Variability arises in multistage manufacturing processes (MMPs) from a variety of sources. Variation reduction demands data fusion from product/process design, manufacturing process data, and quality measurement. Statistical process control (SPC), with a focus on quality data alone, only tells half of the story and is a passive method, taking corrective action only after variations occur. Learn how the Stream of Variation (SoV) methodology helps reduce or even eliminate variations throughout the entire MMP in Jianjun Shi's Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes.
The unified methodology outlined in this book addresses all aspects of variation reduction in a MMP, which consists of state space modeling, design analysis and synthesis, engineering-driven statistical methods for process monitoring and root-cause diagnosis, and quick failure recovery and defect prevention. Coverage falls into five sections, beginning with a review of matrix theory and multivariate statistics followed by variation propagation modeling with applications in assembly and machining processes. The third section focuses on diagnosing the sources of variation while the fourth section explains design methods to reduce variability. The final section assembles advanced SoV-related topics and the integration of quality and reliability.
Introducing a powerful and industry-proven method, this book fuses statistical knowledge with the engineering knowledge of product quality and unifies the design of processes and products to achieve more predictable and reliable manufacturing processes.
Table of Contents
What Is Stream of Variation for Multistage Manufacturing Processes?
BASIS OF MATRIX THEORY AND MULTIVARIATE STATISTICS
Basics of Matrix Theory
Basics of Multivariate Statistical Analysis
Statistical Inferences in Mean Vectors and Linear Models
Principle Component Analysis and Factor Analysis
VARIATION PROPAGATION MODELING IN MMP
State Space Modeling for Assembly Processes
State Space Modeling for Machining Processes
Factor Analysis Method for Variability Modeling
VARIATION SOURCE DIAGNOSIS
Diagnosability Analysis for Variation Source Identification
Diagnosis through Variation Pattern Matching
DESIGN FOR VARIATION REDUCTION
Optimal Sensor Placement and Distribution
Design Evaluation and Process Capability Analysis
Optimal Fixture Layout Design
Process-Oriented Tolerance Synthesis
QUALITY AND RELIABILITY INTEGRATION AND ADVANCED TOPICS
Quality and Reliability Chain Modeling and Analysis
Quality-Oriented Maintenance for Multiple Interactive System Components
Additional Topics on Stream of Variation
The author is one of the leading researchers engaged in addressing this issue … an excellent source for applied statisticians and engineers alike who are interested in the applications of multivariate statistical models. The treatment of the subject matter makes it not only a useful reference for researchers and practitioners in this field (quality monitoring and applied statistics), but also an excellent textbook for a graduate level advanced quality course … a useful resource for industrial practitioners … an excellent collection of materials for researchers, practitioners, and educators interested in advanced quality monitoring through the application of multivariate statistics and linear systems principles, and this book can be used as part of a graduate level advanced quality course.
—Satish T.S. Bukkapatnam, Oklahoma State University, Technometrics, Vol. 51 No. 4, Nov. 2009