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Engineering - Production & Process Engineering | Statistical and Computational Techniques in Manufacturing

Statistical and Computational Techniques in Manufacturing

Davim, J. Paulo (Ed.)

2012, XIV, 285 p. 137 illus.

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  • Chapters written by experts in the field
  • Useful for teaching modern manufacturing
  • Reference work for both students and industrial engineers
In recent years, interest in developing statistical and computational techniques for applied manufacturing engineering has been increased. Today, due to the great complexity of manufacturing engineering and the high number of parameters used, conventional approaches are no longer sufficient. Therefore, in manufacturing, statistical and computational techniques have achieved several applications, namely, modelling and simulation manufacturing processes, optimization manufacturing parameters, monitoring and control, computer-aided process planning, etc. The present book aims to provide recent information on statistical and computational techniques applied in manufacturing engineering. The content is suitable for final undergraduate engineering courses or as a subject on manufacturing at the postgraduate level. This book serves as a useful reference for academics, statistical and computational science researchers, mechanical, manufacturing and industrial engineers, and professionals in industries related to manufacturing engineering.

Content Level » Research

Keywords » Modern manufacturing - Nanomanufacturing - Optimization techniques - Simulation processes

Related subjects » Computational Science & Engineering - Mechanical Engineering - Production & Process Engineering

Table of contents 

Machining.- Non traditional machining (EDM, LAM, USM, etc).-Abrasive machining.- Casting & joining.- Forming.- Powder metallurgy.- Composites & ceramics processing.- Rapid prototyping & tooling processes.- Micro and nanomanufacturing processes.- Sustainable and green manufacturing.- Manufacturing systems.- Statistical and computational techniques.- Design experiments.- Taguchi techniques.- ANOVA (Analysis of variance).- MRA (multi analysis regression).- RSM (Response surface methodology) Finite Element Method (FEM) modeling/simulation.- Artificial Neural Networks (ANNs).- Evolutionary computation.- Fuzzy logic.- Neuro-fuzzy systems.- Particle swarm optimization (PSO).- Optimization techniques of complex systems.

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