A Methodology for Manufacturing Process Signature Analysis (Classic Reprint) - Hardcover

Steven D. Eppinger

 
9780484046459: A Methodology for Manufacturing Process Signature Analysis (Classic Reprint)

Synopsis

Unlocking factory insights with signature analysis that turns data into better processes.

This nonfiction guide explains how engineers measure and interpret signals from production lines to monitor quality, detect failures, and improve control systems. It combines theory with practical methods you can apply to real manufacturing challenges.

The book outlines a clear, three‑step approach: extract meaningful features from process signatures, select the most informative ones, and build classifiers that predict quality outcomes. It compares traditional tools like statistical process control with modern neural networks, showing how each fits different data sets and goals. Through examples from solder joint inspection and other processes, you’ll see how signatures reveal how well a process is behaving and where to intervene.
What you’ll experience
  • A practical framework for turning raw measurements into actionable quality signals
  • Tutorials on feature extraction, feature selection, and classifier choices
  • Real‑world case studies illustrating how sensors and analyses align to improve online monitoring
  • Guidance on balancing accuracy with computational cost in manufacturing analytics
Ideal for engineers, quality leaders, and students seeking a hands‑on method to monitor and improve complex production systems. The discussion stays grounded in real data and process realities, helping you translate signal patterns into better decisions on the shop floor.

  • Learn how to parameterize signals and classify process quality effectively
  • Compare classical SPC approaches with modern neural network methods
  • See how sensor placement and data quality influence results
  • Explore practical steps for implementing on‑line monitoring and control

Ideal for readers of manufacturing analytics, quality engineering, and process improvement literature.

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