Introduction to Engineering Statistics and Six Sigma (eBook, PDF)
Statistical Quality Control and Design of Experiments and Systems
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It is said that six sigma methods are vital to survive, let alone thrive, in today's competitive markets, but what are these methods and how or when do we use them?This book describes related methods and details case studies showing how they've been applied in engineering and business to save millions of dollars. These methods include: design of experiments (DOE) and statistical process control (SPC) charting approaches, failure mode and effects analysis (FMEA), formal optimization, genetic algorithms, gauge reproducibility and repeatability (R & R), linear regression, neural nets, simulation,...
It is said that six sigma methods are vital to survive, let alone thrive, in today's competitive markets, but what are these methods and how or when do we use them?
This book describes related methods and details case studies showing how they've been applied in engineering and business to save millions of dollars. These methods include: design of experiments (DOE) and statistical process control (SPC) charting approaches, failure mode and effects analysis (FMEA), formal optimization, genetic algorithms, gauge reproducibility and repeatability (R & R), linear regression, neural nets, simulation, quality function deployment (QFD) and Taguchi methods. The aim is to help the reader determine which methods to use in which scenario and to predict the effectiveness of these methods.
Illustrative examples support all methods presented and case-study based exercises help the reader associate techniques with industrial problems. A glossary of acronyms is provided in addition to solutions to homework and practice exams.
This book describes related methods and details case studies showing how they've been applied in engineering and business to save millions of dollars. These methods include: design of experiments (DOE) and statistical process control (SPC) charting approaches, failure mode and effects analysis (FMEA), formal optimization, genetic algorithms, gauge reproducibility and repeatability (R & R), linear regression, neural nets, simulation, quality function deployment (QFD) and Taguchi methods. The aim is to help the reader determine which methods to use in which scenario and to predict the effectiveness of these methods.
Illustrative examples support all methods presented and case-study based exercises help the reader associate techniques with industrial problems. A glossary of acronyms is provided in addition to solutions to homework and practice exams.
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