Description
Book SynopsisThis textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis.
Industrial Design of Experiments: A Case Study Approach for Design and Process Optimization is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE.
Table of Contents1) Presentations, Statistical Distributions, Quality Tools and Relationship to DoE2) Samples and Populations: Statistical Tests for Significance of Mean and Variability3) Regression, Treatments, DoE Design and Modelling Tools. 4) Two-Level Factorial Design and Analysis Techniques5) Three-Level Factorial Design and Analysis Techniques 6) DoE Error Handling, Significance and Goal Setting 7) DoE Reduction Using Confounding and Professional Experience 8) Multiple Level Factorial Design and DoE Sequencing Techniques9) Variability Reduction Techniques and Combining with Mean Analysis 10) Strategies for Multiple Outcome Analysis and Summary of DoE Case Studies and Techniques
