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Modern optimization techniques for advanced machiningheuristic and metaheuristic techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern optimization techniques for advanced machiningby Imhade P. Okokpujie, Lagouge K. Tartibu.
Reminder of title:	heuristic and metaheuristic techniques /
Author:	Okokpujie, Imhade P.
other author:	Tartibu, Lagouge K.
Published:	Cham :Springer Nature Switzerland :2023.
Description:	xiv, 359 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Metal-cutting.
Online resource:	https://doi.org/10.1007/978-3-031-35455-7
ISBN:	9783031354557$q(electronic bk.)

Modern optimization techniques for advanced machiningheuristic and metaheuristic techniques /
Okokpujie, Imhade P.

Modern optimization techniques for advanced machiningheuristic and metaheuristic techniques /[electronic resource] :by Imhade P. Okokpujie, Lagouge K. Tartibu. - Cham :Springer Nature Switzerland :2023. - xiv, 359 p. :ill. (chiefly color), digital ;24 cm. - Studies in systems, decision and control,v. 4852198-4190 ;. - Studies in systems, decision and control ;v.3..

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.

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 the major 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.

ISBN: 9783031354557$q(electronic bk.)

Standard No.: 10.1007/978-3-031-35455-7doiSubjects--Topical Terms:

190468
Metal-cutting.

LC Class. No.: TJ1185

Dewey Class. No.: 671.35

Modern optimization techniques for advanced machiningheuristic and metaheuristic techniques /
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505 0 $a 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.
520 $a 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 the major 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.
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