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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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Journal ArticleDOI
Impact of Model Complexity in the Monitoring of Machine Tools Condition Using Volumetric Errors
TL;DR: This paper provides guidelines for selecting machine error models for the SAMBA method when using VEs to monitor the machine tool condition, and shows that the “13” machine error model achieves a better recognition rate of the machine condition.
Book ChapterDOI
Principal Components Based Multivariate Statistical Process Monitoring of Machining Process Using Machine Vision Approach
TL;DR: This chapter presents an innovative approach for monitoring the machining process using integration of three well-established techniques to provide a machine vision based multivariate statistical process monitoring technique (MSPM) with dimensionality reduction using principal component analysis (PCA).
Book ChapterDOI
Evaluating the Capability Index of a Process Integrating Sampling Plan and the Measurement System Number of Distinct Categories NDC
TL;DR: In this paper, the authors proposed a technique for overcoming the inaccuracy of the used measurement system by targeting higher CpAQL values as the same value of can be obtained by several combinations of CpRQL and NDC.
Journal ArticleDOI
Risk Ahead: Actigraphy-Based Early-Warning Signals of Increases in Depressive Symptoms During Antidepressant Discontinuation
Yoram K. Kunkels,Arnoud Smit,Olga Minaeva,Evelien Snippe,Sandip V. George,Arie M. van Roon,Marieke Wichers,Harriëtte Riese +7 more
TL;DR: In this article , the authors tested whether transitions in depression were preceded by increases in actigraphy-based critical-slowing-down-based early warning signals (EWSs; variance, kurtosis, autocorrelation), circadian-rhythm-based indicators, and decreases in mean activity levels.