Computational Intelligence in Manufacturing addresses applications of AI, machine learning and other innovative computational techniques across the manufacturing supply chain. The rapid development of smart or digital manufacturing known as Industry 4.0 has swiftly provided a large number of opportunities for product and manufacturing process improvement. Selecting the appropriate technologies and combining them successfully is a challenge this book helps readers overcome . It explains how to prepare different manufacturing cells for flexibility and enhanced productivity with better supply chain management, e.g., calibrating design machine tools for automation and agility.
Computational intelligence applications for non-conventional manufacturing processes such as ECM and EDM are covered alongside recent advances in traditional processes like casting, welding and metal forming. As well as describing specific applications, this practical guide also explains the computational intelligence paradigm for enhanced supply chain management.
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Table of Contents
1. Multiverse multiobjective optimization of thinning and wrinkling in automotive connector 2. An approach for machining curve cooling hole in plastic injection mold 3. Experimental and numerical investigation of deformation and forming behavior of dual-phase steel (DP 590) at elevated temperatures 4. Optimization of thermal efficiency of Scheffler solar concentrator receiver using slime mold algorithm 5. Study on drilling behavior of polymer nanocomposites modified by carbon nanomaterial with fiber: A case study 6. Machining performance analysis of micro-ED milling process of titanium alloy (Ti-6Al-4V) 7. Computational analysis of provisional study on white layer properties by EDM vs. WEDM of aluminum metal matrix composites 8. Scope of industry 4.0 components in manufacturing SMEs 9. Process parameter optimization in manufacturing of root canal device using gorilla troops optimization algorithm 10. A comprehensive review of agriculture irrigation using artificial intelligence for crop production