The document discusses the application of statistical tolerance limits in process qualification. Tolerance limits define an interval that covers a proportion of a population with a given confidence level. Using tolerance limits provides a statement of confidence and reliability about a process while accounting for uncertainty due to sample size. Three examples are provided to demonstrate calculating tolerance limits and determining minimum sample sizes needed to validate processes.
PPT ON TAGUCHI METHODS / TECHNIQUES - KAUSTUBH BABREKARKaustubh Babrekar
A brief brief to Taguchi Methods / Techniques; Loss function; Orthogonal arrays; Fractional Factprials and various case studies and examples related to each topic covered in detail.
PPT presented by Kaustubh Babrekar under the guidance of Prof. Dr. N. G. Phafat. MGM JNEC Aurangabad.
- Notations, assumptions, and rule of thumb;
- Control limits;
- Phase I and Phase II;
- Estimating process capability;
- Example of application;
- Designing control charts;
- Charts based on standard values;
- Patterns interpretation;
- The operating-characteristic function;
- Average run length.
This document provides an overview of statistical process control and related quality control techniques. It discusses descriptive statistics, statistical process control methods including the seven basic quality tools, and acceptance sampling. Statistical process control is identified as the most important statistical quality control tool because it can identify changes or variations in quality during the production process using methods like control charts. Control charts, check sheets, Pareto charts, flow charts and other tools are explained as part of statistical process control. Acceptance sampling procedures and how they manage producer and consumer risks 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 ppt @ bec domsBabasab Patil
The document discusses key concepts in statistical process control including control charts for variables and attributes, process capability, acceptance sampling, and operating characteristic curves. The learning objectives are to identify key terms, describe the role of statistical quality control in measuring process performance using statistics, and explain different types of statistical process control including process control, acceptance sampling, and their use in controlling processes and inspecting samples.
Statistical quality control (SQC) uses statistical tools to monitor and improve production processes. Walter Shewhart pioneered control charts in the 1920s to distinguish normal variation from problems. W. Edwards Deming helped spread SQC in the US and Japan. Descriptive statistics describe quality characteristics, while control charts monitor processes over time. Variables charts like X-bar and R charts monitor measurable attributes, while P and C charts monitor discrete attributes like defects. Process capability evaluates a process's ability to meet specifications by comparing variability to tolerance limits. Key metrics include Cp, Cpk, and process centering.
This document provides an overview of statistical process control (SPC). It discusses key SPC concepts including:
1) SPC focuses on detecting and eliminating abnormal variations (assignable causes) to achieve consistent quality.
2) SPC requires knowledge of basic statistics, variation, histograms, process capability, and control charts. Control charts are used to monitor a process and detect when assignable causes result in variations outside the natural limits.
3) A histogram provides a visual representation of a process and can indicate if a process is capable and centered on the target, or if assignable causes are present.
PPT ON TAGUCHI METHODS / TECHNIQUES - KAUSTUBH BABREKARKaustubh Babrekar
A brief brief to Taguchi Methods / Techniques; Loss function; Orthogonal arrays; Fractional Factprials and various case studies and examples related to each topic covered in detail.
PPT presented by Kaustubh Babrekar under the guidance of Prof. Dr. N. G. Phafat. MGM JNEC Aurangabad.
- Notations, assumptions, and rule of thumb;
- Control limits;
- Phase I and Phase II;
- Estimating process capability;
- Example of application;
- Designing control charts;
- Charts based on standard values;
- Patterns interpretation;
- The operating-characteristic function;
- Average run length.
This document provides an overview of statistical process control and related quality control techniques. It discusses descriptive statistics, statistical process control methods including the seven basic quality tools, and acceptance sampling. Statistical process control is identified as the most important statistical quality control tool because it can identify changes or variations in quality during the production process using methods like control charts. Control charts, check sheets, Pareto charts, flow charts and other tools are explained as part of statistical process control. Acceptance sampling procedures and how they manage producer and consumer risks 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 ppt @ bec domsBabasab Patil
The document discusses key concepts in statistical process control including control charts for variables and attributes, process capability, acceptance sampling, and operating characteristic curves. The learning objectives are to identify key terms, describe the role of statistical quality control in measuring process performance using statistics, and explain different types of statistical process control including process control, acceptance sampling, and their use in controlling processes and inspecting samples.
Statistical quality control (SQC) uses statistical tools to monitor and improve production processes. Walter Shewhart pioneered control charts in the 1920s to distinguish normal variation from problems. W. Edwards Deming helped spread SQC in the US and Japan. Descriptive statistics describe quality characteristics, while control charts monitor processes over time. Variables charts like X-bar and R charts monitor measurable attributes, while P and C charts monitor discrete attributes like defects. Process capability evaluates a process's ability to meet specifications by comparing variability to tolerance limits. Key metrics include Cp, Cpk, and process centering.
This document provides an overview of statistical process control (SPC). It discusses key SPC concepts including:
1) SPC focuses on detecting and eliminating abnormal variations (assignable causes) to achieve consistent quality.
2) SPC requires knowledge of basic statistics, variation, histograms, process capability, and control charts. Control charts are used to monitor a process and detect when assignable causes result in variations outside the natural limits.
3) A histogram provides a visual representation of a process and can indicate if a process is capable and centered on the target, or if assignable causes are present.
After this PPT you'll get idea about 'What is quality control? Why is Quality control Important? Types of Quality control, What is quality inspection? Tools of Quality inspection and Quality inspection loop.'
Statistical process control (SPC) involves using statistical methods to monitor and control processes to ensure they produce conforming products. Variation exists in all processes, and SPC helps determine when variation is normal versus requiring correction. Key SPC tools include control charts, which graph process data over time to identify special causes of variation needing addressing. Process capability analysis also examines whether a process can meet specifications under natural variation. Together these tools help processes run at full potential with minimal waste.
This document discusses cost of quality and provides definitions, categories, and models of quality costs. It defines cost of quality as the costs incurred to prevent, detect, and fix defects. Quality costs are divided into conformance costs (prevention and appraisal) and non-conformance costs (internal and external failure). Prevention costs aim to avoid defects, appraisal costs detect defects, and failure costs result from defects. The document also outlines the history of cost of quality analysis, gives examples to illustrate the categories, and presents a case study of a company's quality costs over four years that demonstrates how prevention costs can reduce total quality costs.
Statistical process control (SPC) uses statistical methods to monitor and control processes. The goal of SPC is to reduce process variability through the prevention of defects. SPC analyzes process data over time to distinguish common and special causes of variation. Key aspects of SPC include understanding variable and attribute data, using control charts to monitor processes, and calculating process capability indices to quantify a process's ability to meet specifications.
Control charts are used to monitor process variables over time in various industries and organizations. They tell us when a process is out of control by showing data points outside the control limits. When this occurs, those closest to the process must find and eliminate the special cause of variation to prevent it from happening again. Control charts have basic components like a centerline and upper and lower control limits. They are constructed by selecting a process, collecting data, calculating statistics and control limits, and plotting the results over time. Control charts come in two types - variables charts for continuous measurements and attributes charts for counting items. Common and special causes can lead to variations monitored by these charts.
This presentation provides an overview of control charts, including what they are, their purposes and advantages, different types of control charts, and how to construct and interpret them. Control charts graphically display process data over time to determine whether a manufacturing or business process is in a state of statistical control. The presentation discusses variable and attribute control charts, and specific charts like X-bar and R-bar charts. It provides examples of how to calculate control limits and plot data on a chart, and how to interpret results to determine if a process is capable or needs improvement. A case study example analyzing wait time data from a hotel management company is also reviewed.
This document provides an introduction to statistical process control (SPC). It defines SPC as a strategy that uses statistical techniques to evaluate processes, identify variability, and find opportunities for improvement. The goal of SPC is to make high-quality products the first time by reducing variability, rather than reworking defective products. It focuses on monitoring process behavior rather than just final product quality. SPC distinguishes between common cause variability that is always present and special cause variability that can be addressed to improve the process. It emphasizes identifying and addressing special causes first before adjusting process means. Control charts are used to monitor processes and determine if they are in control or need adjustment.
This document outlines the course content for a statistical quality control course. It covers 5 chapters: 1) Introduction to statistical quality control, 2) Methods of statistical process control and capability analysis, 3) Other statistical process monitoring and control techniques, 4) Acceptance sampling, and 5) Reliability and life testing. Key topics include the history of statistical quality control, uses of SQC, quality improvement, modeling process quality, control charts, process capability indices, cumulative sum control charts, and acceptance sampling concepts. The document provides an overview of the concepts, methods, and techniques that will be covered in the statistical quality control course.
This document provides an introduction to statistical process control (SPC). It discusses the limitations of inspection and why SPC is better. It explains that SPC allows monitoring of processes to detect changes before defective products are produced. Various control chart templates are shown and key SPC concepts are defined, including sources of variation, the central limit theorem, and using average and range to monitor process behavior over time. Examples are provided to illustrate variability, distributions, and how control charts can be used.
Statistical Process Control & Operations Managementajithsrc
This document discusses statistical process control and quality management techniques. It defines key terms like chance causes, assignable causes, control charts, attributes and variables. It also describes different types of control charts like Pareto charts, fishbone diagrams, mean charts, range charts, p-charts and c-charts. The document provides examples of how to construct and interpret these different control charts. It also discusses acceptance sampling and how to construct an operating characteristic curve.
There are six measures that can define a process's capability: Z values, sigma, percentage out of specification, defects per million opportunities (DPMO), defects per number of opportunities, and Cpk. These measures represent both short-term and long-term capability. Short-term refers to random variation, while long-term also includes non-random sources of drift. To convert between them, a 1.5 sigma shift is typically used. Calculating percentage out of specification first is generally recommended to determine process capability without distributional assumptions.
FMEA is a systematic method for evaluating potential failures in a design, manufacturing or assembly process. It involves analyzing possible failures, identifying their causes and effects, and prioritizing issues based on severity, occurrence, and detection. The process results in a risk priority number to determine which failures should be addressed first. FMEA is widely used in industries like automotive, aerospace, healthcare to prevent failures and improve quality and safety.
After World War II, Japan adopted quality as an economic strategy and selected seven statistical tools to analyze quality problems and drive continuous improvement. The seven tools - Pareto charts, cause-and-effect diagrams, histograms, control charts, scatter plots, check sheets, and flow charts - can identify up to 95% of issues. Each tool has a specific purpose, such as prioritizing problems with Pareto charts or identifying relationships between variables with scatter plots. Using these tools, Japanese companies were able to dramatically improve quality and economic performance.
The cause of inefficiency and poor quality is the system, not the employees and it is management’s responsibility to correct the system in order to achieve desired results. The quality of a product is the driving force for any organisation.
This document provides an introduction to statistical process control (SPC) and control charts. It discusses the basic concepts of common cause and special cause variation and how control charts can distinguish between them. The objectives of control charts are to detect special causes of variation so corrective actions can be taken to reduce nonconforming units and keep the process stable and predictable. The document reviews the anatomy of control charts and rules for interpreting when a process is in or out of statistical control. Finally, it outlines the different types of control charts for variable and attribute data.
Quality Control Tools And Techniques PowerPoint Presentation Slides SlideTeam
Find issues related to the quality using Quality Control Tools And Techniques PowerPoint Presentation Slides. Solve most quality related issues with the help of professionally designed quality control tools and techniques PPT templates. These templates showcase necessary tools required to control the quality within an organization or project. These templates have the relevant content to detect and control the loopholes. This deck comprises of quality tools such as cause and effect diagram, check sheet, control charts, histogram, pareto chart, scatter diagram, and more. Implement these tools to enhance the quality of the product and services or the quality of an organization. These tools and techniques are apt for quality planning and quality assurance. Prioritize objectives and discover problem areas by adding quality control charts and graphs. These PPT templates are completely customizable. Edit the color, text, icon and font size as per your requirement. Download quality control tools or also known as Kaoru Ishikawa’s quality tools presentation slideshow to achieve the desired goals and outcomes. Develop a feel for difficulties ahead with our Quality Control Tools And Techniques Powerpoint Presentation Slides. They help anticipate hassles.
Statistical quality control presentationSuchitra Sahu
Here are the key steps to construct a C-chart for this example:
1. Count the number of defects (misspelled words) in each sample (newspaper edition)
2. Calculate the average number of defects per unit (C=average number of defects)
3. Calculate the upper and lower control limits
4. Plot the number of defects for each sample versus the sample number
5. Analyze for points outside the control limits to identify periods where the process is out of control
Does this help explain the basic approach to constructing a C-chart? Let me know if you need any clarification or have additional questions.
Taguchi Method is a new engineering design optimisation methodology that improves the quality of existing products and processes and simultaneously reduces their costs very rapidly, with minimum engineering resources and development man-hours
This presentation from IVT's 2nd Annual Validation Week Canada covers the 2011 FDA Process validation and the subsequent statistical processes. Statistics in process validation is introduced as well as the integration with six sigma and solutions to common mistakes.
Is 919.1.1993 iso systems of limits and fits,Vishal Mistry
This document provides information on an Indian standard regarding the ISO system of limits and fits. It establishes the bases of the ISO system including standard tolerances and fundamental deviations. The standard defines key terms used such as shaft, hole, size, deviation, tolerance, and fits. It provides graphical representations of tolerances and deviations. The standard aims to promote accuracy and consistency in manufacturing parts that interface through standardized tolerances and limits.
After this PPT you'll get idea about 'What is quality control? Why is Quality control Important? Types of Quality control, What is quality inspection? Tools of Quality inspection and Quality inspection loop.'
Statistical process control (SPC) involves using statistical methods to monitor and control processes to ensure they produce conforming products. Variation exists in all processes, and SPC helps determine when variation is normal versus requiring correction. Key SPC tools include control charts, which graph process data over time to identify special causes of variation needing addressing. Process capability analysis also examines whether a process can meet specifications under natural variation. Together these tools help processes run at full potential with minimal waste.
This document discusses cost of quality and provides definitions, categories, and models of quality costs. It defines cost of quality as the costs incurred to prevent, detect, and fix defects. Quality costs are divided into conformance costs (prevention and appraisal) and non-conformance costs (internal and external failure). Prevention costs aim to avoid defects, appraisal costs detect defects, and failure costs result from defects. The document also outlines the history of cost of quality analysis, gives examples to illustrate the categories, and presents a case study of a company's quality costs over four years that demonstrates how prevention costs can reduce total quality costs.
Statistical process control (SPC) uses statistical methods to monitor and control processes. The goal of SPC is to reduce process variability through the prevention of defects. SPC analyzes process data over time to distinguish common and special causes of variation. Key aspects of SPC include understanding variable and attribute data, using control charts to monitor processes, and calculating process capability indices to quantify a process's ability to meet specifications.
Control charts are used to monitor process variables over time in various industries and organizations. They tell us when a process is out of control by showing data points outside the control limits. When this occurs, those closest to the process must find and eliminate the special cause of variation to prevent it from happening again. Control charts have basic components like a centerline and upper and lower control limits. They are constructed by selecting a process, collecting data, calculating statistics and control limits, and plotting the results over time. Control charts come in two types - variables charts for continuous measurements and attributes charts for counting items. Common and special causes can lead to variations monitored by these charts.
This presentation provides an overview of control charts, including what they are, their purposes and advantages, different types of control charts, and how to construct and interpret them. Control charts graphically display process data over time to determine whether a manufacturing or business process is in a state of statistical control. The presentation discusses variable and attribute control charts, and specific charts like X-bar and R-bar charts. It provides examples of how to calculate control limits and plot data on a chart, and how to interpret results to determine if a process is capable or needs improvement. A case study example analyzing wait time data from a hotel management company is also reviewed.
This document provides an introduction to statistical process control (SPC). It defines SPC as a strategy that uses statistical techniques to evaluate processes, identify variability, and find opportunities for improvement. The goal of SPC is to make high-quality products the first time by reducing variability, rather than reworking defective products. It focuses on monitoring process behavior rather than just final product quality. SPC distinguishes between common cause variability that is always present and special cause variability that can be addressed to improve the process. It emphasizes identifying and addressing special causes first before adjusting process means. Control charts are used to monitor processes and determine if they are in control or need adjustment.
This document outlines the course content for a statistical quality control course. It covers 5 chapters: 1) Introduction to statistical quality control, 2) Methods of statistical process control and capability analysis, 3) Other statistical process monitoring and control techniques, 4) Acceptance sampling, and 5) Reliability and life testing. Key topics include the history of statistical quality control, uses of SQC, quality improvement, modeling process quality, control charts, process capability indices, cumulative sum control charts, and acceptance sampling concepts. The document provides an overview of the concepts, methods, and techniques that will be covered in the statistical quality control course.
This document provides an introduction to statistical process control (SPC). It discusses the limitations of inspection and why SPC is better. It explains that SPC allows monitoring of processes to detect changes before defective products are produced. Various control chart templates are shown and key SPC concepts are defined, including sources of variation, the central limit theorem, and using average and range to monitor process behavior over time. Examples are provided to illustrate variability, distributions, and how control charts can be used.
Statistical Process Control & Operations Managementajithsrc
This document discusses statistical process control and quality management techniques. It defines key terms like chance causes, assignable causes, control charts, attributes and variables. It also describes different types of control charts like Pareto charts, fishbone diagrams, mean charts, range charts, p-charts and c-charts. The document provides examples of how to construct and interpret these different control charts. It also discusses acceptance sampling and how to construct an operating characteristic curve.
There are six measures that can define a process's capability: Z values, sigma, percentage out of specification, defects per million opportunities (DPMO), defects per number of opportunities, and Cpk. These measures represent both short-term and long-term capability. Short-term refers to random variation, while long-term also includes non-random sources of drift. To convert between them, a 1.5 sigma shift is typically used. Calculating percentage out of specification first is generally recommended to determine process capability without distributional assumptions.
FMEA is a systematic method for evaluating potential failures in a design, manufacturing or assembly process. It involves analyzing possible failures, identifying their causes and effects, and prioritizing issues based on severity, occurrence, and detection. The process results in a risk priority number to determine which failures should be addressed first. FMEA is widely used in industries like automotive, aerospace, healthcare to prevent failures and improve quality and safety.
After World War II, Japan adopted quality as an economic strategy and selected seven statistical tools to analyze quality problems and drive continuous improvement. The seven tools - Pareto charts, cause-and-effect diagrams, histograms, control charts, scatter plots, check sheets, and flow charts - can identify up to 95% of issues. Each tool has a specific purpose, such as prioritizing problems with Pareto charts or identifying relationships between variables with scatter plots. Using these tools, Japanese companies were able to dramatically improve quality and economic performance.
The cause of inefficiency and poor quality is the system, not the employees and it is management’s responsibility to correct the system in order to achieve desired results. The quality of a product is the driving force for any organisation.
This document provides an introduction to statistical process control (SPC) and control charts. It discusses the basic concepts of common cause and special cause variation and how control charts can distinguish between them. The objectives of control charts are to detect special causes of variation so corrective actions can be taken to reduce nonconforming units and keep the process stable and predictable. The document reviews the anatomy of control charts and rules for interpreting when a process is in or out of statistical control. Finally, it outlines the different types of control charts for variable and attribute data.
Quality Control Tools And Techniques PowerPoint Presentation Slides SlideTeam
Find issues related to the quality using Quality Control Tools And Techniques PowerPoint Presentation Slides. Solve most quality related issues with the help of professionally designed quality control tools and techniques PPT templates. These templates showcase necessary tools required to control the quality within an organization or project. These templates have the relevant content to detect and control the loopholes. This deck comprises of quality tools such as cause and effect diagram, check sheet, control charts, histogram, pareto chart, scatter diagram, and more. Implement these tools to enhance the quality of the product and services or the quality of an organization. These tools and techniques are apt for quality planning and quality assurance. Prioritize objectives and discover problem areas by adding quality control charts and graphs. These PPT templates are completely customizable. Edit the color, text, icon and font size as per your requirement. Download quality control tools or also known as Kaoru Ishikawa’s quality tools presentation slideshow to achieve the desired goals and outcomes. Develop a feel for difficulties ahead with our Quality Control Tools And Techniques Powerpoint Presentation Slides. They help anticipate hassles.
Statistical quality control presentationSuchitra Sahu
Here are the key steps to construct a C-chart for this example:
1. Count the number of defects (misspelled words) in each sample (newspaper edition)
2. Calculate the average number of defects per unit (C=average number of defects)
3. Calculate the upper and lower control limits
4. Plot the number of defects for each sample versus the sample number
5. Analyze for points outside the control limits to identify periods where the process is out of control
Does this help explain the basic approach to constructing a C-chart? Let me know if you need any clarification or have additional questions.
Taguchi Method is a new engineering design optimisation methodology that improves the quality of existing products and processes and simultaneously reduces their costs very rapidly, with minimum engineering resources and development man-hours
This presentation from IVT's 2nd Annual Validation Week Canada covers the 2011 FDA Process validation and the subsequent statistical processes. Statistics in process validation is introduced as well as the integration with six sigma and solutions to common mistakes.
Is 919.1.1993 iso systems of limits and fits,Vishal Mistry
This document provides information on an Indian standard regarding the ISO system of limits and fits. It establishes the bases of the ISO system including standard tolerances and fundamental deviations. The standard defines key terms used such as shaft, hole, size, deviation, tolerance, and fits. It provides graphical representations of tolerances and deviations. The standard aims to promote accuracy and consistency in manufacturing parts that interface through standardized tolerances and limits.
The document discusses key terminology used in limits, fits, and tolerances including:
- Basic size, actual size, limits of size, deviations, tolerance, fundamental deviations, and fundamental tolerances.
- Holes and shafts refer to internal and external features, respectively.
- Fits include clearance, interference, and transition fits depending on how the tolerance zones of the hole and shaft overlap.
- Mass production aims to reduce costs and time through standardized parts, tools, and measurements while ensuring interchangeability.
Using minitab instead of tables for z values probabilities etcBrent Heard
This document discusses using Minitab instead of tables to find probabilities and z-values for the standard normal distribution. It provides examples of finding probabilities for given z-values using both tables and Minitab, and shows that Minitab makes the calculations faster and easier. The document also demonstrates how to use Minitab to find z-values for given probabilities, as well as find the z-values that define a symmetric probability between them. Overall, the document promotes using Minitab over tables for standard normal distribution calculations.
The document provides guidance on preparing for group discussions (GDs) and interviews. It outlines common mistakes to avoid in GDs such as arguing, not listening to others, and lacking knowledge. It also discusses the importance of communication skills, teamwork, and confidence. For interviews, the document recommends preparing answers to common questions, avoiding arguments, and highlighting achievements. The overall message is to practice actively, learn from mistakes, and approach GDs and interviews with a positive attitude.
The document discusses the history and basic types of milling machines. It begins by defining a milling machine as a machine tool that uses multiple-toothed cutters to remove metal from a workpiece. Eli Whitney is credited with developing the first milling machine in the early 1800s. There are two basic types - vertical and horizontal. Vertical mills have a vertically oriented cutting tool, while horizontal mills have a horizontally oriented cutting tool. The document then discusses the basic components and functions of both vertical and horizontal milling machines.
The document describes the parts and functions of an indexing or dividing head used on milling machines. The indexing head allows the precise rotation of a workpiece to cut complex shapes. It consists of a headstock, index plates, gears, and other components. There are several methods for indexing including direct, simple, angular, and differential indexing. Direct indexing uses numbered slots or holes to rotate the workpiece a set number of divisions. Simple indexing uses gears and a crank to rotate the workpiece a calculated fraction of a turn.
Calculating critical values of t distributions using tables of percentage pointsmodelos-econometricos
This document discusses how to calculate critical values for the t-distribution. It shows how to use tables of percentage points of the t-distribution to find one-tailed and two-tailed critical values for 5% and 1% significance levels with 72 degrees of freedom. The steps include locating the column for the desired significance level, interpolating if the degrees of freedom do not match the table, and determining the critical values. Formulas for linear interpolation are provided.
This document summarizes a lecture on fits and tolerances in engineering drafting. It defines tolerance as the total amount a dimension can vary between its maximum and minimum limits. It discusses different ways to express tolerances, including direct limits, geometric tolerancing, and general notes. Key terms are introduced, such as nominal size, basic size, actual size, limits, clearance fits, interference fits, and transition fits. Standard tables for shaft and hole fits in both English and metric units are referenced. Examples are provided for calculating fits and tolerances.
The document discusses two main types of statistical sampling for quality control: acceptance sampling and statistical process control. Acceptance sampling involves inspecting finished products to determine whether to accept or reject the entire lot, while statistical process control involves sampling processes to determine if they are operating within acceptable limits. The document then provides more details on acceptance sampling, including its purposes and advantages, how acceptance sampling plans are designed, typical applications of acceptance sampling, and how operating characteristic curves are used to calculate acceptance sampling plans.
This document provides an overview of limits, fits, and tolerances in manufacturing. It defines key terms like nominal size, tolerance, limits, allowance, and describes different systems of tolerances. It explains the different classes of fits - clearance, transition, and interference - and gives examples. It also discusses hole basis and shaft basis systems, interchangeability, and selective assembly. Fundamental deviations and tolerance grades according to the IS system are presented, along with examples of calculating tolerances and limits of size for hole and shaft assemblies.
This document discusses interchangeable manufacture, terminology for limits and fits. It defines interchangeable manufacture as parts that are identical enough to be mutually interchangeable in any device of the same type. It provides examples like bottle caps, rims, tires. The advantages are easy replacement, assembly, repair by minimizing time and cost.
It then defines terminology for limits and fits, including basic size, tolerance, allowance, deviations, fits. It explains hole basis and shaft basis systems for defining limits and fits between holes and shafts to achieve clearances, interference or transition fits.
El documento describe los conceptos clave relacionados con el tamaño de la muestra en estadística. Explica que la población es el conjunto total de unidades de estudio, y la muestra es una parte representativa de esta. Luego detalla cinco tipos de muestreo como aleatorio simple, estratificado, por áreas, sistemático y discrecional. Finalmente da un ejemplo de calcular el tamaño de muestra para investigar el consumo eléctrico en una ciudad de 30,000 medidores con un error máximo del 5%.
This document discusses efficient reliability demonstration tests that can reduce sample sizes and test times compared to conventional methods. It presents principles for test time reduction using degradation measurements during testing. Methods are provided for calculating optimal test plans that minimize costs while meeting reliability requirements and risk constraints. Decision rules are given for terminating tests early based on degradation measurements and risk estimates. An example application demonstrates how the approach can significantly reduce testing costs.
The document discusses tolerances and allowances in manufacturing. It defines tolerance as the acceptable variation from a nominal dimension, with three common types being limit, unilateral, and bilateral tolerances. Allowance is defined as a planned deviation from nominal to account for dimensional changes during future manufacturing processes, such as grinding or heat treating. The document explains that tolerances and allowances are important for interchangeable parts, product design, quality control, and manufacturing efficiency.
The standard normal curve & its application in biomedical sciencesAbhi Manu
1) The document discusses the normal distribution and its applications in statistical inference. It is the most important probability distribution used to model many continuous variables in biomedical fields.
2) The normal distribution is characterized by its mean and standard deviation. It is perfectly symmetrical and bell-shaped. Properties of the normal curve include that about 68%, 95%, and 99.7% of the data lies within 1, 2, and 3 standard deviations of the mean, respectively.
3) The standard normal distribution is used to convert raw scores to z-scores in order to compare variables measured on different scales. Z-scores indicate how many standard deviations a score is above or below the mean and can be used to determine probabilities, percentiles
The document discusses methods for determining sample sizes in reliability testing. It covers two main approaches: the estimation approach which aims to control the confidence interval width, and the risk control approach which aims to control type I and type II errors. Examples are provided to demonstrate how to use each approach to determine the needed sample size given parameters like required reliability, confidence level, allowable failures. Both parametric and non-parametric methods are introduced for different test scenarios. Software tools can help calculate the sample sizes required to meet the test objectives.
Este documento describe diferentes tipos de planes de muestreo, incluyendo planes de muestreo simples, dobles y múltiples. Explica cómo se usan estos planes para inspeccionar lotes de producción y tomar decisiones de aceptación o rechazo en función de la calidad detectada en las muestras. También discute la relación entre los riesgos del productor y el consumidor en los planes de muestreo.
Stantards, ISO(INTERNATIONAL STANTARD ORGANAIZATION),BSI(Bureau of Indian Sta...Musthafa K M
This presentation gives brief idea about ISO(International Standard Organization) ,Bureau of Indian Standards, and ISI mark and various concept for standardization of products and services.
This document outlines statistical quality control techniques for evaluating manufacturing and service processes. It discusses measuring and controlling process variation using variables like mean, standard deviation and control charts. Key aspects covered include process capability analysis using metrics like Cpk, acceptance sampling plans to determine quality levels while balancing producer and consumer risks, and operating characteristic curves.
Statistical Process Control WithAdrian™ AQPAdrian Beale
Statistical Process Control (SPC) is a technique used to interpret and organize numerical data from processes by identifying sources of variation. The goal of SPC is to ensure consistent process performance over time through defect prevention and reducing external inspection. Process capability compares a process's actual performance to specifications by measuring how widely a process's output varies from the mean and determining if it falls within set limits.
The document discusses parametric tolerance interval tests for assessing delivered dose uniformity of orally inhaled products. It provides details on:
- What parametric tolerance intervals and the FDA-proposed two one-sided tolerance interval test are
- How the test determines if a pre-specified proportion of doses fall within the target interval limits with a certain confidence level
- Operational characteristics and acceptance criteria for the two-tiered test approach
- Challenges and advantages of the parametric tolerance interval and alternative counting tests
This document discusses various statistical tools used in decision making, including regression analysis, confidence intervals, comparison tests, and analysis of variance. It provides examples of how regression analysis can be used to determine correlations and unknown parameters. It also explains how confidence intervals are calculated and used to determine how reliable a sample statistic is in estimating an unknown population parameter. Comparison tests are outlined as a method to determine if one process or supplier is better than another.
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The Use Of Statistical Tolerance Limits For Process
1. The Application of Statistical ToleranceThe Application of Statistical Tolerance
Limits in Process QualificationLimits in Process Qualification
SpeakerSpeaker
David A. Goodrich, P.E., CQE, CQADavid A. Goodrich, P.E., CQE, CQA
2. What are Tolerance Limits?What are Tolerance Limits?
Tolerance limits define an interval that coversTolerance limits define an interval that covers
a proportion of the overall population with aa proportion of the overall population with a
given confidence levelgiven confidence level
In 1931 tolerance limits were introduced byIn 1931 tolerance limits were introduced by
Shewhart in his book “ Economic Control ofShewhart in his book “ Economic Control of
Quality of Manufactured Product.”Quality of Manufactured Product.”
3. Why Use Tolerance Limits for ProcessWhy Use Tolerance Limits for Process
Qualification?Qualification?
Provides a statement of confidence and reliabilityProvides a statement of confidence and reliability
about the processabout the process
Accounts for uncertainty due to sample sizeAccounts for uncertainty due to sample size
Allows comparison of the practical processAllows comparison of the practical process
boundaries to the design specificationboundaries to the design specification
Using Cpk for process qualification is flawed due toUsing Cpk for process qualification is flawed due to
the relatively small sample sizes used and the lack ofthe relatively small sample sizes used and the lack of
sufficient process data to establish that the processsufficient process data to establish that the process
data is in statistical controldata is in statistical control
4. Formula for Tolerance LimitsFormula for Tolerance Limits
Standard deviation method (normality assumed)Standard deviation method (normality assumed)
For two-sided limits:For two-sided limits:
Tolerance limitsTolerance limits = X + K= X + K22ss
For one-sided limit:For one-sided limit:
Tolerance limitTolerance limit = X= X ++ KK11s or Xs or X -- KK11ss
Where:Where: X = sample averageX = sample average
KK11 = tolerance limit factor (one-sided)= tolerance limit factor (one-sided)
KK22 = tolerance limit factor (two-sided)= tolerance limit factor (two-sided)
S = sample standard deviationS = sample standard deviation
5. Calculating the Tolerance LimitsCalculating the Tolerance Limits
Test for a normal distributionTest for a normal distribution
Two-sided or one-sided specificationTwo-sided or one-sided specification
Sample Mean and Standard DeviationSample Mean and Standard Deviation
Confidence and reliability for process acceptanceConfidence and reliability for process acceptance
Based on risk (product safety)Based on risk (product safety)
Based on economics (cost of poor quality)Based on economics (cost of poor quality)
Compute using K-factor tables (Juran, etc.)Compute using K-factor tables (Juran, etc.)
Sample Size used to determine Mean and Standard DeviationSample Size used to determine Mean and Standard Deviation
Confidence and ReliabilityConfidence and Reliability
Computer applicationsComputer applications
Minitab (Minitab free macro available)Minitab (Minitab free macro available)
““Home grown” Excel spreadsheets availableHome grown” Excel spreadsheets available
6. Example 1- Container filling operationExample 1- Container filling operation
The fill specification is 6.95 to 7.05 gThe fill specification is 6.95 to 7.05 g
A sample of 66 containers were weighed whichA sample of 66 containers were weighed which
resulted in a mean weight of 7.0113 g and aresulted in a mean weight of 7.0113 g and a
standard deviation of 0.0066 gstandard deviation of 0.0066 g
What are the 95% upper and lower tolerance limitsWhat are the 95% upper and lower tolerance limits
for 95% of the population?for 95% of the population?
8. Test For NormalityTest For Normality
Assessing normality using the Ryan-Joiner test.Assessing normality using the Ryan-Joiner test.
Null hypothesis: the data {Null hypothesis: the data {xx1, ...,1, ..., xnxn} are a random} are a random
sample of sizesample of size nn from a normal distribution.from a normal distribution.
Alternative hypothesis: the data are a random sampleAlternative hypothesis: the data are a random sample
from some other distribution.from some other distribution.
Desired confidence level 95%Desired confidence level 95%
9. P > 0.10 Cannot reject the null hypothesis. The data appearP > 0.10 Cannot reject the null hypothesis. The data appear
to be consistent with a sample from a normal distribution.to be consistent with a sample from a normal distribution.
P-Value (approx): > 0.1000
R: 0.9860
W-test for Normality
N: 66
StDev: 0.0066281
Average: 7.01129
7.0257.0157.0056.995
.999
.99
.95
.80
.50
.20
.05
.01
.001
Probability
Fill Weight
Normal Probability PlotExample 1
10. Tolerance Limits CalculatedTolerance Limits Calculated
From K-factor table (two sided)From K-factor table (two sided)
For N=60 (closest to 66)For N=60 (closest to 66)
Confidence = .95Confidence = .95
Reliability (population) = .95Reliability (population) = .95
KK22 = 2.333= 2.333
Tolerance limitsTolerance limits = X= X ++ KK22ss
= 7.0113 g= 7.0113 g ++ 2.333(0.0066)2.333(0.0066)
== 6.99596.9959 to 7.0267 gto 7.0267 g
Compared to specification of 6.95 to 7.05 g, this process meetsCompared to specification of 6.95 to 7.05 g, this process meets
the 95%/95% confidence/reliability for acceptancethe 95%/95% confidence/reliability for acceptance
Example 1
11. Example 2 – Container Seal BurstExample 2 – Container Seal Burst
StrengthStrength
Minimum burst specification is 26.3 psiMinimum burst specification is 26.3 psi
A sample size of 40 container seals were burst toA sample size of 40 container seals were burst to
failure with a mean failure pressure of 48.175 psifailure with a mean failure pressure of 48.175 psi
and a standard deviation of 4.590 psiand a standard deviation of 4.590 psi
Will the 95% lower tolerance limit for 95% of theWill the 95% lower tolerance limit for 95% of the
population meet the specification?population meet the specification?
13. Average: 48.175
StDev: 4.59033
N: 40
W-test for Normality
R: 0.9913
P-Value (approx): > 0.1000
40 45 50 55
.001
.01
.05
.20
.50
.80
.95
.99
.999
Probability
Pressure
Burst
Normal Probability Plot
Example 2
14. Lower Tolerance Limit CalculatedLower Tolerance Limit Calculated
From K-factor table (one sided)From K-factor table (one sided)
For N=40For N=40
Confidence = .95Confidence = .95
Reliability (population) = .95Reliability (population) = .95
KK11 == 2.1262.126
Tolerance limitTolerance limit = X – K= X – K11ss
= 48.175 - 2.126(4.590 )= 48.175 - 2.126(4.590 )
== 38.4238.42 psipsi
Compared to the specification of 26.3 psi, this process meets theCompared to the specification of 26.3 psi, this process meets the
95%/95% confidence/reliability for acceptance95%/95% confidence/reliability for acceptance
Example 2
15. Example 3 – Minimum Sample SizeExample 3 – Minimum Sample Size
15 new sealing machines must be validated (all exact make and model)15 new sealing machines must be validated (all exact make and model)
for pouch a sealing processfor pouch a sealing process
A minimum of five runs required per validation (2 at max/min, 3 atA minimum of five runs required per validation (2 at max/min, 3 at
nominal process parameters)nominal process parameters)
For a C = 0 failures attribute sampling plan, a 95% confidence level of
95% reliability would require 59 samples per run, for a total of 4425
samples to validate 15 machines
N = ln (1 – confidence) / ln reliabilityN = ln (1 – confidence) / ln reliability
A thorough process study of one existing (identical) machine wasA thorough process study of one existing (identical) machine was
conducted with the following results:conducted with the following results:
Data distribution is normalData distribution is normal
Process mean = 6.788 lbfProcess mean = 6.788 lbf
Process standard deviation = 1.378 lbfProcess standard deviation = 1.378 lbf
Based on the available process data, for a specification of 1.0 lbfBased on the available process data, for a specification of 1.0 lbf
minimum peel strength, what is the minimum practical sample size perminimum peel strength, what is the minimum practical sample size per
run that can be used for the remaining machines?run that can be used for the remaining machines?
16. Minimum Sample Size CalculatedMinimum Sample Size Calculated
Specification Limit = 1.0 lbfSpecification Limit = 1.0 lbf
Process Mean = 6.788 lbfProcess Mean = 6.788 lbf
Process Standard Deviation = 1.378 lbfProcess Standard Deviation = 1.378 lbf
1) Solve for one-sided K-factor1) Solve for one-sided K-factor
6.788 - 1.0 = K ( 1.378)6.788 - 1.0 = K ( 1.378)
K = (6.788 – 1.0)/1.378K = (6.788 – 1.0)/1.378
K = 4.20K = 4.20
2) Reverse look-up in K-factor Table2) Reverse look-up in K-factor Table
Confidence = .95Confidence = .95
Reliability (population) = .95Reliability (population) = .95
For KFor K11 = 4.20= 4.20 Therefore:Therefore: NN = 5 samples= 5 samples
Therefore, the total number of samples required to validate 15 machinesTherefore, the total number of samples required to validate 15 machines
based onbased on variables data is reduced from 4425 tois reduced from 4425 to 375 samples (5x15x5)(5x15x5)
Example 3
17. Data with Unknown DistributionData with Unknown Distribution
- Requires a non-parametric estimate of the toleranceRequires a non-parametric estimate of the tolerance
intervalinterval
- Two-Sided Intervals (Juran Table W)Two-Sided Intervals (Juran Table W)
- Based on sample size only, Juran Table W shows for a giveBased on sample size only, Juran Table W shows for a give
confidence, the proportion of the process that liesconfidence, the proportion of the process that lies
between the extremes of the sample measurementsbetween the extremes of the sample measurements
- One-Sided IntervalsOne-Sided Intervals
- Based on sample size only, an upper or lower toleranceBased on sample size only, an upper or lower tolerance
limit can be estimated based on the one-sided extreme oflimit can be estimated based on the one-sided extreme of
the measurements:the measurements:
N = ln (1 – C) / ln PN = ln (1 – C) / ln P
where:where: C = confidence levelC = confidence level
P = population proportion (reliability)P = population proportion (reliability)
18. Data with Unknown DistributionData with Unknown Distribution
(Example)(Example)
- One-Sided Lower Limit Example:One-Sided Lower Limit Example:
- What is the sample size required to determine with 95%What is the sample size required to determine with 95%
confidence that a process yields results with 90% ofconfidence that a process yields results with 90% of
population equal to or above the lowest measurement inpopulation equal to or above the lowest measurement in
the samplethe sample
N = ln (1 – C) / ln PN = ln (1 – C) / ln P
wherewhere C = confidence levelC = confidence level
P = population proportionP = population proportion
N = ln (1 - .95) / ln .90N = ln (1 - .95) / ln .90
N = ln .05 / ln .90N = ln .05 / ln .90
N = 28.4 (rounded up to nearest whole number N = 29)N = 28.4 (rounded up to nearest whole number N = 29)
19. Questions ?Questions ?
Helpful Web addressesHelpful Web addresses
Mintab (http://www.Minitab.Com)Mintab (http://www.Minitab.Com)
Excel spreadsheet for tolerance limitsExcel spreadsheet for tolerance limits
(http://statpages.Org/tolintvl.Html)(http://statpages.Org/tolintvl.Html)
NIST engineering statistics handbookNIST engineering statistics handbook
(http://www.itl.nist.gov/div898/handbook/index.htm)(http://www.itl.nist.gov/div898/handbook/index.htm)