Statistical and Computational Techniques in Manufacturing [electronic resource] / edited by J. Paulo Davim.

By: Davim, J. Paulo [editor.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Description: XIV, 285 p. 137 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642258596Subject(s): Engineering | Computer science | Mechanical engineering | Machinery | Engineering | Manufacturing, Machines, Tools | Mechanical Engineering | Computational Science and EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 670 LOC classification: TJ241Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
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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.

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.

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