Advanced manufacturing via computer numerical machining is the art of producing mechanical components employed in aerospace, automobile, and industrial applications where a high level of accuracy is needed. This book focuses on the nano-machining of aluminum alloy and its optimization. The application of aluminum alloy in the manufacturing industry has increased tremendously due to its lightweight to high strength ratio and high-level resistance to corrosion. However, aluminum alloy has some challenges during the machining and manufacturing stage in order to solve real-life manufacturing challenges in advanced machining operation for sustainable production processes. Therefore, it is a need for the implementation of a general algebraic modeling system (GAMS) and other metaheuristic techniques for problem solving and to effectively develop mathematical models for high accuracy prediction and optimization under nano-lubrication machining conditions. This book discusses majorly on themajor three responses in machining such as surface roughness, cutting force, and material removal rate, which will give an excellent guide to undergraduate and postgraduate students, senior research fellows in academia, operational, and strategic staff in manufacturing industries.

Discusses modern optimization techniques for advanced machining processes Focuses on developing mathematical models for high accuracy prediction and optimization using nano-lubricants Includes heuristic and metaheuristic techniques for advanced machining optimization

Autorentext
Engr. Dr. Okokpujie Imhade Princess MNSE, NIMechE is Senior Lecturer/Researcher in Afe Babalola University, Ado-Ekiti. She obtained her Doctor of Philosophy (Ph.D.), from Covenant University in 2020. Her Ph.D. research focus is on Nano-lubricant in End-milling Machining Process for Advance Manufacturing. She has authored over one-hundred and fifty (150) scholarly scientific papers. Okokpujie is formally Chief Editor of Covenant Journal of Engineering Technology (CJET) and has served as Reviewer to many international journals and conferences. She is one of the top-rated researchers in her institution and has won numerous awards for her scholarly sagacity, which include Covenant University Chancellor's Exceptional Researcher of the year (won twice, 2018 and 2019 successively). Her research areas are design and production, advanced manufacturing such as machining, tool wear, vibration, nano-lubricant, energy systems, modeling, optimization, mechatronics, and a multi-disciplinary research. Recently, she was awarded the Global Excellence Stature Fellowship Grant Industry 4.0 2021 for advanced research in University of Johannesburg. Dr. Okokpujie is Registered Engineer with the Council for the Regulation of Engineering in Nigeria (COREN) and Corporate Member of the Nigeria Society of Engineers (MNSE). She is very passionate about the Girl Child's Education, committed to women development initiative, and offers quality mentorship to Young Researchers and Engineers'.

Lagouge is Full Professor in the Department of Mechanical Engineering at the University of Johannesburg. He received a Doctorate degree in mechanical engineering focusing on engineering optimization and thermo-acoustic technology and a master's degree in mechanical engineering focusing on mechanical vibration from Cape Peninsula University of Technology. He holds a bachelor's degree in electromechanical engineering from the University of Lubumbashi. Lagouge's primary research areas are engineering optimization, applied thermal engineering, electricity generation, and refrigeration using thermo-acoustic technology, artificial intelligence, and mechanical vibration.

Inhalt

Overview of Advanced Machining Process.- Cutting Fluid and its Application with Different Delivering Machining Techniques.- Development and Application of Nano-Lubricant in Machining: A Review.- Global Machining Prediction and Optimization.- Multi-objective Grey Wolf Optimizer for improved machining performance.- Multi-objective Ant Lion Optimizer for improved machining performance.- Multi-objective Grasshopper Optimizer for improved machining performance.- A multi-objective optimization approach for improving machining performance using the General Algebraic Modelling System (GAMS).- ANN and QRCCD Prediction of Surface Roughness under Biodegradable Nano-lubricant.- Cutting Force Optimization under ANN and QRCCD.- Material Removal Rate Optimization under ANN and QRCCD.- Application of Hybrid ANN and PSO for Prediction of Surface Roughness Under Biodegradable Nano-Lubricant.- Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness Under Biodegradable Nano-Lubricant.