This document provides information on conducting Measurement Systems Analysis (MSA) as required by ISO TS 16949. It describes the objectives of MSA which are to understand how to audit MSA requirements and complete all types of MSA studies. The key aspects of MSA covered include gauge repeatability and reproducibility studies, bias, linearity, and stability. Detailed methods are provided for variable gauge short-term and long-term studies as well as attributes gauge studies. The importance of interpreting results and determining if measurement systems are acceptable is also emphasized.
Quality is defined as customers' perception of how well a product or service meets their expectations. There are three types of quality: quality of design, quality of performance, and quality of conformance. Statistical quality control uses statistical techniques to control, improve, and maintain quality. Control charts are used to determine if a process is in or out of control by monitoring for random or assignable variation. Process capability indices like Cp and Cpk compare process variability to specification limits to determine if a process is capable of meeting specifications.
Statistical process control ppt @ doms Babasab Patil
This document provides an overview of statistical process control (SPC). It discusses the basics of SPC including control charts for attributes and variables. Control charts monitor a production process to detect issues. Attribute charts like p-charts and c-charts monitor defects, while variable charts like x-bar and R-charts monitor measured values. The document also discusses applying SPC to services and provides examples of constructing and interpreting control charts using Excel and Minitab. Process capability and identifying special causes of variation from control chart patterns are also covered.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Measurement System ...J. García - Verdugo
This document discusses measurement system analysis for continuous measurements. It introduces the Gage R&R study as a tool to assess measurement systems. Key indices for evaluating measurement systems are the Percentage of Tolerance (P/T) and Percentage of Range and Repeatability (%R&R). P/T assesses how much of the specification tolerance is used by measurement error while %R&R evaluates measurement error relative to total process variation. The document provides guidelines for properly conducting a Gage R&R study and interpreting its results.
Measurement risk and the impact on your processes Transcat
Howard Zion, Transcat's Director of Service Application Engineering, discusses how measurements are incorrectly influencing the acceptance decision on your products. This webinar will teach you:
What is Measurement Risk?
Where does risk creep into your process?
Where does risk creep into the calibration process?
Calibration Results: Impact on your process
This chapter outline describes statistical quality control tools including control charts. Control charts are used to detect assignable causes of variation and improve process stability. The document defines key control chart terminology like rational subgroups and patterns. It provides examples of variable control charts like X-bar and R charts and attribute charts like P and U charts. Process capability is also discussed along with ratios to quantify how close a process operates to specification limits. The goal of these statistical quality control methods is reduction of process variability through detection and elimination of assignable causes.
This document provides an overview of using Minitab software to perform six sigma analysis. It discusses key tools in Minitab like cause and effect diagrams, control charts, pareto charts, and various statistical tests. Examples are given to demonstrate how to use these tools to analyze data from various industrial processes and identify sources of variation or defects. Control charts can be used to determine if a process is in or out of control, while other tools like pareto charts and regression can help identify the most influential factors. Minitab is a useful statistical package that can play an important role in six sigma improvement projects.
Fluke Corporation: Gas Custody Transfer CalibrationTranscat
This document discusses calibration considerations for orifice plate based gas flow computers used in custody transfer applications. Differential pressure meters are commonly used despite ultrasonic meters having advantages. Calibration is important due to the financial impact of errors in custody transfer measurements. Test equipment selection depends on the type of inputs to the flow computer. Procedures involve verifying and adjusting pressure, temperature, and current inputs through multiple measurement points. Safety, isolation, and proper setup are important to get accurate results and verify calibrations.
Quality is defined as customers' perception of how well a product or service meets their expectations. There are three types of quality: quality of design, quality of performance, and quality of conformance. Statistical quality control uses statistical techniques to control, improve, and maintain quality. Control charts are used to determine if a process is in or out of control by monitoring for random or assignable variation. Process capability indices like Cp and Cpk compare process variability to specification limits to determine if a process is capable of meeting specifications.
Statistical process control ppt @ doms Babasab Patil
This document provides an overview of statistical process control (SPC). It discusses the basics of SPC including control charts for attributes and variables. Control charts monitor a production process to detect issues. Attribute charts like p-charts and c-charts monitor defects, while variable charts like x-bar and R-charts monitor measured values. The document also discusses applying SPC to services and provides examples of constructing and interpreting control charts using Excel and Minitab. Process capability and identifying special causes of variation from control chart patterns are also covered.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Measurement System ...J. García - Verdugo
This document discusses measurement system analysis for continuous measurements. It introduces the Gage R&R study as a tool to assess measurement systems. Key indices for evaluating measurement systems are the Percentage of Tolerance (P/T) and Percentage of Range and Repeatability (%R&R). P/T assesses how much of the specification tolerance is used by measurement error while %R&R evaluates measurement error relative to total process variation. The document provides guidelines for properly conducting a Gage R&R study and interpreting its results.
Measurement risk and the impact on your processes Transcat
Howard Zion, Transcat's Director of Service Application Engineering, discusses how measurements are incorrectly influencing the acceptance decision on your products. This webinar will teach you:
What is Measurement Risk?
Where does risk creep into your process?
Where does risk creep into the calibration process?
Calibration Results: Impact on your process
This chapter outline describes statistical quality control tools including control charts. Control charts are used to detect assignable causes of variation and improve process stability. The document defines key control chart terminology like rational subgroups and patterns. It provides examples of variable control charts like X-bar and R charts and attribute charts like P and U charts. Process capability is also discussed along with ratios to quantify how close a process operates to specification limits. The goal of these statistical quality control methods is reduction of process variability through detection and elimination of assignable causes.
This document provides an overview of using Minitab software to perform six sigma analysis. It discusses key tools in Minitab like cause and effect diagrams, control charts, pareto charts, and various statistical tests. Examples are given to demonstrate how to use these tools to analyze data from various industrial processes and identify sources of variation or defects. Control charts can be used to determine if a process is in or out of control, while other tools like pareto charts and regression can help identify the most influential factors. Minitab is a useful statistical package that can play an important role in six sigma improvement projects.
Fluke Corporation: Gas Custody Transfer CalibrationTranscat
This document discusses calibration considerations for orifice plate based gas flow computers used in custody transfer applications. Differential pressure meters are commonly used despite ultrasonic meters having advantages. Calibration is important due to the financial impact of errors in custody transfer measurements. Test equipment selection depends on the type of inputs to the flow computer. Procedures involve verifying and adjusting pressure, temperature, and current inputs through multiple measurement points. Safety, isolation, and proper setup are important to get accurate results and verify calibrations.
To gain a basic understanding of the principles of PID Loop Optimisation.
To understand why “Loop Tuning” is often not the solution to achieving (or restoring) stability in a process control loop.
To understand how PROFIBUS PA instrumentation can assist in Loop Optimisation.
1. The document discusses statistical quality control (SQC) methods including statistical process control (SPC), descriptive statistics, acceptance sampling, control charts, process capability analysis, and six sigma.
2. SPC uses control charts to monitor quality characteristics and identify sources of variation. Descriptive statistics are used to describe data distributions and central tendencies.
3. Acceptance sampling randomly inspects batches to determine acceptance or rejection. Control charts like X-bar, P, and C charts help monitor different quality characteristics.
4. Process capability analysis compares process variation to specification limits using metrics like Cp and Cpk. Six sigma aims for very low defect levels.
Time series forecasting involves analyzing sequential data measured over time. A time series can be univariate (containing a single variable) or multivariate (containing multiple variables). It can also be continuous or discrete. Key components of time series include trends, cyclical variations, seasonal variations, and irregular variations. Time series analysis involves fitting a model to the data. Stationarity, where the statistical properties do not depend on time, is required for forecasting. Common forecasting models include ARMA, ARIMA, and SARIMA stochastic models as well as artificial neural networks and support vector machines. Each approach has strengths for modeling nonlinear relationships and generalizing to make predictions.
ARCH/GARCH model.ARCH/GARCH is a method to measure the volatility of the series, to model the noise term of ARIMA model. ARCH/GARCH incorporates new information and analyze the series based on the conditional variance where users can forecast future values with updated information. Here we used ARIMA-ARCH model to forecast moments. And forecast error 0.9%
This document discusses total quality management and continuous quality improvement. It covers quality planning, quality control, and quality improvement. Quality planning involves understanding customer needs. Quality control includes inspection, process control, and correction. Quality improvement aims to establish infrastructure. Common quality control tools are discussed like check sheets, Pareto charts, why-why diagrams, flowcharts, and control charts. Problem solving uses the plan-do-study-act cycle. Quality circles link these approaches.
Control Loop Foundation for Batch and Continuous ControlJim Cahill
Greg McMillan is a retired automation expert who has written many books on process modeling and control. He discusses improving control loop performance for both batch and continuous processes. Some key techniques include properly tuning loops, selecting high-quality valves and flow meters, and applying model-based control strategies like model predictive control.
This document provides an overview of statistical process control (SPC) concepts including control charts, process capability, and applying SPC to services. It discusses control charts for attributes like p-charts and c-charts and control charts for variables like x-bar charts and R-charts. It also covers determining control limits, identifying patterns in control charts, and using Excel for SPC.
This document provides an overview of statistical quality control techniques. It describes the three main categories of statistical quality control as statistical process control, descriptive statistics, and acceptance sampling. Control charts are introduced as a key tool of statistical process control, and the differences between variable and attribute control charts are explained. Process capability, six sigma methodology, and acceptance sampling plans are also overviewed.
This document discusses various empirical techniques for tuning PID controllers, including the Ziegler-Nichols and Cohen-Coon reaction curve methods. The Ziegler-Nichols method determines controller parameters by forcing sustained oscillations and measuring the ultimate gain and period. The Cohen-Coon method analyzes the open-loop step response curve to estimate parameters. Simulation tools can also assist in empirically tuning controllers to achieve desired closed-loop responses.
The document provides an overview of flow meter technologies, with a focus on positive displacement flow meters. It discusses the principles of operation for positive displacement meters, including how they directly measure volume by passing measured volumes of fluid through a measuring chamber. The document also outlines applications for positive displacement meters like batching and dispensing. It introduces the new MX series of positive displacement flow meters from Macnaught, highlighting features like increased temperature and pressure ratings, a new mounting system, and hazardous area approvals.
Sensor Fusion Study - Ch10. Additional topics in kalman filter [Stella Seoyeo...AI Robotics KR
This document discusses additional topics related to Kalman filtering, including verifying filter performance, multiple model estimation, reduced-order filtering, robust filtering, and handling delayed measurements. Specific topics covered include using innovations statistics to verify filters, running multiple filters in parallel with different models, reducing filter order to lower computational costs, making filters more robust to model uncertainties, and modifying filters to incorporate out-of-sequence measurements.
Arima Forecasting - Presentation by Sera Cresta, Nora Alosaimi and Puneet MahanaAmrinder Arora
Arima Forecasting - Presentation by Sera Cresta, Nora Alosaimi and Puneet Mahana. Presentation for CS 6212 final project in GWU during Fall 2015 (Prof. Arora's class)
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Control charts are discussed as a key SPC tool used to monitor processes and identify variations. The concepts of process capability, six sigma quality levels, and acceptance sampling plans are also introduced.
SERENE 2014 Workshop: Paper "Verification and Validation of a Pressure Contro...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 1: Verification and Validation of a Pressure Control Unit for Hydraulic Systems
This document contains 31 multiple choice questions related to control systems and controllers. The questions cover topics such as:
- The relationship between steady-state error, gain, and oscillations for proportional controllers.
- Design criteria for PID controllers including quarter-amplitude decay ratio and Zeigler-Nichols method.
- Phenomena related to switching between manual and automatic modes.
- Definitions of proportional band, proportional gain, reset control, and integral control.
- Applications and effects of P, PI, PD, and PID controllers.
- Characteristics and uses of proportional, integral, and derivative controllers.
The document concludes by providing the solutions to each multiple choice question.
The document contains six sections describing various quality control processes. Section A describes a process to measure glue drying time and calculates control limits for the mean. Section B calculates control limits for the diameter of machine parts. Section C constructs control charts for the processing time of new bank accounts. Section D describes determining control limits for a process with 5% defective units at 99.73% confidence. Section E uses a C-chart to analyze defects per roll of wire. Section F determines if a process is capable based on its mean, standard deviation, and specification limits.
1. Statistical process control (SPC) methods are useful for mass production but not for job shop production where each product is unique. Quality in job shops can be ensured by controlling input quality, process conditions, and output quality checks.
2. The quality control manager should be involved in planning quality, not just policing it. However, the quality control function needs separation from production to allow an independent quality assessment.
3. Specification limits define the acceptable performance range for customers, while control limits are set narrower by the producer to monitor the process and ensure it stays within specifications. Process capability affects whether control limits can keep the process within specifications.
This document provides summaries of several appendices related to PID tuning and control. Appendix A offers a short cut tuning method that can identify process dynamics and tune a controller in about five dead times. It reduces open loop test time by over 80% for processes with large time constants. Appendix B provides a PID checklist to help utilize full PID capabilities and ensure parameters are correctly set. Appendix C derives equations to understand the effects of dynamics and tuning on performance. It provides a guide to change plant dynamics and tuning to achieve objectives.
This document provides an overview of time series analysis techniques including moving average (MA) models, exponential smoothing, and ARMA models. It describes the key components of MA models including the MA(q) notation and theoretical properties. Exponential smoothing is presented as a weighted moving average for smoothing and short-term forecasting. The ARMA model is introduced as combining autoregressive and moving average terms to model a time series.
This document discusses repeatability and reproducibility in measurement systems. Repeatability refers to the variability from measurements taken by the same person on the same item, and depends on the precision of the measurement equipment. Reproducibility refers to variability from measurements taken by different operators on the same item. The document provides examples of using the range-and-average method and analysis of variance (ANOVA) method to quantify repeatability, reproducibility, and overall measurement system variability.
This document discusses the importance of meetings for leaders and how to approach them effectively. It makes three key points:
1. Meetings are the principal opportunity for leadership and where bosses spend most of their time, so they must be occasions for excellence, enthusiasm, and motivation.
2. Meetings should stir imagination, bonding, and engagement in order to avoid being permanently lost opportunities.
3. Meetings are a form of theater for the leader to express values and aspirations and demonstrate their vision for the organization. Beginnings and endings are most important to make an impact.
To gain a basic understanding of the principles of PID Loop Optimisation.
To understand why “Loop Tuning” is often not the solution to achieving (or restoring) stability in a process control loop.
To understand how PROFIBUS PA instrumentation can assist in Loop Optimisation.
1. The document discusses statistical quality control (SQC) methods including statistical process control (SPC), descriptive statistics, acceptance sampling, control charts, process capability analysis, and six sigma.
2. SPC uses control charts to monitor quality characteristics and identify sources of variation. Descriptive statistics are used to describe data distributions and central tendencies.
3. Acceptance sampling randomly inspects batches to determine acceptance or rejection. Control charts like X-bar, P, and C charts help monitor different quality characteristics.
4. Process capability analysis compares process variation to specification limits using metrics like Cp and Cpk. Six sigma aims for very low defect levels.
Time series forecasting involves analyzing sequential data measured over time. A time series can be univariate (containing a single variable) or multivariate (containing multiple variables). It can also be continuous or discrete. Key components of time series include trends, cyclical variations, seasonal variations, and irregular variations. Time series analysis involves fitting a model to the data. Stationarity, where the statistical properties do not depend on time, is required for forecasting. Common forecasting models include ARMA, ARIMA, and SARIMA stochastic models as well as artificial neural networks and support vector machines. Each approach has strengths for modeling nonlinear relationships and generalizing to make predictions.
ARCH/GARCH model.ARCH/GARCH is a method to measure the volatility of the series, to model the noise term of ARIMA model. ARCH/GARCH incorporates new information and analyze the series based on the conditional variance where users can forecast future values with updated information. Here we used ARIMA-ARCH model to forecast moments. And forecast error 0.9%
This document discusses total quality management and continuous quality improvement. It covers quality planning, quality control, and quality improvement. Quality planning involves understanding customer needs. Quality control includes inspection, process control, and correction. Quality improvement aims to establish infrastructure. Common quality control tools are discussed like check sheets, Pareto charts, why-why diagrams, flowcharts, and control charts. Problem solving uses the plan-do-study-act cycle. Quality circles link these approaches.
Control Loop Foundation for Batch and Continuous ControlJim Cahill
Greg McMillan is a retired automation expert who has written many books on process modeling and control. He discusses improving control loop performance for both batch and continuous processes. Some key techniques include properly tuning loops, selecting high-quality valves and flow meters, and applying model-based control strategies like model predictive control.
This document provides an overview of statistical process control (SPC) concepts including control charts, process capability, and applying SPC to services. It discusses control charts for attributes like p-charts and c-charts and control charts for variables like x-bar charts and R-charts. It also covers determining control limits, identifying patterns in control charts, and using Excel for SPC.
This document provides an overview of statistical quality control techniques. It describes the three main categories of statistical quality control as statistical process control, descriptive statistics, and acceptance sampling. Control charts are introduced as a key tool of statistical process control, and the differences between variable and attribute control charts are explained. Process capability, six sigma methodology, and acceptance sampling plans are also overviewed.
This document discusses various empirical techniques for tuning PID controllers, including the Ziegler-Nichols and Cohen-Coon reaction curve methods. The Ziegler-Nichols method determines controller parameters by forcing sustained oscillations and measuring the ultimate gain and period. The Cohen-Coon method analyzes the open-loop step response curve to estimate parameters. Simulation tools can also assist in empirically tuning controllers to achieve desired closed-loop responses.
The document provides an overview of flow meter technologies, with a focus on positive displacement flow meters. It discusses the principles of operation for positive displacement meters, including how they directly measure volume by passing measured volumes of fluid through a measuring chamber. The document also outlines applications for positive displacement meters like batching and dispensing. It introduces the new MX series of positive displacement flow meters from Macnaught, highlighting features like increased temperature and pressure ratings, a new mounting system, and hazardous area approvals.
Sensor Fusion Study - Ch10. Additional topics in kalman filter [Stella Seoyeo...AI Robotics KR
This document discusses additional topics related to Kalman filtering, including verifying filter performance, multiple model estimation, reduced-order filtering, robust filtering, and handling delayed measurements. Specific topics covered include using innovations statistics to verify filters, running multiple filters in parallel with different models, reducing filter order to lower computational costs, making filters more robust to model uncertainties, and modifying filters to incorporate out-of-sequence measurements.
Arima Forecasting - Presentation by Sera Cresta, Nora Alosaimi and Puneet MahanaAmrinder Arora
Arima Forecasting - Presentation by Sera Cresta, Nora Alosaimi and Puneet Mahana. Presentation for CS 6212 final project in GWU during Fall 2015 (Prof. Arora's class)
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Control charts are discussed as a key SPC tool used to monitor processes and identify variations. The concepts of process capability, six sigma quality levels, and acceptance sampling plans are also introduced.
SERENE 2014 Workshop: Paper "Verification and Validation of a Pressure Contro...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 1: Verification and Validation of a Pressure Control Unit for Hydraulic Systems
This document contains 31 multiple choice questions related to control systems and controllers. The questions cover topics such as:
- The relationship between steady-state error, gain, and oscillations for proportional controllers.
- Design criteria for PID controllers including quarter-amplitude decay ratio and Zeigler-Nichols method.
- Phenomena related to switching between manual and automatic modes.
- Definitions of proportional band, proportional gain, reset control, and integral control.
- Applications and effects of P, PI, PD, and PID controllers.
- Characteristics and uses of proportional, integral, and derivative controllers.
The document concludes by providing the solutions to each multiple choice question.
The document contains six sections describing various quality control processes. Section A describes a process to measure glue drying time and calculates control limits for the mean. Section B calculates control limits for the diameter of machine parts. Section C constructs control charts for the processing time of new bank accounts. Section D describes determining control limits for a process with 5% defective units at 99.73% confidence. Section E uses a C-chart to analyze defects per roll of wire. Section F determines if a process is capable based on its mean, standard deviation, and specification limits.
1. Statistical process control (SPC) methods are useful for mass production but not for job shop production where each product is unique. Quality in job shops can be ensured by controlling input quality, process conditions, and output quality checks.
2. The quality control manager should be involved in planning quality, not just policing it. However, the quality control function needs separation from production to allow an independent quality assessment.
3. Specification limits define the acceptable performance range for customers, while control limits are set narrower by the producer to monitor the process and ensure it stays within specifications. Process capability affects whether control limits can keep the process within specifications.
This document provides summaries of several appendices related to PID tuning and control. Appendix A offers a short cut tuning method that can identify process dynamics and tune a controller in about five dead times. It reduces open loop test time by over 80% for processes with large time constants. Appendix B provides a PID checklist to help utilize full PID capabilities and ensure parameters are correctly set. Appendix C derives equations to understand the effects of dynamics and tuning on performance. It provides a guide to change plant dynamics and tuning to achieve objectives.
This document provides an overview of time series analysis techniques including moving average (MA) models, exponential smoothing, and ARMA models. It describes the key components of MA models including the MA(q) notation and theoretical properties. Exponential smoothing is presented as a weighted moving average for smoothing and short-term forecasting. The ARMA model is introduced as combining autoregressive and moving average terms to model a time series.
This document discusses repeatability and reproducibility in measurement systems. Repeatability refers to the variability from measurements taken by the same person on the same item, and depends on the precision of the measurement equipment. Reproducibility refers to variability from measurements taken by different operators on the same item. The document provides examples of using the range-and-average method and analysis of variance (ANOVA) method to quantify repeatability, reproducibility, and overall measurement system variability.
This document discusses the importance of meetings for leaders and how to approach them effectively. It makes three key points:
1. Meetings are the principal opportunity for leadership and where bosses spend most of their time, so they must be occasions for excellence, enthusiasm, and motivation.
2. Meetings should stir imagination, bonding, and engagement in order to avoid being permanently lost opportunities.
3. Meetings are a form of theater for the leader to express values and aspirations and demonstrate their vision for the organization. Beginnings and endings are most important to make an impact.
This document provides an overview of statistical process control (SPC). It discusses the objectives of an SPC course, which are to learn how to audit SPC and understand variables and attributes control charts. It then defines SPC and the types of variation it can detect. The document outlines the different types of SPC charts, including variables charts (x-bar and R, x-bar and s), attributes charts (p, np, c, u), and when each is best applied. It also discusses process capability studies and calculating Ppk and Cpk values.
This document outlines the curriculum for VCE IT 2014, including 4 units covering topics like data analysis, software development, networks, and careers involving information and communication technologies. Each unit includes 3 areas of study. Area of Study 1 focuses on processes like selecting and analyzing data to create visualizations. Area of Study 2 involves programming, databases, and career pathways. Area of Study 3 addresses collaboration, problem-solving methodologies, and contemporary ICT issues. Students apply a problem-solving methodology across stages of analysis, design, development and evaluation in their projects. They use a variety of software tools to complete solutions meeting specific purposes.
The document contains a collection of quotes and sources related to leadership. It discusses topics such as setting direction, developing people, communication skills, vision, emotional intelligence, leadership styles and approaches, and characteristics of effective leaders.
The document discusses various aspects of quality including:
1) Most errors go unreported due to fear of blame or feeling errors are insignificant.
2) Quality is important for customer retention and satisfaction. Poor quality leads to high costs from inspection, prevention, and failures.
3) Continuous improvement is needed to develop strategies, take action, and evaluate performance to meet customer needs.
The document discusses six persuasive strategies that can be used to strengthen arguments:
1) Citing facts, statistics, and reliable research to support claims.
2) Referencing the opinions of important people or experts.
3) Using emotive language to appeal to emotions.
4) Establishing credibility and trust with the audience.
5) Calling the audience to immediate action.
6) Providing specific examples to illustrate arguments.
This document provides a guide for writing Shakespearean sonnets. It begins with an introductory quote about the importance of loving the sonnet form. It then presents Shakespeare's Sonnet 73 as an example, analyzing its structure including the four line quatrains that introduce the theme, continue the metaphor, and sometimes include a "volta" or twist, as well as the two line couplet that provides the conclusion. The document guides the reader through brainstorming a topic, choosing a metaphor, and composing their own sonnet line-by-line while maintaining the proper rhyme scheme and iambic pentameter. It concludes by encouraging revision to perfect the newly written sonnet.
Values are ideals that guide personal conduct and involvement in career, helping distinguish right from wrong and lead a meaningful life. Personal values like honesty define individuals, while cultural values sustain community connections. Beliefs are convictions held without proof. Worldviews are organized sets of ideas explaining social and physical worlds. Rituals are formal symbolic actions performed regularly. Hierarchies rank and organize elements in a system with each subordinate to another. Ideologies are sets of doctrines and symbols adopted by social movements acting in extreme ways due to their beliefs.
This document outlines the post-audit activities involved in reporting an audit. It discusses reviewing the previous day's activities, recording audit findings, grading any non-conformances, holding a closing meeting, and establishing corrective actions and follow-up procedures. Specifically, it covers drafting an audit report, documenting any non-conformities, conducting a closing meeting to present results, and establishing a corrective action plan to address any issues found. The overall goal is to formally communicate audit results and ensure any necessary improvements are implemented.
This document defines key concepts in measurement system analysis including accuracy, precision, stability, bias, repeatability, and reproducibility. It provides guidelines for conducting a measurement system analysis, including determining the number of appraisers and parts to measure, ensuring the measurement procedure is documented and followed, and analyzing the results in terms of stability, bias, and gauge R&R to determine if the measurement system is capable and can be used for decision making. The goal is to qualify measurement systems and identify opportunities for improvement.
This document provides guidance on completing a Process Failure Mode and Effects Analysis (Process FMEA). It outlines the 25 steps to complete a Process FMEA, including identifying the process, potential failure modes and causes, severity, occurrence, detection, and risk priority number. It also includes examples for each step. The overall aim of a Process FMEA is to proactively identify potential failures in a manufacturing process to take actions to reduce risks and ensure process capability.
The document discusses the Production Part Approval Process (PPAP), which defines requirements for approving production parts. It covers topics like when PPAP submission is required, the requirements for part approval, submission levels and retention requirements. PPAP applies to internal and external suppliers providing bulk materials, production materials, parts or service parts. The document provides details on each of the PPAP requirements and guidelines for suppliers to follow.
This document discusses measurement systems analysis (MSA), which determines the error in a measuring device compared to the tolerance. An MSA consists of gauge repeatability, reproducibility, bias, linearity, and stability. It provides examples of variable short and long gauge R&R studies. A short study has 2 operators measure 5 parts once each to calculate the average range and percentage gauge R&R. A long study has 2-3 operators measure 10 parts 2-3 times each to determine ranges, averages, and further analyze the measurement system. The goal of an MSA is to ensure measuring devices are accurate and do not exceed acceptable error levels.
1. The document discusses measurement systems analysis and different techniques for evaluating variable and attribute measurement systems.
2. Key aspects of measurement systems that can introduce variation are described, including bias, stability, repeatability, and reproducibility.
3. Three techniques are presented for analyzing variable gages: the average-range method, ANOVA method, and gauge R&R study which evaluates repeatability, reproducibility and overall measurement system accuracy.
1. A gauge R&R study involves measuring sample parts with different appraisers using the same gauge to determine measurement system variability.
2. Key steps include selecting appraisers, obtaining a representative sample of 10 parts, measuring the parts in random order over multiple trials while recording the results, and calculating various variations to determine if the measurement system is acceptable.
3. Acceptance criteria for gauge R&R is less than 10% for good systems and 10-30% for systems that may need evaluation depending on the application. The study quantifies repeatability, reproducibility and overall gauge R&R variation.
This document discusses measurement system analysis (MSA), which assesses the quality of measurement systems that generate measurements critical to a facility's operations. MSA applies statistical methods to evaluate six key properties (repeatability, reproducibility, accuracy, bias, linearity, stability) that characterize a measurement system's variation. Conducting an MSA is important when a new measuring device is introduced, to compare measurement devices, or when required by a customer's control plan. The document outlines methods like variable and attribute gauge studies to assess a measurement system, and provides guidance on properly planning and conducting an MSA.
This document discusses measurement system analysis (MSA), which assesses the quality of measurement systems that generate measurements critical to a facility's operations. MSA applies statistical methods to evaluate six key properties (repeatability, reproducibility, accuracy, bias, linearity, stability) that characterize a measurement system's variation. Conducting an MSA is important when a new measuring device is introduced, to compare measurement devices, or when required by a customer's control plan. The document outlines methods like variable and attribute gauge studies to evaluate a measurement system, and provides acceptance criteria to determine if a system is acceptable based on the percentage of variation attributed to the gauge.
This chapter introduces control chart methods for variables. It discusses constructing control charts for variables to monitor process variation and determine process capability. Control charts graphically display variations in quality characteristics between subgroups to identify whether the process is in or out of statistical control. Points outside the control limits or unusual patterns within the limits indicate the presence of assignable causes of variation requiring investigation and process improvement. The objectives of variable control charts are quality improvement, determining process capability, and making production decisions.
This document discusses measurement system analysis (MSA), which is used to evaluate statistical properties of process measurement systems. MSA determines if current measurement systems provide representative, unbiased and minimal variability measurements. The document outlines the MSA process, including preparing for a study, evaluating stability, accuracy, precision, linearity, and repeatability and reproducibility. Accuracy looks at bias while precision considers repeatability and reproducibility. MSA is required for certification and helps identify process variation sources and minimize defects.
The document provides an overview of measurement system analysis (MSA) techniques for both variable and attribute gages. It describes the average-range method and ANOVA method for analyzing variable gages, and the short method, hypothesis test analysis, and long method for attribute gages. Acceptability criteria are outlined for determining if a measurement system is capable of measuring process variation.
The document discusses a case study involving the evaluation of a measurement system for an important quality variable, CTQ1, at W.R. Grace. A measurement systems analysis (MSA) study was conducted involving the four worldwide sites that produce the raw material. The results showed a high %GR&R of 94.3% and P/T ratio of 116%, indicating significant measurement error. When analyzed separately, the sites showed varying levels of measurement capability, with one site having a %GR&R of 38.9%. The MSA study identified opportunities to improve the measurement system and link it back to process improvements.
Process Capability for certificate course for marketing engineers onlineDevendraLokhande
The document discusses fundamentals of process capability including:
1) Achieving process control is key to reducing variation and non-conforming parts to achieve quality and cost targets.
2) Process capability indices like Cp, Cpk measure how well a process performs versus specifications and a value of 1.33 or higher indicates a stable and capable process.
3) Many factors can impact a process's capability including the machine, environment, operator, material and more; conducting process capability studies helps evaluate a process and identify sources of variation.
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What is MSA .
1. Why we Need MSA
2. How to use data.
3.Measurement Error Sources of Variation
• Precision (Resolution, Repeat ability, Reproducibility)
•Accuracy (Bias, Stability, Linearity)
4.What is Gage R&R?
5.Explain MSA Sheet
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Overview
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4. Deployment Using ArgoCD for Edge Devices
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11. What is a Jupyter Notebook?
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12. Jupyter Notebooks with Code Examples
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
2. 2
Course Objectives
• By the end of the course the
participant should be able to;
– Understand how to Audit the
requirements of MSA
• Identify what constitutes a
Measurement Systems Analysis
• Complete and understand all types of
Measurement Systems Analysis
3. 3
Measurement Systems Analysis
ISO TS 16949 requires a Measurement
Systems Analysis be conducted on all
inspection, measuring and test devices
denoted on the Control plan.
4. 4
Measurement Systems Analysis
• What is Measurement Systems Analysis
(MSA)?
– A Measurement System Analysis (MSA)
determines the error in the measuring
device in comparison to the tolerance.
7. 7
Measurement Systems Analysis
• Definition of Gauge Repeatability
– Repeatability
• The ability of a measurement device to repeat its
reading when used several times by the same operator
to measure the same characteristic. Generally this is
referred to as Equipment variation.
– Repeatability = Equipment
Variation
8. 8
Measurement Systems Analysis
• Definition of Gauge Reproducibility
– Reproducibility
• The variation between the averages of the
measurements taken by different operators
using the same measurement device and
measuring the same characteristic. Generally
this is referred to as Operator Variation
Reproducibility = Operator
Variation
9. 9
Measurement Systems Analysis
• There are three types of Gauge R&R
studies
– Variable - Short Method (Range method)
– Variable - Long Method (Average & Range
method)
– Attribute Gauge study
10. 10
Measurement Systems Analysis
Variable - Short Method (Range method)
• Step 1
– Obtain 2 operators and 5 parts for this study
• Step 2
– Each operator is to measure the product once
and record their findings e.g.
Part # Operator A Operator B
1 1.75 1.70
2 1.75 1.65
3 1.65 1.65
4 1.70 1.70
5 1.70 1.65
11. 11
Measurement Systems Analysis
Variable - Short Method (Range method)
• Step 3
– Calculate the range e.g.
Part # Operator A Operator B Range
1 1.75 1.70 0.05
2 1.75 1.65 0.10
3 1.65 1.65 0.00
4 1.70 1.70 0.00
5 1.70 1.65 0.05
12. 12
Measurement Systems Analysis
Variable - Short Method (Range method)
• Step 4
– Determine the average range and calculate the
% Gauge R&R e.g.
Average Range (R) = Ri / 5 = 0.20/ 5 = 0.04∑
The formula to calculate the % R&R is;
%R&R = 100[R&R / Tolerance]
where R&R = 4.33(R) = 4.33(0.04) = 0.1732
assuming that the tolerance = 0.5 units
%R&R = 100[0.1732/ 0.5] = 34.6%
13. 13
Measurement Systems Analysis
Variable - Short Method (Range method)
• Step 5
– Interpret the result
• The acceptance criteria for variable Gauge
R&R studies is that the % R&R is below 30%
• Based on the results obtained the
measurement error is to large and we
therefore must review the measurement
device and techniques employed.
• Measurement device is unsatisfactory
14. 14
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 1
– Record all preliminary information onto the form
e.g.
Part Name: Engine mount Characteristic: Hardness Tolerance: 10 units
Part Number: 92045612 Gauge Name/Number: QA1234 Date: 27 September 1995
Calculated by: John Adamek Operator names: Operator A, Operator B, Operator C
Operator A Operator B Operator C
Sample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range
1
2
3
4
5
6
7
8
9
10
Total
15. Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 2
– Choose 2 or 3 operators and have each operator
measure 10 parts randomly 2 or 3 times - Enter these
results on to the form
Part Name: Engine mount Characteristic: Hardness Tolerance: 10 units
Part Number: 92045612 Gauge Number: QA 1234 Date: 27 September 1995
Calculated by: John Adamek Operator names: Operator A, Operator B, Operator C
Operator A Operator B Operator C
Sample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range
1 75 75 74 76 76 75 76 75 75
2 73 74 76 76 75 75 75 76 76
3 74 75 76 76 75 76 74 76 76
4 74 75 74 75 75 74 74 74 74
5 75 74 74 74 74 76 76 75 74
6 76 75 75 74 74 76 76 76 76
7 74 77 75 76 75 74 75 75 74
8 75 74 75 75 74 74 75 74 76
9 76 77 77 74 76 76 74 74 76
10 77 77 76 76 74 75 75 76 74
Total
17. 17
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 4
– Calculate the average of the averages then determine the
maximum difference and then determine the average of the
average ranges e.g..
Operator A Operator B Operator C
Sample Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range Trial 1 Trial 2 Trial 3 Range
1 75 75 74 1 76 76 75 1 76 75 75 1
2 73 74 76 3 76 75 75 1 75 76 76 1
3 74 75 76 2 76 75 76 1 74 76 76 2
4 74 75 74 1 75 75 74 1 74 74 74 0
5 75 74 74 1 74 74 76 2 76 75 74 2
6 76 75 75 1 74 74 76 2 76 76 76 0
7 74 77 75 3 76 75 74 2 75 75 74 1
8 75 74 75 1 75 74 74 1 75 74 76 2
9 76 77 77 1 74 76 76 2 74 74 76 2
10 77 77 76 1 76 74 75 2 75 76 74 2
Average 74.9 75.3 75.2 1.5 75.2 74.8 75.1 1.5 75.0 75.1 75.1 1.3
X
X
X
X
A
B
C
diff
= ( 74.9 + 75.3 + 75.2) / 3 = 75.1 R = average of the average ranges
= ( 75.2 + 74.8 + 75.1) / 3 = 75.0 R = (1.5 + 1.5 + 1.3) / 3 = 1.43
= ( 75.0 + 75.1 + 75.1) / 3 = 75.1
= Xmax - Xmin = 75.1 - 75.0 = 0.1
18. 18
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 5
– Calculate the UCLR and discard or repeat any readings
with values greater than the UCLR
– Since there are no values greeter than 3.70, continue
* RUCL = R x D4 = 1.43 x 2.58 = 3.70
19. 19
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 6
– Calculate the equipment variation using the following
formula;
Repeatability - Equipment Variation (E.V.)
E.V. = R x K %E.V. = 100 [(E.V.) / (TOL)]
E.V. = 1.43 x 3.05 %E.V. = 100[(4.36) / (10)]
E.V. = 4.36 %E.V. = 43.6 %
1
Trials 2 3
K1 4.56 3.05
20. 20
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 7
– Calculate the Operator Variation using the following
formula;
Reproducibilty - Operator Variation (O.V.)
O.V. = (X x K E.V) N x R)] %O.V. = 100[(O.V.) / (TOL)]
O.V. = (0.1 x 2.7) - [(4.36) / (10 x 3)] %O.V. = 100 [(0.0) / (10)]
O.V. = 0 %O.V. = 0.0%
diff
2
2 2
2
2
) [( / (−
# Operators 2 3
K2 3.65 2.70
21. 21
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 8
– Calculate the Repeatability and Reproducibility using the
following formula;
Repeatability and Reproducibility (R&R)
R&R = (E.V.) + (A.V.) %R&R = 100[(R&R) / (TOL)]
R&R = (4.36) + (0.0) %R&R = 100[(4.36) / (10)]
R&R = 4.36 %R&R = 43.6%
2 2
2 2
22. 22
Measurement Systems Analysis
Variable - Long Method (Range method)
• Step 9
– Interpret the results;
• The gauge %R&R result is greater than 30%
therefore it is unacceptable
• The operator variation is zero and therefore
we can conclude that the error due to
operators is insignificant
• The focus on achieving an acceptable %
Gauge R&R must be on the equipment
23. 23
Measurement Systems Analysis
Attributes Gauge study
• The purpose of any gauge is to detect
nonconforming product. If it is able to detect
nonconforming product it is acceptable,
otherwise, the gauge is unacceptable
• An attributes Gauge study cannot quantify
how “good” the gauge is, but only whether
the gauge is acceptable or not.
24. 24
Measurement Systems Analysis
Attributes Gauge study
• Methodology - Step1
– Select 20 parts. When selecting these
parts ensure that a sample (say 2-6) are
slightly below or above the specification.
• Step 2
– Number them. Preferably in a area that is
not noticeable to the operator, if this is
possible.
25. 25
Measurement Systems Analysis
Attributes Gauge study
• Step 3
– Two operators measure the parts twice.
Ensure the parts are randomised to
prevent bias.
• Step 4
– Record the results
• Step 5
– Assess capability of gauge
26. 26
Measurement Systems Analysis
Attributes Gauge study
• Acceptance criteria
– The gage is acceptable if all
measurement decisions agree i.e. all four
measurements must be the same
Refer to example on next page
27. 27
Measurement Systems Analysis
Attributes Gauge study - Example
Part Name: Rubber Hose I.D. Gauge Name/ID: Go/No-Go Gauge
Part number: 92015623 Date: 3 October 1995
Operator A Operator B
Trial 1 Trial 2 Trial 1 Trial 2
1 G G G G
2 NG NG NG NG
3 NG NG G G
4 G G G G
5 G G G G
6 NG NG NG NG
7 NG G G NG
8 G G G G
9 G G G G
10 G G G G
11 G G G G
12 NG NG NG G
13 G G NG G
14 G G G G
15 G G G G
16 G G G G
17 G G G G
18 G G G G
19 G G G G
20 NG NG NG NG
Result: Acceptable/Unacceptable
Interpretation of results
1. Assume parts 2,3,6,12 and 20 were the
nonconforming parts.
2. The gauge detected part #2 as nonconforming.
3. Although part #3 is also nonconforming Operator
B did not detect this. Therefore the gauge is
unacceptable
4. Part #6 was nonconforming. this was detected by
both operators.
5. Part #7 was acceptable but it was found to be
nonconforming using the gauge by both operators
once.
6.
29. 29
Measurement Systems Analysis
Bias
• Bias is related to accuracy, in that,
if the average measured value is
the same or approximately the
same, there is said to be zero bias
and therefore the gauge being
used is “accurate”.
30. 30
Measurement Systems Analysis
Linearity
• Definition of Linearity
Linearity is defined as the difference
in the bias values of a gauge
through the expected operating
range of the gauge.
33. 33
Measurement Systems Analysis
Determining the amount of Bias with an example
Step 1. Obtain 50 or more measurements
Example: A micrometer is used to measure
the diameter of a pin produced by an
automatic machining process. The true value
of the pin is 1 inch. The resolution of the
micrometer is 0.0050 inches. All of the
readings in table 1 are deviations from the
standard value in 0.0010 increments
Ref: Pyzdeks guide to SPC. Vol 2
35. 35
Measurement Systems Analysis
Determining the amount of Bias with an example
Step 2.
If all of the readings are equal to the true
value, then there is no bias and the
gauge is accurate. If all of the reading
are identical but are not the same as
the true value, then bias exist, to
identify the level of bias and whether it
is acceptable we continue.
36. 36
Measurement Systems Analysis
Determining the amount of Bias with an example
Step 3. Determine the moving ranges
based on the data from table 1.
None 150 50 0 0 100 50 0 150 50
100 50 50 50 50 100 100 50 50 50
100 0 50 50 50 0 100 100 0 50
50 0 100 0 150 50 0 0 50 100
100 150 150 0 100 0 50 0 50 100
0
37. 37
Measurement Systems Analysis
Determining the amount of Bias with an
example
Step 4 Prepare a frequency tally for the moving ranges.
In this example each gauge increment will equal one
cell i.e. Range Frequency Cum. Freq. Cum. Freq %
0 14 14 28.6%
50 18 32 65.3
100 12 44 89.8
150 5 49 100.0
38. 38
Measurement Systems Analysis
Determining the amount of Bias with an example
Step 5. Determine the “cut off” point using
the following equation;
cut off = value of cell that put count above 50% + value of next cell
2.0
cut off = (50 + 100)/2 = 75.0
39. 39
Measurement Systems Analysis
Determining the amount of Bias with an example
• Step 6. Calculate the cut off portion
using the following equation;
17.0
667.98
167.17
3
2
49)x(2
6
1
17
3
2
countx total2
6
1
countremaining
portionoffcut
==
+
+
=
+
+
=
40. 40
Measurement Systems Analysis
Determining the amount of Bias with an example
• Step 7. Determine the Equivalent
Gaussian Deviate (EGD) that
corresponds to the cut off portion.
From Statistical tables, the EGD = 0.95
41. 41
Measurement Systems Analysis
Determining the amount of Bias with an example
• Step 8. Determine the estimated
standard deviation;
8.55
95.02
75
2
ˆ =
×
=
×
=
EGD
cutoff
σ
42. 42
Measurement Systems Analysis
Determining the amount of Bias with an example
• Step 9. Calculate the Control Lines
only.deviationsshowsdata
recordedthesincezero,isvaluetrueThe:Note
4.1678.5530ˆ3
4.1678.5530ˆ3
=×−=+=
−=×−=−=
σ
σ
truevalueUCL
truevalueLCL
bias
bias
44. 44
Measurement Systems Analysis
Determining the amount of Bias with an example
• Step 11. Interpret the chart.
• If all of the points fall within the Control
lines we conclude that the gauge is
accurate and the bias that does exist
has no effect
45. 45
Measurement Systems Analysis
Determining the amount of Bias with an example
• Step 11. Interpret the chart cont..
If points were found outside of the
control lines it could be concluded that
their exists a “special” cause which
may be the source of variation
46. CONTROL CHART INDIVIDUALS & MOVING RANGE (X-MR) – Bias Example
200
UCL
5
0.0
-100
LCL
-200
Moving Range readings
150
100
50
DATE
TIME
1 -50 50 -50 0 100 -50 -100 -100 -100 50 0 -50 0 -100 50 50 0 -50 -50 -50 -50 0 50 -100 0 -100 0 0
Moving range 100 100 50 100 150 50 0 0 150 50 50 50 100 150 0 50 50 0 0 0 50 50 150 100 100 100 0
* For sample sizes of less than seven, there is no lower control limit for ranges.
47. 47
Measurement Systems Analysis
Linearity
• Definition of Linearity
Linearity is defined as the difference
in the bias values of a gauge
through the expected operating
range of the gauge.
48. 48
Measurement Systems Analysis
Example of how to determine Linearity
• Linearity Example:
• An Engineer was interested in
determining the linearity of a
measurement system. The
operating range of the gauge
ranged from 2.0 mm to 10.0 mm.
49. 49
Measurement Systems Analysis
Example of how to determine Linearity
• Step 1
• Select a minimum of 5 parts to be
measured at least 10 times each. For
this example we will select 5 parts and
measure each part 12 times.
• Refer to the following page for data.
55. 55
Measurement Systems Analysis
Example of how to determine Linearity
• Step 4. Determine from the graph
whether a linear relationship exists
between the bias and reference
values. If a “good” linear relationship
exists then the % linearity can be
calculated. If a linear relationship does
not exist, then we must look at other
sources of variation.
56. 56
Measurement Systems Analysis
Example of how to determine Linearity
• Step 5 Calculate the Linearity, using;
( )
( ) ( )
%17.13
variationprocess
Linearity
100%linearity
0.796.000.1317varlinearity
98.0fitofgoodness
7367.0
n
y
b0.1317-
1
.,,where;
2
2
2
2
2
2
22
=
×=
=×=×=∴
=
−×
−
−
==
=
−===
−
−
=
===+=
∑
∑
∑
∑
∑ ∑
∑
∑∑
∑ ∑
∑ ∑
∑
iationprocessslope
n
y
y
n
x
x
n
y
xxy
R
n
x
aslope
x
n
x
n
y
xxy
a
valuerefxslopeabiasyaxby
58. 58
Measurement Systems Analysis
Stability
• To calculate stability use the following steps;
• Step 1.
Obtain a master sample and establish
its reference value(s)
• Step 2
On a periodic basis measure the
master sample five times.
59. 59
Measurement Systems Analysis
Stability
• Step 3
Plot the data on an Xbar and R chart
• Step 4
Calculate the Control limits and
evaluate for any out of control
conditions
• Step 5
If out of control conditions exist, the
measurement system is not stable.
60. 60
Auditing MSA
1. Does the organisation conduct an MSA on all
IMTE denoted in the Control Plan
2. Is the acceptance criteria for Gauge R&R met?
3. Where it is not met, what actions have taken
place?
4. Have these been communicated to the customer?
5. What mechanism is in place to ensure all new
IMTE undergoes a MSA study?
6. Does the organisation conduct attribute Gauge
studies on subjective characteristics?
61. 61
Auditing MSA
7. Verify that the calculations are correct for a
number of Gauge R&R studies
8. Ensure the correct tolerance is used for the
algorithm
9. Does the organisation consider the capability of
the existing IMTE during APQP and any new
IMTE for new parts/projects?