Artificial Intelligence in Manufacturing Research

J. Paulo Davim, PhD (Editor)
Department of Mechanical Engineering, University of Aveiro, Campus Santiago, Aveiro, Portugal

Series: Materials and Manufacturing Technology

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Volume 10

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Volume 2

Volume 3

Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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Artificial intelligence is a subfield of computer science concerned with understanding the nature of intelligence and constructing computer systems capable of intelligent action. Artificial intelligence can be applied to all systems and manufacturing processes.This book aims to provide the research and review studies on artificial intelligence in manufacturing. The present research book can be used as a text book for final undergraduate engineering course (for example, mechanical, manufacturing, systems, etc) or as a subject on artificial intelligence in manufacturing at the postgraduate level. Also, this book can serve as a useful reference for academics, manufacturing and computational sciences researchers, mechanical, systems and manufacturing engineers, professional in related industries with artificial intelligence and manufacturing. (Imprint:Novinka)

Preface pp. i-vii
Research and review studies

Chapter 1. Application of neural networks and fuzzy sets to machining and metal forming
(U. S. Dixit, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, INDIA)pp. 1-30

Chapter 2. Multi-objective optimization of multi-pass milling process parameters using artificial bee colony
(R. Venkata Rao, S.V. National Institute of Technology, Gujarat,INDIA; P. J. Pawar,K.K. Wagh Institute of Engineering Education and Research, Nashik, Maharashtra, INDIA)pp. 31-50

Chapter 3. Optimization of abrasive flow machining process parameters using particle swarm optimization and simulated annealing algorithms
(P. J. Pawar, R. Venkata Rao, J. Paulo Davim)pp. 51-64

Chapter 4. Study of effects of process parameters on burr height in drilling of AISI 316 stainless steel using artificial neural network model
(V. N. Gaitonde and S.R. Karnik, B.V.B. College of Engineering & Technology, Hubli– INDIA; J. Paulo Davim)pp. 65-78

Chapter 5. Artificial neural network modelling of surface quality characteristics in abrasive water jet machining of trip steel sheet
(N. M. Vaxevanidis and A. Markopoulos, School of Pedagogical & Technological Education (ASPETE), Athens, GREECE; Te G. Petropoulos,
University of Thessaly, Volos, GREECE)pp. 79-100

Chapter 6. Multi-objective optimisation of cutting parameters for drilling aluminium
(Ramón Quiza, University of Matanzas, Matanzas, CUBA; J. Paulo Davim)pp. 101-110

Chapter 7. Application of fuzzy logic in manufacturing: a study on modelling of cutting force in turning GFRP composites
K. Palanikumar, J. Paulo Davim pp. 111-128

Chapter 8. Flank wear detection with AE signal and FNN during turning of Al/15vol%SiC-MMC
(Alakesh Manna, Punjab Engineering College, Deemed University,
Chandigarh, INDIA)pp. 129-140

Chapter 9. Integration of product development process using STEP and PDM
(S. Q. Xie, W. L. Chen, University of Auckland, Auckland, NEW ZEALAND)pp. 141-174

Index pp. 175-182

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