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Evolutionary Computations for Manufacturing

Author Name : Padmakar J. Pawar

Features

  • Publisher : Studium Press (India) Pvt. Ltd.
  • Edition : Ist
  • ISBN 13 : 978-93-85046-52-0
  • Page no : 308
  • Publication Year : 2019
$1395

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  • Description
  • Table Of Content
  • Reviews
"In the next generation of Industry-Industry 4.0, the manufacturing systems will be flexible and adaptive in nature. However, it will create new challenges for engineers such as supply chain visibility, inventory optimization. Optimizing planning and scheduling in an integrated manner, real time process optimization,making robots and machines autonomous, fine tuning of product quality,etc. Artificial intelligence (AI) can offer solutions to most of these challenges. Cognitive computing is one of the AI technologies which makes the manufacturing system capable of anticipating new problems, modeling possible solutions and makes decisions by its own. Evolutionary computing being a subset of cognitive computing, its acquaintance is very essential to explore the applications of technological drivers of Industry 4.0. This book therefore provides theoretical concepts and practical applications of several successful evolutionary computational methods such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, shuffled frog leaping algorithm, simulated annealing algorithm, harmony search algorithm, teaching learning based optimization algorithm, fuzzy optimization, and multiobjective optimization. Salient features of this book are: 1. Basic concepts of various evolutionary computational methods are explained in step by step manner through simple examples at the beginning of chapters. 2. Applications of various algorithms are demonstrated through about 20 real life case studies. Most of these case studies are based on the research work of the author and their results are practically implemented and validated. 3. Several variants of each algorithm are also demonstrated through examples."
Table of Content "About the Author – Preface – 1. Introduction 1.1 Traditional Optimization Techniques – 2 1.2 Optimization of Ultrasonic Machining Process 1.3 Applications of Evolutionary Computational Methods to Manufacturing Systems – 2. Genetic Algorithm 2.1 Introduction 2.2 Mechanism of Working of Genetic Algorithm (GA) 2.3 Optimization of the Plant Layout in Production of an Automobile Transmission System 2.4 Modeling and Optimization of Blank Nesting in Press Tool Operations 2.5 Material Flow Optimisation in Flexible Manufacturing System 2.6 Variants of Genetic Algorithm – 3. Particle Swarm Optimization 3.1 Introduction 3.2 Mechanism of Working of Particle Swarm Optimization (PSO) Algorithm 3.3 Optimization of Abrasive Water Jet Machining (AWJM) Process 3.4 Optimization of Control Parameters of Cooling System for an Industrial Robot Controller 3.5 Modelling and Optimization of Process Parameters of Electric Discharge Machining to Minimize Wire Breakage 3.6 Variants of PSO Algorithm – 4. Artificial Bee Colony Algorithm 4.1 Introduction 4.2 Mechanism of Working of Artificial Bee Colony (ABC) Algorithm 4.3 Design Optimization of Screw Conveyer System for Handling Carbon Black Powder 4.4 Parametric Optimization of Hard Chrome Electro-plating Process for Uniform Coating Thickness and Improved Hardness 4.5 Variants of ABC Algorithm – 5. Shuffled Frog Leaping Algorithm 5.1 Introduction 5.2 Mechanism of Working of Shuffled Frog Leaping Algorithm 5.3 Optimization of Laser Beam Machining Process 5.4 Tool Path Planning for Hole Making Operations in Injection Moulds 5.5 Variants of Shuffled Frog Leaping Algorithm – 6. Harmony Search Algorithm 6.1 Introduction 6.2 Mechanism of Working of Harmony Search (HS) Algorithm 6.3 Motion Planning for Redundant Robot Manipulator Under the Condition of Restrictions 6.4 Parametric Optimization of Electro-chemical Process 6.5 Variants of Harmony Search Algorithm – 7. Simulated Annealing Algorithm 7.1 Introduction 7.2 Mechanism of Working of Simulated Annealing (SA) Algorithm 7.3 Design Optimization of a Universal Motor Using Simulated Annealing Algorithm 7.4 Modelling and Optimization of Process Parameters of Injection Molding Process 7.5 Variants of Simulated Annealing Algorithm – 8. Teaching Learning Based Optimization 8.1 Introduction 8.2 Mechanism of Working of Teaching Learning Based Optimization (TLBO) Algorithm 8.3 Optimization of Cold Backward Extrusion Process 8.4 Optimization of Supply Chain System in Multi-product and Multi-supplier Scenario 8.5 Variants of Teaching Learning Based Optimization Algorithm – 9. Fuzzy Logic Applications in Optimization 9.1 Introduction 9.2 Collaboration of Fuzzy Logic and Evolutionary Algorithms 9.3 Development of Fuzzy Scale – 10. Multi-objective Optimization Methods 10.1 Introduction 10.2 Methods of Formulating Combined Objective Function (Z) in Multi-objective Optimization Using Priori Approach 10.3 Methods of Multi-objective Optimization Using Posteriori Approach 10.4 Improving the Quality Characteristics of Abrasive Water Jet Machining of Marble Material – References – Subject Index".
Series No
Title Evolutionary Computations for Manufacturing
Author's Name Padmakar J. Pawar
Publisher Studium Press (India) Pvt. Ltd.
Page No. 308
Year Of Publication 2019
ISBN 10 No
ISBN 13 978-93-85046-52-0
Book size width -
book size(hei) -
Edition Ist
Book Size(len) -
Binding type Hard bound
About The Book "In the next generation of Industry-Industry 4.0, the manufacturing systems will be flexible and adaptive in nature. However, it will create new challenges for engineers such as supply chain visibility, inventory optimization. Optimizing planning and scheduling in an integrated manner, real time process optimization,making robots and machines autonomous, fine tuning of product quality,etc. Artificial intelligence (AI) can offer solutions to most of these challenges. Cognitive computing is one of the AI technologies which makes the manufacturing system capable of anticipating new problems, modeling possible solutions and makes decisions by its own. Evolutionary computing being a subset of cognitive computing, its acquaintance is very essential to explore the applications of technological drivers of Industry 4.0. This book therefore provides theoretical concepts and practical applications of several successful evolutionary computational methods such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, shuffled frog leaping algorithm, simulated annealing algorithm, harmony search algorithm, teaching learning based optimization algorithm, fuzzy optimization, and multiobjective optimization. Salient features of this book are: 1. Basic concepts of various evolutionary computational methods are explained in step by step manner through simple examples at the beginning of chapters. 2. Applications of various algorithms are demonstrated through about 20 real life case studies. Most of these case studies are based on the research work of the author and their results are practically implemented and validated. 3. Several variants of each algorithm are also demonstrated through examples."

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