ABSTRACT

Improvements in process control, such as defined-accuracy instrumentation structures and computationally intelligent process modeling, enable advanced capabilities such as molecular manufacturing. High Performance Instrumentation and Automation demonstrates how systematizing the design of instrumentation and automation leads to higher performance through more homogeneous systems, which are frequently assisted by rule-based, fuzzy logic, and neural network process descriptions.

Incorporate Advanced Performance Enhancements into Your Automation Enterprise

The book illustrates generic common core process-to-control concurrent engineering linkages applied to a variety of laboratory and industry automation systems. It outlines:

  • Product properties translated into realizable process variables
  • Axiomatic decoupling of subprocess variables for improved robustness
  • Production planner model-driven goal state execution
  • In situ sensor and control structures for attenuating process disorder
  • Apparatus tolerance design for minimizing process variabilities
  • Production planner remodeling based on product features measurement for quality advancement

Coverage also includes multisensor data fusion, high-performance computer I/O design guided by comprehensive error modeling, multiple sensor algorithmic error propagation, robotic axes volumetric accuracy, quantitative video digitization and reconstruction evaluation, and in situ process measurement methods.

High Performance Instrumentation and Automation reflects the experience of engineer and author Patrick Garrett, including his role as co-principal investigator for an Air Force intelligent manufacturing initiative.

You can download Analysis Suite.xls, computer-aided design instrumentation software, available in the book's description on the CRC Press website.

chapter 3|18 pages

Instrumentation filters with nominal error

chapter 5|24 pages

Data conversion devices and errors

chapter 8|20 pages

Automation systems concurrent engineering

chapter 12|8 pages

Neural network directed steel annealing