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Statistical quality control : a modern introduction
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Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Chapter 3: Statistical Methods Useful in Quality Control and Improvement Chapter 4: Inferences about Process QualityAbstract:
Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Part II: Statistical Methods Useful in Quality Control and Improvement Chapter 3: Modeling Process Quality Chapter 4: Inferences about Process Quality Part III: Basic Methods of Statistical Process Control and Capability Analysis Chapter 5: Methods and Philosophy of Statistical Process Control Chapter 6: Control Charts for Variables Chapter 7: Control Charts for Attributes Chapter 8: Process and Measurement System Capability Analysis Part IV: Other Statistical Process-Monitoring and Control Techniques Chapter 9: Cumulative Sum and Exponentially Weighted Moving Average Control Charts Chapter 10: Other Univariate Statistical Process Monitoring and Control Techniques Chapter 11: Multivariate Process Monitoring and Control Chapter 12: Engineering Process Control and SPC Part V: Process Design and Improvement with Designed Experiments Chapter 13: Factorial and Fractional Experiments for Process Design and Improvements Chapter 14: Process Optimization and Designed Experiments Part VI: Acceptance Sampling Chapter 15: Lot-by-Lot Acceptance Sampling for Attributes Chapter 16: Other Acceptance Sampling Techniques Appendixread more
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Application of Bernoulli process-based charts to electronic assembly
TL;DR: The application of protective gel, which is a subprocess of the electronic assembly of the exhaust gas recirculation sensor, is a highly capable process with the fraction of nonconforming units as... as mentioned in this paper.
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An optimal multivariate control chart for correlated Poisson variables using multiple dependent state sampling
TL;DR: In this paper, a control chart to monitor correlated Poisson variables, which uses a statistic based on the linear combination of poisson variables considering the methodology of sampling called Multiple Dependent State (MDSLCP), is proposed.
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Monitoring Process Variability and Root Cause Analysis in Paper Box Production
TL;DR: Monitoring procedure for process variability in multivariate setting based on individual observations which is a combination of Hotelling’s T2 control chart in detecting out of control signal and implementation of Mason, Young and Tracy (MYT) decomposition and structure analysis technique for root cause analysis is introduced.
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A new exponentially weighted moving average chart with an adaptive control scheme for high yield processes—An application in injection molding process
Journal ArticleDOI
A new multivariate control chart for monitoring the quality of a process with the aid of auxiliary information
TL;DR: Control chart is a statistical process control tool to routinely monitor the quality of a process as mentioned in this paper, and auxiliary information has been used to enhance the ability of control chart to detect defects.