed a highly massive particle, the value of which lies between 125and 126.5 GeV/c2.. According to the basic concepts of Quantum Mechanics, and in full compliance with the Uncertainty Principle and Yukawa intuitions, we were able to calculate the maximum limit of the Higgs boson‟s field of action. From the calculations show that the Higgs boson presents a range of action really very small, namely 9.8828∙10-16[cm], that is slightly smaller than 10-15[cm]. This value is justified by the considerable mass that the Higgs bosonacquires, in perfect agreement with the Uncertainty Principle.
The document presents the results of calculations of parameters of turbulent fluid flow in a pipe with a circular cross-section. Graphs and mathematical functions show how total pressure, velocity, vorticity, turbulent length, dissipation, viscosity, energy, and time change along the length of the pipe for different mass flow rates. A transition from laminar to turbulent flow occurs at around 2/5 the length of the pipe from the inlet. Parameters generally increase with mass flow rate and distance along the pipe, while turbulent time decreases. Functions are given to describe the variation of each parameter within different sections of the pipe.
This document summarizes a study that analyzes magnetohydrodynamic (MHD) flow of Newtonian and non-Newtonian nanofluids passing over a magnetic sphere. Nanofluids containing alumina or copper nanoparticles in water or oil bases were examined. Governing equations for continuity, momentum, and energy were derived and non-dimensionalized. The equations were transformed into similarity equations using a stream function and solved numerically. Results showed that increasing the magnetic parameter decreases velocity and temperature. Newtonian nanofluid velocity and temperature were higher than non-Newtonian. Copper-water nanofluid also had higher values than alumina-water.
Building materials used for the walls of simple houses in lower-middle-class areas in Indonesia are currently dominated by brick. This study proposes that soil-paper blocks coated with calcium silicate board may be a suitable alternative, with high embodied energy and density. The research aims to obtain an optimal wall thickness to provide protection against cooling and embodied energy in low income houses, as well as against the temperature conditions in these buildings in highland and lowland areas. Determination of wall thickness is performed by simulation of a 9 m2 building model with thick variables. Cooling calculations involved the use of Archipak software. Temperature measurements were carried out using a data logger on a sample of soil-paper blocks. The results indicate that the optimal wall thickness for protection against cooling and embodied energy is 8 cm. Soil-paper block has a lower density than brick. The use of calcium silicate boards does not affect the internal temperature of a low income house, but they can be used as protection against rainwater and as a substitute for wall plastering.
Knowledge management (KM) has become an effective way of managing organization‟s intellectual capital or, in other words, organization‟s full experience, skills and knowledge that is relevant for more effective performance in future. The paper proposes a knowledge management to achieve a competitive control of the machining systems. Then an application of Knowledge Management in engineering has been attempted to explain. The model can be used by the manager for the choosing of competitive orders.
1) The document evaluates the effect of varying mole ratios of reactants on the yield of ceftriaxone sodium synthesis. Ceftriaxone sodium was synthesized by reacting 7-ACT and MAEM, then with sodium salt.
2) Testing showed that increasing the mole ratio of MAEM increased the yield up to a ratio of 1:2, where the yield was 72.17% and purity was 99.32%. However, further increasing the ratio did not increase yield.
3) The highest mole ratio of 1:2 produced the highest yield while maintaining high purity. This suggests that a 1:2 mole ratio of reactants could be optimal for industrial scale ceftriaxone sodium production.
The challenges of river water quality management are so enormous, due to the unpredictive modes of contamination. Monitoring different sources of pollutant load contribution to the river basin is also quite tasking, resulting to laborious and expensive process which sometimes lead to analytical errors. This study deals with the assessment of the physico– chemicaland bacteriological parameters of water samples from River Amba during the period of August 2017 to January 2018 and developing regression models. Water quality Parameters such as Temperature, Turbidity (NTU), Suspended solids (mg/l), Colour, Total solids, Total dissolved solids, Electrical conductivity (μs/cm), pH, Hardness, Chemical Oxygen Demand, Dissolved Oxygen (DO), and Total Coliform were obtained and compared with water quality standards. The results of the water quality analysis of the study in comparison with drinking water quality standard issued byWorld Health Organization(WHO) and National Agency for Food and Drug Administration Control (NAFDAC) revealed that most of the water quality parameters were not adequate to pronounce the water potable. Hence adequate water treatment processes should be employed to make the water fit for consumption and other domestic uses. Statistical analysis was done, in which the systematic correlation and regressionanalysis showed a significant linear relationship between different pairs of water quality parameters. The highest correlation coefficient between different pairs of parameters obtained is (r = 0.999), resulting from the correlation between TS and SS. Multiple regression analysis was also carried out and regression equations were developed. It was observed that the parameters studied had a positive correlation with each other.
Time, in the globalized world, is one of the most important factors about the economy, science and health. Mankind has made various efforts to use time efficiently for many years. In these studies transport came to the fore and it has become indispensable. In the light of today's technological conditions, air transport is developing at an increasing rate. Every day many aircrafts are produced, which have different speeds, weight and volume, for serve to transport. Therefore to make structures for easy and safe transport need a stable soil. Particularly suitable areas for the airport grounds in cities today, not being physically proper that construction of the airport made on soil with low bearing capacity, swelling potential of an expansive soil, settlement of soil etc, areas. In this study, soil problems encountered in the construction of airports will be explained and a summary of studies on the solution of these problems will be presented.
People in a big city as Antananarivo, capital of Madagascar, have leads to take street foods for their daily nutritional needs. This food habits may be a risk for consumers due to contaminations from street environment and bad practices related to hygiene. This study aimed to examine the quality and safety of street vended foods in Antananarivo, on January 2016 to December 2017.Six hundred and sixty two samples including 126samples of melting salads, 70 beef skewers, 54 chicken skewers, and typical Malagasy foods as : mofoanana (67 samples), mofogasy (64 samples), ramanonaka (64), makasaoka (66), mofoakondro (62) and kobandravina(89);were randomly collected from the streetvendors in Antananarivo marketsto evaluate their bacteriological quality.International Methods (ISO) was adopted for to find the load of Total Aerobic Bacteria andEnterobateriaceae,Escherichia coli and to search pathogen bacteria as Salmonella, Campylobacter jejuni, Escherichia coli O157H7 and Bacillus cereus in these foods.The results revealed that the mean values ofthe Total Aerobic Bacteria count was 0.1x106- 4.8x106cfu/g. Enterobacteriaceaecount range from 0.4x102 to 1.9x102cfu/g. Escherichia coli count range from 0.04x102cfu/g. to 0.19 x102cfu/g.Salmonellawas only present in melting salads, beef skewers and chicken skewers samples. Bacillus cereus count range from 0,1x102 to 1,5x102cfu/g. Campylobacter jejuniwas only present in samples of ramanonaka and kobandravina. Two strains of presumptive Eschercichia coli O157 H7 (βglucuronidase -) were isolated. PCR method was used to confirm the identity of these two isolates. A high contamination above 106 cfu/g food and the presence of potential pathogens bacteria could be hazardous. Systematic inspections and training of food vendors on food hygiene and application of hazard analysis critical control point (HACCP) has been recognised as measures to guarantee improvement of the quality of street foods.
In order to clean up soils contaminated with hydrocarbons, the bioremediation activity of Pseudomonas putida was studied. Pseudomonas putida is a bacterium that can withstand the harshest environmental conditions. It is able to metabolize a wide range of petroleum hydrocarbons which is used as a source of carbon and energy. Given the potential of this microorganism, an experiment wasconducted on this strain.
For the isolation of this microorganism, a sample ofsoil from the Vakinankaratra region in the urban commune of Antsirabe II, Madagascar was microbiologically analysed. The bacterial identification was based on a study of the morphological, physicochemical and sequential analysis of the 16S rDNA gene.
Scored tablets provide dose flexibility, ease of swallowing and cost savings. However, some problems with scored tablets can be confronted like difficulty of breaking, unequally breaking and loss of mass upon breaking. This paper investigates the effect of score lines on the density distribution using continuum modelling. In keeping with previous work in the pharmaceutical field, a modified Drucker Prager Cap model is described briefly and used in the simulations. Coulomb friction is included between powder and tools. The microcrystalline cellulose (MCC) Vivapur® 102 was used to identify the model parameters using experimental tests with instrumented die, shear cell and diametrical crushing. The obtained results indicate that simulations may be useful not only to determine density distributions within tablets, but also may provide indications about performance of score lines.
The document discusses using solar chimneys to reduce heating loads in cold climates. It summarizes previous research on solar chimneys and their impact on ventilation. The author models a school building in a cold climate with and without a solar chimney using energy simulation software. The results show that with a solar chimney, the indoor temperature reached 22°C without mechanical heating, within the comfort range. By applying passive solar techniques like solar chimneys, architectural projects can save energy without large costs.
The control of motor rotation speed by the change of resistor resistance value in armature circuit is called ‘resistor control”. For the regulation of resistance value R0, included in armature winding circuit, we can use various technical solutions. The most used solution is the discrete variation of armature added resistance value by shunting its parts with contactors contacts. Nowadays, the change of resistor resistance in armature circuit can be realized by shunting with a given porosity γ of resistor R0 trough electronic keys. In this paper, we study the design of control system represented on figure 1.
A poultry yield prediction model have then designed using a data mining and machine learning technique called Classification and Regression Tree (CART) algorithm. The developed model has been optimized and pruned using the Reduced Error Pruning (REP) algorithm to improve prediction accuracy. An algorithm to make the prediction model flexible and capable of making predictions irrespective of poultry size or population has been proposed. The model can be used by poultry farmers to predict yield even before a breeding season. The model can also be used to help farmers take decisions to ensure desirable yield at the end of the breeding season.
Today, Web site design is used to make sites useful to users, with accessible functions, resources and information. Therefore, that design involves use of methodologies that allow an adequate structuring of them resources and organization, permitting users to access them quickly, easily and intuitively. This research consisted of a usability study oriented to website structure designers using a methodology based on concepts of ontology design. This study includes a planning to evaluate the design and the structure of website in aspects such as: ease of use, efficient access to information and performance on the tasks focused to total satisfaction of end user. Heuristic tests were used as diagnostic tools to evaluate usability of website design structures; these were supported by a heuristic evaluation guide and in the Sirius methodology[3]. The results obtained from them, allowed us to detect opportunities for improvement and optimization in website design, and in refining the Web interface oriented to end users.
Acceptance of a website is determined by various factors, one of the most important is the organization that allows users to access to functions, resources and information that it contains. This work consisted of a study of comparative usability between a website designed using principles of linguistics and design of ontologies and other using a strategy of a commercial product. A plan was designed and applied to evaluate the following aspects of website: ease of use, efficiency to access its information, efficacy to perform tasks and user satisfaction. Heuristic and user tests were used as diagnostic tools in usability evaluations, and an observation guide was made by an external evaluator as a complement to previous tests. The results clearly shown that is better use the proposed website design methodology. This allows to create site more structured, functional and with greater ease of access to resources that it contain.
This document summarizes research on modifying an epoxy resin with epoxidized sunflower oil (ESO) and assessing the impact on material properties. Two processes for incorporating ESO into the epoxy resin were tested: a one-stage and two-stage process. Results showed the two-stage process produced materials with greater impact strength, fracture toughness, and decomposition temperature compared to the one-stage process. Specifically, the polymer composite achieved the best properties when containing 5% ESO using the two-stage process, improving the toughness and strength of the material.
A Network Intrusion Detection System (NIDS) monitors a network for malicious activities or policy violations [1]. The Kernel-based Virtual Machine (KVM) is a full virtualization solution for Linux on x86 hardware virtualization extensions [2]. We design and implement a back-propagation network intrusion detection system in KVM. Compared to traditional Back Propagation (BP) NIDS, the Particle Swarm Optimization (PSO) algorithm is applied to improve efficiency. The results show an improved system in terms of recall and precision along with missing detection rates.
In this paper, we consider the scaling invariant spaces for fractional Navier-Stokes in the
Lebesgue spaces ( ) p n L R and homogeneous Besov spaces
, ( ) s n
p q B R respectively.
More from International Journal of Innovation Engineering and Science Research (20)
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
New techniques for characterising damage in rock slopes.pdf
Cutting Process Control
1. International Journal of Innovation Engineering and Science Research
www.ijiesr.com
Volume 2 Issue 6 November-December 2018 15|P a g e
ABSTRACT
Cutting Process Control
Daschievici Luiza, Ghelase Daniela
“Dunarea de Jos” University of Galati
Galati, Romania
ABSTRACT
Adaptive-optimal control involves re-identification of the machining process and the model obtained is used to
calculate the optimal process parameters.
Optimal control characterizes the addiction of the technical and economic indicators to process parameters.
Characteristic for performance technical indicators is that their dependence to parameter values of process has a
limitative, what leads to one of the following conclusions, appropriately or inappropriately, and therefore can
serve as restrictions in optimization problem.
Economic indicators have a continuous dependence of process parameters and therefore they are used as
objective functions.
Keywords— manufacturing system, adaptive-optimal control, cutting process
I. Introduction
In the course of cutting process the monitored sizes are the following:
Cutting regime parameters; Processing error ; Position of the tool subassembly at a time (CNC machine); The
actual size of the processed surface; Surface roughness; Static and dynamic stability of the cutting process;
Cutting force; Temperature of the cutting area; Cutting power.
For monitoring parameters or the performance indicators of the manufacturing process the following
techniques are used: For position of the cutting-tool subassembly is used the transducers system of CNC
machine; For measurement the processing error are used dimensional inspection tools; For the cutting force are
used transducers attached at the subassembly of the tool or piece; For temperature measurement are used the
natural thermocouple (tool-piece) or thermal radiation of the machined surface.
Monitoring is at large intervals. In cases where monitoring is done online, the interpretation of monitoring results
is the same as for offline monitoring. Even when both the measurement and interpretation of results is on-line
(continuous), the result is used to maintain constant parameters of the process and not to recalculate its optimum
value.
II. Characteristics of Conventional Control
It is characterized in that the model of the process remains unchanged and the result of the
management represents necessary values of the process parameters. Values of the process parameters are set
before and held constant during the development of process. For example: is constant: cutting regime, geometry
of the cutting tool, processing material and features of the technological system.
Sometimes instead of the parameters: v, s, t of the regime of cutting are maintained constant values of
cutting speed, force and feed rate of cutting and another time are maintained constant the speed, power and
depth of cutting. So, from the process parameters are selected an independent set of parameters whose values
are held constant during the process. Selected values of process parameters (which are held constant) is
calculated on the basis of general mathematical models that are used in the management of any process. Eg
speed cutting is determined depending on the tool durability, feed rate of cutting is determined depending the
roughness of required surface, depth of cutting is determined depending on the size of the perform and achieved
size, the geometry of the tool is determined depending on the geometrical features of processed surface.
Dimensional control in conventional regime is characterized by:
- Off-line measurement of surface dimensions processed;
2. Daschievici Luiza “International Journal of Innovation Engineering and Science Research”
Volume 2 Issue 6 November-December 2018 16|P a g e
- The change the position of the tool relative to the materials just after the surface has been generated
completely;
- Even when measuring of dimension is done online, change of the position of the tool relative to the material is
made after the surface has been generated completely.
III. Optimal Control of Cutting Process
Basically, it is found that processing system evolves in time and space, making as a model, valid at a time and in
a certain place, not to be suitable at another time and in another place. Other disadvantages would be:
- Does not take into account the characteristics of each specimen processed;
- Does not take into account errors of the technological system;
- There is no forecast on the evolution of the system in time and space.
Optimal control in conventional regime is characterized in that:
- Determining the optimal parameter values is based on general mathematical models applied in all particular
cases encountered;
- The optimization criterion is the cost or productivity;
- The restrictions from the problem of the optimization are given by the limitations imposed technical indicators of
performance of the process, such as speed minimum and maximum of the principal axle of the machine-tool, the
minimum and maximum value of the feed rate, the maximum power of the motor drive, the maximum roughness
of the surface ;
- The geometry of the tool is determined on the basis of general qualitative indications;
- Overcoming restrictions initially considered is ascertained after what the processing is occurred;
- It is not done the monitoring of the performance indicators of the process, but just only of the inputs in the
process.
The objective of the optimal control is not getting imposed values of performance indicators, but getting the most
favorable values of them. The parameter values corresponding to the most favorable performance indicator
values are called optimal values.
Adaptive-optimal control involves re-identification of the machining process and the model obtained is used to
calculate the optimal process parameters.
Optimal control characterizes the addiction of the technical and economic indicators to process parameters.
Characteristic for performance technical indicators is that their dependence to parameter values of process has a
limitative, what leads to one of the following conclusions, appropriately or inappropriately, and therefore can
serve as restrictions in optimization problem.
Economic indicators have a continuous dependence of process parameters and therefore they are used as
objective functions.
IV. Strategies for Optimal Management of Cutting Process
To achieve various optimization criteria and, sometimes, to achieve concomitant such criteria it use different
strategies. So,
A) To obtain a high productivity are used the following strategies:
a) - feed rate strategy adapted to the cutting path - in which the processing time is reduced by a cutting operation
along the cutting direction with minimum force and a reduced error and maximizing the speed of advance;
b) selection of process parameters (feed rate, cutting depth, cutting speed) can have significant influence on
system performance on productivity;
c) Another work [1] analyzes productivity and problem of the preventive control maintenance for a manufacturing
system on a machine-tool that processes multiple tracks simultaneously. The objective of such a study is to
determine productivity and ensure a continuous supply of the machine-tool so that the total costs to be minimal.
In the work [5] in which the author refers to the fact that adjusting the cutting forces lead to significant economic
benefits by increasing productivity and improving quality operations. This is a challenge since the variation of the
forces vary significantly in cutting normal conditions. The controllers can not guarantee system performance and
stability since the force varies and substantial research effort was conducted to develop an adaptive controller.
Consequently, in industry this technology it is applied reduced
B): To get a surface with a good quality it is used:
a) - the surface control strategy of the in which processing errors are minimized by using an offset control of the
surface, based on a prediction of the processing error ;
b) - [1] presents an optimization model in terms of selection parameters of the machine tools that lead to a certain
amount of material removed for a certain quality of surface and a certain durability of the cutting tools. The
interpretation is based on the models presented by [2] to predict the surface roughness, cutting the maximum
3. Daschievici Luiza “International Journal of Innovation Engineering and Science Research”
Volume 2 Issue 6 November-December 2018 17|P a g e
temperature, distribution of the residual stress on the processing surface and using of the parameters of the
machine-tool and of the cutting conditions. The numerical results demonstrate that the procedure can be
considered as an advantage used to specify variations of the machine-tool for developing of the data base of the
variables of the machine. This unified model is proposed for to represent the strategy for a adaptive control
system of the machine-tool process. In conclusion, this computerized procedure is presented to illustrate the
potential of planning of the car by determining the optimum parameters that maximize material removal rate to
achieve the desired parameters of the surface. The procedure takes into account the limits and plastic
deformations of the tool. An interesting result of this study is that, the increasing of the compressive residual
stress limits desired reduce the optimum value of the cutting speed and obviously the rate of the reduction of
material removed. This, because the cutting speed generates fractional heat and residual gradient produces
residual stresses in the surface of the part [6]. This work describes a unified optimization model that takes into
account the integrity of the surface and stability of the tool and it was based on physical relations of the
processes of the machine. Model and simulated results can be applied in the development of the decision basic
data for adaptive control of the machining process.
c) Another approach in terms of optimization is done in [1]. In this paper it is studied an algorithm based on
optimizing stimulated and Hooke-Jeeves search model is developed to optimize cutting operations in multiple
passes. Machine parameters are determined to optimize conditions and minimization of cost per unit of product,
taking into account certain limitations. The results of experiments demonstrate that the optimization algorithm for
limitations nonlinear, called SA / PS is effective for solving complex optimization problems.
The algorithm SA / PS can be inserted into a CAPP system for generating optimum parameters of the machine-
tool. In this paper it was presented a cutting operation with multiple passes and a hybrid procedure based on
simulation algorithm of the normalization and SA / PS for search was applied to the problem of optimizing
machine tool. The algorithm SA / PS can achieve a non-linear solution in a spatial solution extremely rare
compared with the notion of time. The effectiveness of this algorithm was demonstrated by a base experiment.
C) To achieve two goals simultaneously:
a) proposed strategies to improve productivity when conditions generated by cutting force and certain
dimensional limitations. This strategy proposes a new processing aid, namely drawing a map of the maximum
feed rates. In this map, the maximum permitted feed rate, depending on the required limitations, every checkpoint
along the direction of processing are determined using a generating model of the surface. With this help, a part of
the program is able to select a optimum cutting direction and a feed rate to reduce processing time. The strategy
consists of three modules: 1 - selection of the control point; 2 - map of maximum feed speed; 3 - feed rate. So:
- Checkpoints are selected on the control surfaces. Number of points is selected depending of the imposed
accuracy and density can vary. For each geometric point, cutting force and error of the processing are evaluated
in sub-modules;
- The main goal is getting of the feed rate map for control of surfaces;
- The map reflects important information for choosing and selecting cutting speed. So, the processing time can be
significantly reduced when surface processing, cutting forces and dimensional accuracy can be controlled using
the proposed strategy.
b) A system type map is proposed in [3] too. This article proposes an algorithm for designing a control system of
maps which consists of several separate maps, each of them is used to monitor the stage of a critical process
from processing of a product. The algorithm consists of all maps integrated into an integrated system. Thus, the
performance characteristics of a system as a whole can be significantly improved and product quality can be
increased. One such improvement is achieved without additional costs or too much effort. Furthermore, operators
can easily analyze, depending on the maps that are displayed on the display, the current situation at some point.
D) To maximize profits:
a) The strategy is a optimization function from point of view of geometrical quality, of productivity of system and
cost. Geometric quality of the product is modeled as a set of functions based on the general propagation of the
model variation, the productivity being a function of the processing and the cost, represents the sum of cost of
production and the quality cost. Solving optimization problem leads to the determination of the best parameters of
price and tolerance. The researchers followed a specific target for optimal demonstration character of the
processing. It may list a number of ways of determining:
- By monitoring the durability of cutting and cutting tool;
- By monitoring of the cutting process and the durability of tool cutting;
The determination of an optimal algorithm for evaluation of process parameters;
- By looking for certain types of cutting tools;
- By monitoring tool wear;
- The determination of the parameters of the machine-tool.
4. Daschievici Luiza “International Journal of Innovation Engineering and Science Research”
Volume 2 Issue 6 November-December 2018 18|P a g e
The differences come from the definition of optimization criteria, character online and off-line of the process, of
the adaptive character or not of the algorithm.
Regarding optimization of cutting in multiple passes, which imposed certain restrictions, [5] considers that, the
operation in one pass is not always the most economical and productive in terms of restrictions and
demonstrates that cutting process in two or three passes may be cheaper or shorter in terms of time spent.
Optimization is determined by the combination of the geometric and linear programming. The operation in one
pass is optimum if it is limited only to reach the highest speed possible - which is not a general requirement. Most
common are the situations where restrictions are related to cutting force, a some roughness of surface, a
durability of cutting tool and cutting speed.
In the latter case, two passes or three passes may be more economical and can be performed in a shorter time
than the processing operation.
c) In the work [6] is studied the optimal control of material removal rate to achieve a optimal rate of cutting of a
cutting tool so, to be able to achieve maximum profit. This paper focuses not only on the rate of material what is
removed in manufacturing process, integrated in mathematical formulas as objective functions, but it and
implements the calculation of the variables to control the cutting rate.
E). Ensuring of a longer durability of the cutting tool
a) In [7] is presented a statistical model that uses the idea from the rehabilitation theory and statistical control of
quality. The model is based on Taylor's equation so that information can be used relative to the existing relations
between the cutting data and cutting tool durability.
b) Another study which is based on the cutting parameters optimization is studied for increase of durability tool.
Improving sustainability is essential in order to reduce production costs as much as possible. The tools have a
limited durability, which is why we need to find ways to increase the sustainability and critical process parameters
cutting. Cutting tools deteriorates either through gradually wear of the cutting edge or due to plastic deformation
at which they are subject. Usually, criteria of establishing sustainability are established as predetermined values.
Obviously, any development will lead to an increase in durability.
The paper [4] shows the influence of the variation of speed on the durability when cutting. The paper [2] propose
a new equation for durability.
d) Another study is achieved by [5] which shows that in the series production it uses the same cutting tool for
cutting of the different parts having different cutting parameters. In such a case, the optim moments are those
when tools need to be changed when it reaches the minimum production cost and a maximum rate of
productivity. This paper analyzes the status of the processed surface and material waste from cutting tool using
an algorithm of processing / analyzing and micro-optical images.
F) Minimize production costs
Such a strategy is presented in [5] which proposed a model of cutting in multiple passes. Using Lagrange
method, cutting optimal time for each cutting tool is stored and the cost due Lagrange multiplier is multiplied.
V. Conclusions
Analyzing the current solutions of optimal or adaptive control of machining processes, the following conclusions
are:
- Optimization of cutting is done by calculating the optimal parameters in its design stage of the technological
process, following that they will be set and maintained at a constant value throughout the process of processing.
- Changing the parameter values is achieved to long periods and does not lead to the desired performance
indicators;
- When monitoring is done online, the information obtained is used to delay and not for process optimization but
keeping constant or limiting of values of some parameters.
- The mathematical models used to calculate the optimal parameters are generated and not resulted from the
identification of real machining process or of the technological system used to achieve processing;
- The optimization algorithm of the technological process does not take into account the market success of the
product and neither actual characteristics of each processed piece.
- Adaptive management system based on identification of the technological system-manufacturing process is not
done currently at the available technological systems
- It finds that through the current control techniques of management process, real values of parameters are not
optimal, which shows that there are enough reserves to increase performance indicators of the process through
better management and without significant additional costs.
5. Daschievici Luiza “International Journal of Innovation Engineering and Science Research”
Volume 2 Issue 6 November-December 2018 19|P a g e
References
1) S. Hemmati, M. Rabbani, “Make-to-order/make-to-stock partitioning decision using the analytic network
process”, Int J Adv Manuf Technol, DOI 10.1007/s00170-009-2312-4, 2009.
2) A. Garcia-Crespo, B. Ruiz-Mezcua, J.L. Lopez-Cuadrado, I. Gonzales-Carrasco, “A review of conventional
and knowledge based systems for machining price quotation”, J Intel Manuf, DOI 10.1007/s10845-009-
0335-1, 2009.
3) Niazi, A., Dai, J., Balabani, S., & Seneviratne, L. “Product cost estimation: Technique classification and
methodology review”, Journal of Manufacturing Science Engineering,128(2), 563–575, 2006.
4) E. Shehab and H. Abdalla, “An Intelligent Knowledge-Based System for Product Cost Modelling”, Int J Adv
Manuf Technol, 2002, 19:49–65.
5) Fehmi H’mida, Patrick Martin, Francois Vernadat, “Cost estimation in mechanical production: The Cost
Entity approach applied to integrated product engineering”, Int. J. Production Economics, 2006, 103: 17–
35.
6) Z. Bouaziz, J. B. Younes and A. Zghal. “A Fast and Reliable Tool for Estimates for Plastic Blowing
Moulds”, Int J Adv Manuf Technol, 2002, 20:545–550.
7) Z. Bouaziz, J. Ben Younes, A. Zghal, “Cost estimation system of dies manufacturing based on the
complex machining features”, Int J Adv Manuf Technol, 2006, 28: 262–271, DOI 10.1007/s00170-004-
2179-3.