The document describes an experiment to optimize cutting parameters during CNC turning of AISI 8620 alloy steel using response surface methodology. Speed, feed rate, and depth of cut were varied as factors in a 3-level factorial design. The response variable was surface roughness, which was measured after each experimental run. Regression analysis in Minitab was used to develop a model relating the factors to surface roughness. Response optimization was then used to determine the optimal settings of the factors to minimize surface roughness. The goal of the experiment was to evaluate the best cutting parameter settings for achieving a smooth surface finish during CNC turning of AISI 8620 alloy steel.
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEELIAEME Publication
This paper presents the optimization of surface roughness & material removal rate in dry turning of SAE52100 steel. Carbide inserts were used for machining of SAE 52100 to study effects of process parameters [Cutting speed (S), Feed (F) and depth of cut (d)]. These models can be effectively used to predict the surface roughness (Ra) & material removal rate of the workpiece. The big challenge of the Micro, small& medium industries in India for achieving high quality
products with increased productivity.
Analysis of process parameters in dry machining of en 31 steel by grey relati...IAEME Publication
This paper presents the optimization of surface roughness & material removal rate in dry turning of EN-31 steel.Carbide inserts were used for machining of EN-31 to study effects of process parameters [Cutting speed (S), Feed (F) and depth of cut (d)]. These models can be effectively used to predict the surface roughness (Ra) of the workpiece. The big challenge of the Micro, small& medium industries in India for achieving high quality products with increased productivity.Paper presentswork of an investigation of turning process parameters on EN-31 material, for optimization of surface roughness, material removal rate.The experiment is carried out by considering three controllable input variables namely cutting speed, feed rate, and depth of cut.The design of experiment and optimization of surface roughness is carried out by using Taguchi L9 orthogonal array & Grey Relational analysis.
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 big challenge of the mass production firms is concentrated for achieving high quality
products with good dimensionability with high productivity, less wear on the cutting insert, less use
of cutting fluid, within less time. This paper present dissertation work of an investigation of turning
process parameters on hard EN 31 material, for optimization of surface roughness, material removal
rate, machining time in wet and minimum quantity lubrication system. The experiment is carried out
by considering four controllable input variables namely cutting speed, feed rate, depth of cut and
insert nose radius in the presence of wet & MQL system. This experiment also present the relation
between chip formations and controllable variables along with chip thickness, chip colors & chip
velocity from which its effect on insert wear, quality of product can be easily found out, because of
chip morphology gives indirectly the effect of it on the insert wear. In this dissertation work
minimum quantity lubrication system is used for reducing the cutting zone temperature properly and
very fastly. Finally comparison is carried out between wet and minimum quantity lubrication system
from which one can easily identify which system is better for higher productivity along with high
surface finish. This work also present the productivity (MRR) concept in production. The design of
experiment and optimization of surface roughness, material removal rate, machining time is carried
out by using response surface methodology (RSM). Central composite design method is used (CCD)
for the total experimental design work and its analysis and also for optimization of turning process
parameter by which wastage of the machining time, power can be avoided.
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEELIAEME Publication
This paper presents the optimization of surface roughness & material removal rate in dry turning of SAE52100 steel. Carbide inserts were used for machining of SAE 52100 to study effects of process parameters [Cutting speed (S), Feed (F) and depth of cut (d)]. These models can be effectively used to predict the surface roughness (Ra) & material removal rate of the workpiece. The big challenge of the Micro, small& medium industries in India for achieving high quality
products with increased productivity.
Analysis of process parameters in dry machining of en 31 steel by grey relati...IAEME Publication
This paper presents the optimization of surface roughness & material removal rate in dry turning of EN-31 steel.Carbide inserts were used for machining of EN-31 to study effects of process parameters [Cutting speed (S), Feed (F) and depth of cut (d)]. These models can be effectively used to predict the surface roughness (Ra) of the workpiece. The big challenge of the Micro, small& medium industries in India for achieving high quality products with increased productivity.Paper presentswork of an investigation of turning process parameters on EN-31 material, for optimization of surface roughness, material removal rate.The experiment is carried out by considering three controllable input variables namely cutting speed, feed rate, and depth of cut.The design of experiment and optimization of surface roughness is carried out by using Taguchi L9 orthogonal array & Grey Relational analysis.
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 big challenge of the mass production firms is concentrated for achieving high quality
products with good dimensionability with high productivity, less wear on the cutting insert, less use
of cutting fluid, within less time. This paper present dissertation work of an investigation of turning
process parameters on hard EN 31 material, for optimization of surface roughness, material removal
rate, machining time in wet and minimum quantity lubrication system. The experiment is carried out
by considering four controllable input variables namely cutting speed, feed rate, depth of cut and
insert nose radius in the presence of wet & MQL system. This experiment also present the relation
between chip formations and controllable variables along with chip thickness, chip colors & chip
velocity from which its effect on insert wear, quality of product can be easily found out, because of
chip morphology gives indirectly the effect of it on the insert wear. In this dissertation work
minimum quantity lubrication system is used for reducing the cutting zone temperature properly and
very fastly. Finally comparison is carried out between wet and minimum quantity lubrication system
from which one can easily identify which system is better for higher productivity along with high
surface finish. This work also present the productivity (MRR) concept in production. The design of
experiment and optimization of surface roughness, material removal rate, machining time is carried
out by using response surface methodology (RSM). Central composite design method is used (CCD)
for the total experimental design work and its analysis and also for optimization of turning process
parameter by which wastage of the machining time, power can be avoided.
Effectiveness of multilayer coated tool in turning of aisi 430 f steeleSAT Journals
Abstract This paper presents minimization of surface roughness in dry turning of AISI 430F steel using TiN-TiCN-Al2O3-ZrCN multilayer coated cemented carbide & cryo-treated inserts. Effect of cutting velocity, feed rate, depth of cut & machining duration is studied on the surface roughness. Taguchi’s design of experiment is used to find the optimum factor levels. It is found that the feed rate has much effect in producing lower surface roughness followed by speed. The depth of cut has lesser role on surface roughness. The result of Taguchi method shows that cutting velocity of 250m/min, feed rate of 0.25 mm/rev and depth of cut of 0.3mm should be maintained as optimal parameter settings for both coated and cryo-treated tools. Cryo-treated tools perform better. Keywords: Cryo-treatment, Dry Turning, Surface roughness, Taguchi Method
Optimization of Material Removal Rate & Surface Roughness in Dry Turning of M...IJERA Editor
Optimization of machining parameters is valuable to maintain the accuracy of the components and to minimize
the cost of machining. Surface finish is an important measure for the quality of the machined parts. The present
work is an experimental investigation to study the effect of machining parameters on Material Removal Rate
and Surface Roughness in dry turning of medium carbon steel EN19. Taguchi’s single objective optimization
method was used to find the effect of input parameters on the responses. The experiments were conducted as per
Taguchi’s L9 Orthogonal Array on CNC lathe under dry conditions. Cutting parameters of speed, feed and
depth of cut were taken as inputs and machining was done by PVD TiAlN tool. Regression models for the
responses were prepared by using MINITAB-16 software. Analysis of variance (ANOVA) was used to find the
influence of machining parameters on the responses. From the ANOVA results, it is clear that speed has high
influence followed by feed and depth of cut has very low influence in achieving the optimum values for both
Material Removal Rate and Surface Roughness. Finally, experimental and Regression values of responses were
compared. From the results, it is found that both the values are close to each other hence, the regression models
prepared were more accurate and adequate. Percentage of errors between experimental and regression values
were calculated and they found in the range of ±0.20.
COMPARISON OF SURFACE ROUGHNESS OF COLDWORK AND HOT WORK TOOL STEELS IN HARD ...ijmech
The hard turning process has been attracting interest in different industrial sectors for finishing operations
of hard materials at its hardened state.Surface roughness is investigated in hard turning of AISI D3 and
AISI H13 steels of same hardness 62HRC. In this paper, an attempt has been made to model and predict
the surface roughness in hard turning of AISI D3 and AISI H13 hardened steels using Response Surface
Methodology (RSM). The combined effects of three machining parameters such as cutting speed, feed rate
and depth of cut are investigated for main performance characteristic that is surface roughness. RSM
based Central Composite Design (CCD) is applied as an experimental design. Al2O3/TiC mixed ceramic
tool with corner radius 0.8 mm is employed to accomplish 20 tests with six center points. The acceptability
of the developed models is checked using Analysis of Variance (ANOVA).The combined effects of cutting
speed; feed rate and depth of cut are investigated using surface plots.
Statistical Modeling of Surface Roughness produced by Wet turning using solub...IDES Editor
Machining tests were carried out by turning En-
31steel alloy with tungsten carbide tools using soluble oilwater
mixture lubricant under dif ferent machining
conditions. First-order and second-order surface roughness
predicting models were developed by using the experimental
data by applying response surface methodology and factorial
design of experiments. The established equations show that
the feed rate is the main influencing factor on the surface
roughness followed by tool nose radius and depth of cut. It
increases with increase in the feed rate but decreases with
increase in the cutting velocity and tool nose radius,
respectively. The predicted surface roughness values of the
samples have been f ound to lie close to that of the
experimentally observed values. There is an improvement
in surface finish by 10% with wet machining as compared
to dry machining.
Effect of process factors on surface roughness in dip cryogenic machining of ...eSAT Journals
Abstract A large number of structures that are being constructed at present tend to be wind-sensitive because of their slenderness, shapes, size, lightness and flexibility. With the ever increase in the vertical growth of urban cities, high rise buildings are being constructed in large numbers. In this study, analytical investigation of different shapes of buildings are taken as an example and various analytical approaches are performed on the building. These plans are modeled and wind loads are found out according to I.S 875(part 3)-1987 by taking gust factor and without taking gust factor. These models are compared in different aspects such as storey drift, storey displacement, storey shear, etc. for different shapes of buildings by using finite element software package ETAB’s 13.1.1v. Among these results, which shape of building provide sound wind loading to the structure as well as the structural efficiency would be selected. Key Words: Storey displacement, Storey drift, Storey shear, Gust, Wind load
Effectiveness of multilayer coated tool in turning of aisi 430 f steeleSAT Journals
Abstract This paper presents minimization of surface roughness in dry turning of AISI 430F steel using TiN-TiCN-Al2O3-ZrCN multilayer coated cemented carbide & cryo-treated inserts. Effect of cutting velocity, feed rate, depth of cut & machining duration is studied on the surface roughness. Taguchi’s design of experiment is used to find the optimum factor levels. It is found that the feed rate has much effect in producing lower surface roughness followed by speed. The depth of cut has lesser role on surface roughness. The result of Taguchi method shows that cutting velocity of 250m/min, feed rate of 0.25 mm/rev and depth of cut of 0.3mm should be maintained as optimal parameter settings for both coated and cryo-treated tools. Cryo-treated tools perform better. Keywords: Cryo-treatment, Dry Turning, Surface roughness, Taguchi Method
Optimization of Material Removal Rate & Surface Roughness in Dry Turning of M...IJERA Editor
Optimization of machining parameters is valuable to maintain the accuracy of the components and to minimize
the cost of machining. Surface finish is an important measure for the quality of the machined parts. The present
work is an experimental investigation to study the effect of machining parameters on Material Removal Rate
and Surface Roughness in dry turning of medium carbon steel EN19. Taguchi’s single objective optimization
method was used to find the effect of input parameters on the responses. The experiments were conducted as per
Taguchi’s L9 Orthogonal Array on CNC lathe under dry conditions. Cutting parameters of speed, feed and
depth of cut were taken as inputs and machining was done by PVD TiAlN tool. Regression models for the
responses were prepared by using MINITAB-16 software. Analysis of variance (ANOVA) was used to find the
influence of machining parameters on the responses. From the ANOVA results, it is clear that speed has high
influence followed by feed and depth of cut has very low influence in achieving the optimum values for both
Material Removal Rate and Surface Roughness. Finally, experimental and Regression values of responses were
compared. From the results, it is found that both the values are close to each other hence, the regression models
prepared were more accurate and adequate. Percentage of errors between experimental and regression values
were calculated and they found in the range of ±0.20.
COMPARISON OF SURFACE ROUGHNESS OF COLDWORK AND HOT WORK TOOL STEELS IN HARD ...ijmech
The hard turning process has been attracting interest in different industrial sectors for finishing operations
of hard materials at its hardened state.Surface roughness is investigated in hard turning of AISI D3 and
AISI H13 steels of same hardness 62HRC. In this paper, an attempt has been made to model and predict
the surface roughness in hard turning of AISI D3 and AISI H13 hardened steels using Response Surface
Methodology (RSM). The combined effects of three machining parameters such as cutting speed, feed rate
and depth of cut are investigated for main performance characteristic that is surface roughness. RSM
based Central Composite Design (CCD) is applied as an experimental design. Al2O3/TiC mixed ceramic
tool with corner radius 0.8 mm is employed to accomplish 20 tests with six center points. The acceptability
of the developed models is checked using Analysis of Variance (ANOVA).The combined effects of cutting
speed; feed rate and depth of cut are investigated using surface plots.
Statistical Modeling of Surface Roughness produced by Wet turning using solub...IDES Editor
Machining tests were carried out by turning En-
31steel alloy with tungsten carbide tools using soluble oilwater
mixture lubricant under dif ferent machining
conditions. First-order and second-order surface roughness
predicting models were developed by using the experimental
data by applying response surface methodology and factorial
design of experiments. The established equations show that
the feed rate is the main influencing factor on the surface
roughness followed by tool nose radius and depth of cut. It
increases with increase in the feed rate but decreases with
increase in the cutting velocity and tool nose radius,
respectively. The predicted surface roughness values of the
samples have been f ound to lie close to that of the
experimentally observed values. There is an improvement
in surface finish by 10% with wet machining as compared
to dry machining.
Effect of process factors on surface roughness in dip cryogenic machining of ...eSAT Journals
Abstract A large number of structures that are being constructed at present tend to be wind-sensitive because of their slenderness, shapes, size, lightness and flexibility. With the ever increase in the vertical growth of urban cities, high rise buildings are being constructed in large numbers. In this study, analytical investigation of different shapes of buildings are taken as an example and various analytical approaches are performed on the building. These plans are modeled and wind loads are found out according to I.S 875(part 3)-1987 by taking gust factor and without taking gust factor. These models are compared in different aspects such as storey drift, storey displacement, storey shear, etc. for different shapes of buildings by using finite element software package ETAB’s 13.1.1v. Among these results, which shape of building provide sound wind loading to the structure as well as the structural efficiency would be selected. Key Words: Storey displacement, Storey drift, Storey shear, Gust, Wind load
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...IAEME Publication
The big challenge of the mass production firms is concentrated for achieving high quality products with good dimensionability with high productivity, less wear on the cutting insert, less use
of cutting fluid, within less time. This paper present dissertation work of an investigation of turning process parameters on hard EN 31 material, for optimization of surface roughness, material removal rate, machining time in wet and minimum quantity lubrication system. The experiment is carried out by considering four controllable input variables namely cutting speed, feed rate, depth of cut and insert nose radius in the presence of wet & MQL system
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...IJERA Editor
Cylindrical grinding is one of the most important metal cutting processes used extensively in the Metal finishing operations. Metal removal rate and surface finish are the important output responses in the production with respect to quantity and quality respectively. The objective of this paper is to arrive at the optimal grinding conditions that will maximize metal removal rate when grinding IS 319 brass. Empirical models were developed using design of experiments by Taguchi L9 Orthogonal Array and the adequacy of the developed model is tested with ANOVA.
optimization of process parameters for cnc turning using taguchi methods for ...INFOGAIN PUBLICATION
Coated and uncoated tool inserts offers certain degrees of control on the desired rate of tool wear and surface roughness to an extent. This work pursues the quest for realizing the optimal values for the significant process parameters that bears an influence on the response parameters. Experiments were conducted on the samples of EN 24 alloy steel material with the help of PVD coated TiAlN insert and uncoated carbide insert. The experimental runs carried out with proper variation in the levels. Levels are selected with the help of manufacturing catalogue and by pilot experimentation and results are recorded for further analysis. For this study, 9 runs designed using L9 orthogonal array of Taguchi Design of Experiment. Surface roughness was measured using a Mitutoyo surface tester at test lab and material removal rate is calculated by mathematical equation. The data was compiled into Minitab 17 software for analysis. The relationship between the machining parameters and the response variables were analyzed using the Taguchi Method. Optimization of process parameters is carried out by Grey Relational Analysis method (GRA). GRA method is a powerful and most versatile tool which can manipulate the input data as per requirement and comes with results that can be used to have best multi-objective in respective concerns
ANALYSIS OF PROCESS PARAMETERS IN DRY MACHINING OF EN-31 STEEL by GREY RELATI...IAEME Publication
This paper presents the optimization of surface roughness & material removal rate in dry turning of EN-31 steel.Carbide inserts were used for machining of EN-31 to study effects of process parameters [Cutting speed (S), Feed (F) and depth o f cut (d)]. These models can be effectively used to predict the surface roughness (Ra) of the workpiece. The big challenge of the Micro, small& medium industries in India for achieving high quality products with increased productivity.Paper presents work of an investigation of turning process parameters on EN-31 material, for optimization of surface roughness, material removal rate.The experiment is carried out by considering three controllable input variables namely cutting speed, feed rate, and depth of cut.The design of experiment and optimization of surface roughness is carried out by using Taguchi L9 orthogonal array & Grey Relational analysis.
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.
Multi-Objective Optimization ( Surface Roughness & Material Removal Rate) of ...IJERA Editor
The present study applied Taguchi method through a case study in straight turning of AISI 202 stainless steel bar on CNC Machine ( Mfd by ACE DESIGNERS) using Titanium Carbide tool for the optimization of Material removal rate, Surface Roughness and tool wear process parameter.The study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality as well as productivity with special emphasis on maximizing material removal rate and minimizing surface roughness and tool flank wear at various combination of cutting speed, feed, depth of cut. The predicted optimal setting ensured maximum MRR and minimum surface roughness and tool wear. Since optimum material removal rate is desired, so higher the better criteria of Taguchi signal to noise ratio is used for MRR – SNs = -10 log(Sy2/n) For surface roughness and tool wear – SNL = -10 log(S(1/y2)/n) The results have been verified with the help of S/N Ratios calculation and various graphs have been plotted to show the below mentioned observations.
a) MRR first increases with increase in cutting speed and then decreases.
b) With the increase in feed, MRR increases.
c) With the increase in depth of cut, MRR first increases and then decreases.
d) With the increase in cutting speed, Surface Roughness first decreases and then increases.
e) With the increase in feed, Surface Roughness increases.
f) With the increase in depth of cut, Surface Roughness first increases and then decreases.
Implementation of Response Surface Methodology for Analysis of Plain Turning ...IJERD Editor
This paper investigates the effect of cutting speed, feed rate and depth of cut on the surface roughness of mild steel material with turning process. The response surface methodology (RSM) was employed in the experiment. The investigated turning parameters were cutting speed (CS) (1150, 850m/min), feed rate (FR) (1 and 0.5 mm/rev) and depth of cut (DOC) (1.0 and 0.5 mm) and no. of cuts(NOC) (2 and 1). The results showed that the interaction between the feed rate and depth of cut, was the primary factor controlling surface roughness. The responses of various factors were plotted using a three-dimensional surface graph. The optimum condition required for minimum surface roughness(SR) include cutting speed of 1150 m/min, feed rate of 1 mm/rev, axial depth of cut of 0.5 mm and no. of cut 1. With this optimum condition, a surface roughness of 0.280μm was obtained. The methodology for above experimentation is presented in this paper along with results and interpretation.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Q01314109117
1. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 13, Issue 1 Ver. IV(Jan. - Feb. 2016), PP 109-117
www.iosrjournals.org
DOI: 10.9790/1684-1314109117 www.iosrjournals.org 109 | Page
Parametric Optimization during CNC Turning of Aisi 8620 Alloy Steel
Using Rsm
G.Sandeep kumar*1
, R.K.Suresh2
and P.Dileep3
*1
PG Scholar, Mechanical engineering, Sri kalahastheswara institute of technology, India.
2
Assistant Prof (Sr), Mechanical engineering, Sri kalahastheswara institute of technology, India.
3
Lecturer, Mechanical engineering, Sri kalahastheswara institute of technology, India.
Abstract: Turning process is one of the method to remove material mainly from cylindrical work materials. The process of
turning is influenced by many factors such as the cutting speed, feed rate, depth of cut, nose radius, hardness of tool, cutting
conditions etc. The finished product with desired quality targets such as surface roughness and cutting forces developed
which are Responses of these input parameters. Properties such as wear resistance, fatigue strength, coefficient of friction,
lubrication, wear rate and corrosion resistance of the machined parts are greatly influenced by surface roughness. In many
manufacturing processes engineering judgment is still relied upon to optimize the response. Hence The present work
demonstrates the optimization process of surface roughness of Computer numerical control lathe machine (CNC) by using
Response Surface Methodology (RSM) By using 3 level factorial design in design expert 7.1, the experimental run setup
were designed . The process has been successfully modeled by using response surface methodology (RSM) in minitab 17and
model adequacy checking is also carried out. the response models have been validated with analysis of variance and
response optimizer function. The objective of this paper is to evaluate the optimal setting of cutting parameters such as
cutting Speed, depth of cut, feed of the tool to have a minimum surface roughness. In this experiment the work material of
AISI 8620 alloy steel was turned by using CVD(chemical vapor deposit) coated tool insert.
Keywords - AISI 8620 Alloy steel, CVD tool, RSM, Surface roughness.
I. INTRODUCTION
The important goal in the modern industries is to manufacture the product with lower cost and with high quality in
short span of time. There are two main practical problems that engineers face in a manufacturing process, the first is to
determine the values of process parameters that will yield the desire product quality (meet technical specifications) and the
second is to maximize manufacturing system performance using the available resources.
The challenge of modern machining industry is mainly focused on achievement of high quality ,in terms of work
piece dimensional accuracy, surface finish ,high production rate, less wear on the cutting tools ,economy of machining in
terms of cost saving and increase the performance of the product with reduced environmental impact. Today metal cutting
process places major portion of all manufacturing processes .Within these metal cutting processes the turning operation is
the most fundamental metal removal operation in the manufacturing industry. Increase in productivity and the quality of the
machined parts are the main challenges of metal based industry. There has been increased interest in monitoring all aspects
of machining process. Surface finish is an important parameter in manufacturing engineering it is a characteristic that could
influence the performance of mechanical parts and the production costs.
Surface roughness has become the most significant technical requirement and is an index of product quality in
order to improve the tribiological properties, fatigue strength, corrosion resistance and aesthetic appeal of the product
reasonably good surface finish is required. Now a day’s manufacturing industries specially concerned to dimensional
accuracy and surface finish In order to obtain better surface finish proper setting of cutting parameters is crucial before the
process takes place factors such as spindle speed ,feed rate, depth of cut that control the cutting operation can be set up in
advance .However, the factors such as geometry of cutting tool ,tool wear and joint material properties of both tool and work
pieces are uncontrollable .one should develop techniques and evaluate the surface roughness of the product before machining
in order to determine the required machining parameters such as feed rate, spindle speed, depth of cut for attaining desired
surface roughness and product quality.
II. Literature Survey
Author presents a new approach for multi response optimization during turning process. Using AISI 8620 alloy
steel that satisfies the chemical composition and required hardness, by machining of CNC turning centre using chemical
vapor deposition tool (CVD) approached in this process [1]. Speed of the spindle, feed, depth of cut are three turning
parameters used for computing the optimum surface roughness value.[ 1 ] [ 6 ] .
The approach is based on grey relation analysis and desirability function analysis through this study. The AISI
8620 alloy steel and CVD coated tool combination resulting in the better optimum values in the surface roughness. [1] [3].
R.K.Suresh, P.Venkataramaiah and G.Krishnaiah [2] envisages an experimental investigation on turning of AISI
8620 alloy steel using PVD coated cemented carbide CNMG insert. Nine experimental runs based on Taguchi factorial
design were performed to find out optimal cutting level condition. The main focus of present experimentation is to optimize
the process parameters namely spindle speed, feed and depth of cut for desired response characteristics i.e. surface
roughness, VMRR and interface temperature. To study the performance characteristics in this work orthogonal array (OA),
analysis of means (ANOM) and analysis of variance(ANOVA) were employed. The experimental results showed that the
spindle speed affects more on
2. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
DOI: 10.9790/1684-1314109117 www.iosrjournals.org 110 | Page
surface roughness, feed affects more on VMRR and feed affects more on interface temperature. Confirmation tests also been
performed to predict and verify the adequacy of models for determining optimal values of response characteristics.
The paper states that the Surface roughness and metal removal rate are significantly improved by cutting factors.
The results are concluded by desirable function analysis (DFA). The DFA is used to change the multi response characteristic
the work were accomplished by CNC lathe. [ 3 ].
The higher spindle speed, The higher feed, the higher depth of cut are optimum conditions of the process
parameters for turning AISI 8620 alloy steel. Study of investigation of [4].states that cutting speed contributes more
percentile of surface roughness while comparing feed and depth of cut [ 5 ]. Author presents result In an optimal value of
surface roughness by using AI 6351 – T6. alloy with uncoated carbide tool inserts. Regression techniques were used to
predict the surface roughness value and also taguchi techniques was used in this process. [ 5 ]. Environmental parameters are
included in this investigation which are dry cutting and wet cutting. S-N ratios (signal to noise) states the surface roughness
in the graphical point of view.
The residual plots are helps in resulting the graphical values, that helps in finding the easy solution for roughness
average (Ra). the testing investigation highlights that the anova and F-test revealed that the feed is dominant parameter
followed by speed for surface roughness [ 6 ].
The surface roughness resulting in tool geometry, cutting conditions etc & the optimal valve of surface roughness
occurs in highest speed [ 7 ].
Author (Ranganathan) states the RSM model of cnc turning of aluminium work material by CVD. Coated tool were the
results states that feed is the major factor were surface roughness increased by the improving of feed. Mini tab software
helps in computing RSM model and surface and contour plots helps in predicting the significant factor.[ 8 ].
Using CNC lathe analyzing of surface finishing of copper work piece material with coated ceramic tool. Using
parameters such as speed, feed, doc. The process uses the taguchi based approach for resulting .the process includes MINI
TAB 15. Software. Through this study the residual plots of surface roughness states that least depth of cut value was
significant effect on the process [ 9 ].
Surface finishing was an wide index of product quality in turning. therefore there is a need to develop a
methodology to determine the optimal machining parameters such as speed, feed and depth of cut for obtaining a desired
surface roughness and product quality. Author states the best optimal value for the set of parameters by RSM by using
Central composite design model in design experts software.[ 10 ]
From the literature survey, it is evident that no work has been reported on AISI 8620 alloy steel work with
combination of CVD coated tool with machining of CNC machine. Also little work has been reported on RSM method on
various machining operations. Hence the experimentation is done on above said combination of work piece and tool and
optimization by response surface methodology .
III. EXPERIMENTAL DETAILS
WORK PIECE MATERIAL
The AISI 8620 alloy steel work piece material was selected for investigation. The work piece material is a cylindrical rod
with dimensions of 30mm x 70mm was taken and it was machined in CNC turning center. The chemical composition of the
AISI 8620 alloy steel is as follows:
Elements C Si Mn Cr P S Ni M Al B
% 2.179 0.511 0.511 12.634 0.027 0.021 0.050 0.178 0.042 0.065
Table 1: Chemical Composition Of AISI 8620 alloy Steel
Fig 1 work material (AISI 8620 alloy steel)
3. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
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MACHINING PROCESS
The Turning Operation was performed on MICROMATIC (ACE-Designers) model CNC lathe (fig 2).
Fig 2 MICROMATIC (ACE-Designers) model CNC lathe
MEASUREMENT OF SURFACE ROUGHNESS
A surface profile measurement is made with a profilometer that but more generally can be contact (typically a diamond
stylus) or optical (e.g. a white light interferometer). The roughness average, Ra was measured in perpendicular direction to
the cutting direction using a Surface Roughness tester. In this investigation the Surface roughness values are obtained from
SV-C4500 surface roughness measuring instrument for each run.
Fig 3 SV- C4500 Surface roughness measuring device.
IV. DESIGN OF EXPERIMENTS
RESPONSE SURFACE DESIGN
An important face of RSM is to design a plan of experiments after identification of problem several designs such as
fractional factorial design, full factorial design central composite design, boxbehnken, D optimal,
V optimal design, A optimal designs and G optimal designs are available in the literature. Each design is useful for a
particular practical problem depending on the user requirements and user constraints. Response surface methods are used to
examine the relationship between a response variable and a set of experimental variables of factors. These methods are often
employed that optimize the responses. By conducting experiments and applying regression analysis, a model of response to
some independent variables can be obtained. In RSM it is possible to represent independent process parameter in
quantitative form. In This work 3- level factorial design used to conduct experiments. And analysis is carried out by means
of Response surface methodology in MINITAB 17 software.
In design optimization using RSM, the first task is to determine the optimization model, such as the identification
of the interested system measures and the selection of the factors that influence the system measures significantly. To do
this, an understanding of the physical meaning of the problem and some experience are both useful. After this, the important
issues are the design of experiments and to improve the fitting accuracy of the response surface models. DOE techniques are
employed before, during, and after the regression analysis to evaluate the accuracy of the model.RSM also quantifies
relationships among one or more measured responses and the vital input factors.
RSM can be used in the following ways :
1) To determine the factor levels that will simultaneously satisfy a set of desired specifications,
2) To determine the optimum combination of factors that yields a desired response and describes the response near the
optimum,
3) To determine how a specific response is affected by changes in the level of the factors over the Specified levels of
interest,
4) To achieve a quantitative understanding of the system behavior over the region tested,
5) To predict product properties throughout the region, even for a factor combinations not actually run,
6) To find the conditions necessary for process stability (insensitive spot).
3-LEVEL FACTORIAL DESIGN
In Design-Expert 7.1, the proposed designs was located under the Miscellaneous design option. Full factorial 3-level
designs are available for up to 4 factors. The number of experiments will be 3^k plus some replicates of the center point.
Since there are only 3 levels for each factor, the appropriate model is the quadratic model. For more than 2 factors these
4. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
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designs force you to run many more experiments than are needed to estimate the coefficients in a quadratic model. A Box-
Behnken design also requires only three-levels, and is a more efficient alternative to the full three-level factorial.
Table 2 : Parameters and their levels for experiment
METHODOLOGY
After identification of significant process control variables the measurement of responses were found by the
experiments. The corresponding data were analyzed in Response surface methodology in MINITAB V17 software. Results
and regression equation were calculated and corresponding plots are generated. In otherwise among the several process
variables involved in finished turning operation the significant variables found out based on the pilot experiments and the
literature survey are considered in the proposed work as the inclusion of in significant variables excessively increases the
computational complexity of the models. In view of the costly process and the material design of experiments are used
which reduces the number of experiments needed to carry out the analysis with MINITAB 17 software for accuracy and
precession.
Table 3: Experimental observation Details
V. Result And Discussion
Using Minitab 17 software, the response surface method were carried out. The Response surface methods which is used
to examine the relationship between a response variable and a set of experimental variables of factors. These methods are
often employed that optimize the responses. For the proposed method ANOVA (Analysis of variance) are calculated and
tabulated. Also respective regression equation was found for the design. The smaller the better phenomenon is chosen for
surface roughness because surface quality will be high when the surface roughness values will be small. Using response
optimizer the above criteria were satisfied.
levels
Process parameter units
-1 0 1
speed m/min 50 75 100
feed mm/rev 0.02 0.04 0.06
Depth of cut mm 0.1 0.2 0.3
S.no Run oder Speed Feed Depth of cut Ra
1 1 50 0.02 0.1 0.456
2 10 75 0.02 0.1 0.500
3 19 100 0.02 0.1 0.56
4 2 50 0.02 0.2 0.54
5 11 75 0.02 0.2 0.53
6 20 100 0.02 0.2 1.01
7 3 50 0.02 0.3 0.594
8 12 75 0.02 0.3 0.74
9 21 100 0.02 0.3 1.32
10 4 50 0.04 0.1 0.531
11 13 75 0.04 0.1 0.340
12 22 100 0.04 0.1 0.590
13 5 50 0.04 0.2 0.680
14 14 75 0.04 0.2 0.517
15 23 100 0.04 0.2 0.651
16 6 50 0.04 0.3 0.730
17 15 75 0.04 0.3 0.500
18 24 100 0.04 0.3 0.860
19 7 50 0.06 0.1 0.711
20 16 75 0.06 0.1 0.400
21 25 100 0.06 0.1 0.340
22 8 50 0.06 0.2 0.464
23 17 75 0.06 0.2 0.450
24 26 100 0.06 0.2 0.476
25 9 50 0.06 0.3 0.610
26 18 75 0.06 0.3 0.800
27 27 100 0.06 0.3 0.624
5. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
DOI: 10.9790/1684-1314109117 www.iosrjournals.org 113 | Page
ANALYSIS OF VARIANCE (ANOVA)
Source DF Adj SS Adj MS F- value p-value
Model 9 0.91999 0.102221 8.08 0.001*
Linear 3 0.47815 0.159384 12.60 0.001
Speed 1 0.06783 0.067835 5.36 0.033
Feed 1 0.10351 0.103513 8.18 0.011
Depth of cut 1 0.30681 0.306806 24.25 0.001
square 3 0.09656 0.032186 2.54 0.090
Speed*Speed 1 0.08825 0.088250 6.98 0.017
Feed*Feed 1 0.00186 0.001861 0.15 0.706
Depth of cut *Depth of cut 1 0.00645 0.006446 0.51 0.285
2-Way Interaction 3 0.34528 0.115093 9.10 0.001
Speed *Feed 1 0.22277 0.222769 17.61 0.001
Speed *Depth of cut 1 0.09684 0.096840 7.66 0.013
Feed *Depth of cut 1 0.02567 0.025669 2.03 0.172
Error 17 0.21505 0.012650
Total 26 1.000
Table 4: ANOVA TABLE
An ANOVA table is commonly used to summarize the tests performed. it is evident that speed ,feed and doc are significant
at 95% confidence level thus affects mean value and varaiation around the mean value of the Ra.the feed is the most
significant factor in the anova for and thus affects the mean value of Ra followed by Depth of cut.
MODEL SUMMARY
Table 5: Model Summary
RESIDUAL AND INTERACTION PLOT FOR Ra
Fig 4: Residual Plots for Ra
S R-sq R-sq(adj) R-sq(pred)
0.112473 81.05% 71.02% 52.44%
6. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
DOI: 10.9790/1684-1314109117 www.iosrjournals.org 114 | Page
Fig 5 : Interaction Plots for Ra
The normal probability plots of the residuals for Ra reveals that the residuals generally fall on a straight line implying that
errors are distributed normally also fig 4 showing residuals versus order for Ra reveals that they have no obvious pattern and
unusual structure were found. This implies that the models proposed are adequate and there is no reason to suspect any
violation of the independent or constant varaiation assumption. Fig 5 shows the interaction plots of Ra for three parameters
SURFACE AND CONTOUR PLOT FOR Ra
Speed x1 100
Feed x2 0.06
Depth of cut x3 0.1
Hold Values
75
0.4
5.0
60.
505
75 .020
100
0.04
.020
0.0 6
60.
0.7
ar
2xdeeF
1xdeepS
57
4.0
5.0
6.0
550
57 1.0
100
0.2
1.0
.30
6.0
0.7
ar
3xtucfohtpeD
1xdeepS
40.0
05.0
57.0
00.1
0.00. 2
40.0 1.0
60.0
2.0
1.0
0.3
00.1
1 25.
ar
3xtucfohtpeD
2xdeeF
arfostolPecafruS
Fig 6: 3D surface plot for Ra
7. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
DOI: 10.9790/1684-1314109117 www.iosrjournals.org 115 | Page
Speed x1 100
Feed x2 0.06
Depth of cut x3 0.1
Hold Values
Feed x2*Speed x1
1007550
0.056
0.048
0.040
0.032
0.024
Depth of cut x3*Speed x1
1007550
0.28
0.24
0.20
0.16
0.12
Depth of cut x3*Feed x2
0.0560.0480.0400.0320.024
0.28
0.24
0.20
0.16
0.12
>
–
–
–
–
< 0.4
0.4 0.6
0.6 0.8
0.8 1.0
1.0 1.2
1.2
ra
Contour Plots of ra
Fig 7 : contour plot for Ra.
The entire 3d surface graph for surface roughness has curvilinear profile in accordance to the model fitted.fig 6 shows 3d
surface plot graph of the effect of speed, feed and depth of cut on the surface roughness. it has a curve linear shape according
to the model fitted. The contour plot and surface plot are shown in the fig 7 represents the surface roughness increase with
increasing feed followed by depth of cut.
PREDICTION OF OPTIMAL SOLUTION BY RESPONSE OPTIMIZER
Fig 8 : Response Optimizer Plot.
The influence of each control factor can be clearly presented with response graphs (Fig 8). These figures reveal the level to
be chosen for the ideal turning parameters. response optimization helps in identifying the combination of input variable
settings that jointly optimize a single response or a set of responses. Joint optimization must satisfy the requirements for all
the responses in the set, which is measured by the composite desirability. Minitab calculates an optimal solution and draws a
plot. The optimal solution serves as the starting point for the plot. This optimization plot allows to interactively change the
input variable settings to perform sensitivity analyses and possibly improve upon the initial solution.
8. Parametric optimization during CNC turning of AISI 8620 alloy steel using RSM
DOI: 10.9790/1684-1314109117 www.iosrjournals.org 116 | Page
For surface roughness (Ra). The optimal parameter setting combination for AISI 8620 alloy steel is shown in table 6
Control factors Speed Feed Doc
Surface roughness
(Ra) µm
100 0.06 0.1
Table 6. Optimized table obtained for AISI 8620 alloy steel
ANALYSIS OF REGRESSION FOR PREDICTION OF SURFACE ROUGHNESS (SR):
Regression equation is the best fit equation between the input factors output response. That is to say the relationship
between surface roughness and machining independent variables In order to facilitate the determination of constants and
parameters the mathematical model of Experiment for the response (Surface Roughness) are shown below.
Ra=1.033-0.0229speed+17.7feed-1.78Doc+0.000194speed*speed+44Feed*feed+3.28Doc
0.2725Speed*feed+0.0359speed*doc-23.1 Feed*doc.
VI. Conclusion
1. The ANOVA shows that the percentage contribution of Feed is the dominant parameter followed by
depth of cut for surface roughness.
2. From table of model summary R2
for Ra is found to be 0.8105. This shows that the second-order model can explain
the variation in Surface roughness up to the extent of 81.05%. similarly, adjusted R2
is found as 71.02% Predicted
R2
value is 52.44%.
3. The surface and contour plots reveals the parameter increase in feed increases the surface roughness.
Hence The input parameter Feed, has a major effect on surface roughness
4. The optimized parameters for minimum surface roughness are speed (100 rpm), Feed (0.06 mm/rev),
Depth of cut (0.1mm).
5. The optimized minimum surface roughness is 0.340µm.
The present work states the one type of CVD coated tool which is analyzed by the RSM method. Future plan is
there to accomplish the comparison of different style of inserts and all by adding the parameter level more than
present work.
References
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*, P.Venkataramaiah Ḃ
And G.Krishnaiah C
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