Ingénierie
Evolutionary Computations pour Manufacturing
Description
In le next generation de Industry-Industry 4.0, le manufacturing systems will be flexible et adaptive en nature. However, it will create new challenges pour engineers such as supply chain visibility, inventory optimization. Optimizing planning et scheduling en an integrated manner, real time process optimization,making robots et machines autonomous, fine tuning de product quality,etc. Artificial intelligence (AI) can offer solutions to most de these challenges. Cognitive computing is one de le AI technologies which makes le manufacturing system capable de anticipating new problems, modeling possible solutions et makes decisions by its own. Evolutionary computing being a subset de cognitive computing, its acquaintance is very essential to explore le applications de technological drivers de Industry 4.0. This book therefore provides theoretical concepts et practical applications de 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, et multiobjective optimization. Salient features de this book are: 1. Basic concepts de various evolutionary computational methods are explained en step by step manner through simple examples at le beginning de chapters. 2. Applications de various algorithms are demonstrated through about 20 real life case studies. Most de these case studies are based sur le research work de le author et their results are practically implemented et validated. 3. Several variants de each algorithm are also demonstrated through examples.
Table des matières
À propos de le Author – Preface – 1. Introduction 1.1 Traditional Optimization Techniques – 2 1.2 Optimization de Ultrasonic Machining Process 1.3 Applications de Evolutionary Computational Méthodes to Manufacturing Systèmes – 2. Génétique Algorithm 2.1 Introduction 2.2 Mechanism de Working de Génétique Algorithm (GA) 2.3 Optimization de le Plante Layout en Production de an Automobile Transmission Système 2.4 Modeling et Optimization de Blank Nesting en Press Tool Operations 2.5 Material Flow Optimisation en Flexible Manufacturing Système 2.6 Variants de Génétique Algorithm – 3. Particle Swarm Optimization 3.1 Introduction 3.2 Mechanism de Working de Particle Swarm Optimization (PSO) Algorithm 3.3 Optimization de Abrasive Eau Jet Machining (AWJM) Process 3.4 Optimization de Contrôle Parameters de Cooling Système pour an Industrial Robot Contrôleler 3.5 Modelling et Optimization de Process Parameters de Electric Discharge Machining to Minimize Wire Breakage 3.6 Variants de PSO Algorithm – 4. Artificial Bee Colony Algorithm 4.1 Introduction 4.2 Mechanism de Working de Artificial Bee Colony (ABC) Algorithm 4.3 Conception Optimization de Screw Conveyer Système pour Handling Carbon Black Powder 4.4 Parametric Optimization de Hard Chrome Electro-plating Process pour Uniform Coating Thickness et Improved Hardness 4.5 Variants de ABC Algorithm – 5. Shuffled Frog Leaping Algorithm 5.1 Introduction 5.2 Mechanism de Working de Shuffled Frog Leaping Algorithm 5.3 Optimization de Laser Beam Machining Process 5.4 Tool Path Planning pour Hole Making Operations en Injection Moulds 5.5 Variants de Shuffled Frog Leaping Algorithm – 6. Harmony Search Algorithm 6.1 Introduction 6.2 Mechanism de Working de Harmony Search (HS) Algorithm 6.3 Motion Planning pour Redundant Robot Manipulator Under le Condition de Restrictions 6.4 Parametric Optimization de Electro-chemical Process 6.5 Variants de Harmony Search Algorithm – 7. Simulated Annealing Algorithm 7.1 Introduction 7.2 Mechanism de Working de Simulated Annealing (SA) Algorithm 7.3 Conception Optimization de a Universal Motor Using Simulated Annealing Algorithm 7.4 Modelling et Optimization de Process Parameters de Injection Molding Process 7.5 Variants de Simulated Annealing Algorithm – 8. Teaching Learning Based Optimization 8.1 Introduction 8.2 Mechanism de Working de Teaching Learning Based Optimization (TLBO) Algorithm 8.3 Optimization de Cold Backward Extrusion Process 8.4 Optimization de Supply Chain Système en Multi-product et Multi-supplier Scenario 8.5 Variants de Teaching Learning Based Optimization Algorithm – 9. Fuzzy Logic Applications en Optimization 9.1 Introduction 9.2 Collaboration de Fuzzy Logic et Evolutionary Algorithms 9.3 Développement de Fuzzy Scale – 10. Multi-objective Optimization Méthodes 10.1 Introduction 10.2 Méthodes de Formulating Combined Objective Function (Z) en Multi-objective Optimization Using Priori Approche 10.3 Méthodes de Multi-objective Optimization Using Posteriori Approche 10.4 Improving le Quality Characteristics de Abrasive Eau Jet Machining de Marble Material – References – Subject Index