Genetic algorithm-based optimization of cutting parameters in turning processes
TL;DR: An optimization paradigm based on GA for the determination of the cutting parameters in machining operations is proposed in this article, where the GA has been used as an optimal solution finder for finding optimal cutting parameters during a turning process.
About: This article is published in Procedia CIRP.The article was published on 2013-01-01 and is currently open access. It has received 114 citations till now. The article focuses on the topics: Machining & Genetic algorithm.
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TL;DR: An attempt has been made for review on AI applications in Computer Aided Process Planning (CAPP) and manufacturing and role of Evolutionary Techniques (ET) in intelligent system development, execution of PP activities and manufacturing is described.
118 citations
TL;DR: In this article, a new emerging frontier in the evolution of the digitalisation and the 4th industrial revolution (Industry 4.0) is considered to be that of biologicalisation in manufacturing.
Abstract: A new emerging frontier in the evolution of the digitalisation and the 4th industrial revolution (Industry 4.0) is considered to be that of “Biologicalisation in Manufacturing”. This has been defined by the authors to be “The use and integration of biological and bio-inspired principles, materials, functions, structures and resources for intelligent and sustainable manufacturing technologies and systems with the aim of achieving their full potential.” In this White Paper, detailed consideration is given to the meaning and implications of “Biologicalisation” from the perspective of the design, function and operation of products, manufacturing processes, manufacturing systems, supply chains and organisations. The drivers and influencing factors are also reviewed in detail and in the context of significant developments in materials science and engineering. The paper attempts to test the hypothesis of this topic as a breaking new frontier and to provide a vision for the development of manufacturing science and technology from the perspective of incorporating inspiration from biological systems. Seven recommendations are delivered aimed at policy makers, at funding agencies, at the manufacturing research community and at those industries involved in the development of next generation manufacturing technology and systems. It is concluded that it is valid to argue that Biologicalisation in Manufacturing truly represents a new and breaking frontier of digitalisation and Industry 4.0 and that the market potential is very strong. It is evident that extensive research and development is required in order to maximise on the benefits of a biological transformation.
115 citations
Cites methods from "Genetic algorithm-based optimizatio..."
...Genetic algorithms were utilised by Abu Qudeiri et al. [52] to reduce the cutting tool path and by D’Addona and Teti [53] to optimise the cutting parameters in turning processes....
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...Biologicalisation: Biological transformation in manufacturing Gerald Byrnea,*, Dimitri Dimitrovb, Laszlo Monostoric, Roberto Tetid, Fred van Houtene, Rafi Wertheimf a School of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland bDepartment of Industrial Engineering, Stellenbosch University, Victoria Street, Stellenbosch 7600, South Africa c Institute for Computer Science and Control, Hungarian Academy of Sciences, Kende utca 13-17, 1111 Budapest, Hungary dDepartment of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, Naples 80125, Italy eDepartment of Design, Production and Management, University of Twente, Drienerlolaan 5, 7522NB Enschede, The Netherlands f Faculty of Mechanical Engineering, Braude College, Karmiel and Lavon Industrial Park, 2011800, Israel A R T I C L E I N F O Article history: Available online 12 April 2018 Keywords: Industrie 4.0 Manufacturing Biologicalisation in Manufacturing Biological transformation International perspective Cyber-physical systems Industry 4.0 Digitalisation Bio-inspired Bio-intelligent Bio-integrated A B S T R A C T A new emerging frontier in the evolution of the digitalisation and the 4th industrial revolution (Industry 4.0) is considered to be that of “Biologicalisation in Manufacturing”....
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...[52] to reduce the cutting tool path and by D’Addona and Teti [53] to optimise the cutting parameters in turning processes....
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TL;DR: In this article, a process model of CNC machining is presented to scope the system boundaries for energy footprint and process efficiency, and a multi-objective optimization model is then proposed to explore the impact of cutting speed and feed rate on carbon emissions and processing time.
94 citations
TL;DR: Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
82 citations
TL;DR: An improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized.
75 citations
References
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Book•
01 Jan 1996TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Abstract: From the Publisher:
"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.
-- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
9,933 citations
"Genetic algorithm-based optimizatio..." refers background in this paper
...Therefore, GA is a population-based search methodology [8, 9]....
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Book•
01 Jan 1991
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Abstract: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. The remainder of the book is turned over to a series of short review articles by a collection of authors, each explaining how genetic algorithms have been applied to problems in their own specific area of interest. The first part of the book introduces the fundamental genetic algorithm (GA), explains how it has traditionally been designed and implemented and shows how the basic technique may be applied to a very simple numerical optimisation problem. The basic technique is then altered and refined in a number of ways, with the effects of each change being measured by comparison against the performance of the original. In this way, the reader is provided with an uncluttered introduction to the technique and learns to appreciate why certain variants of GA have become more popular than others in the scientific community. Davis stresses that the choice of a suitable representation for the problem in hand is a key step in applying the GA, as is the selection of suitable techniques for generating new solutions from old. He is refreshingly open in admitting that much of the business of adapting the GA to specific problems owes more to art than to science. It is nice to see the terminology associated with this subject explained, with the author stressing that much of the field is still an active area of research. Few assumptions are made about the reader's mathematical background. The second part of the book contains thirteen cameo descriptions of how genetic algorithmic techniques have been, or are being, applied to a diverse range of problems. Thus, one group of authors explains how the technique has been used for modelling arms races between neighbouring countries (a non- linear, dynamical system), while another group describes its use in deciding design trade-offs for military aircraft. My own favourite is a rather charming account of how the GA was applied to a series of scheduling problems. Having attempted something of this sort with Simulated Annealing, I found it refreshing to see the authors highlighting some of the problems that they had encountered, rather than sweeping them under the carpet as is so often done in the scientific literature. The editor points out that there are standard GA tools available for either play or serious development work. Two of these (GENESIS and OOGA) are described in a short, third part of the book. As is so often the case nowadays, it is possible to obtain a diskette containing both systems by sending your Visa card details (or $60) to an address in the USA.
6,758 citations
01 Jan 1998
TL;DR: The software described in this document is furnished under a license agreement and the rights of the Government regarding its use, reproduction and disclosure are as set forth in Clause 52.227-19(c)(2) of the FAR.
Abstract: The software described in this document is furnished under a license agreement. The software may be used or copied only under the terms of the license agreement. No part of this manual may be photocopied or reproduced in any form without prior written consent from The MathWorks, Inc. (a) for units of the Department of Defense: RESTRICTED RIGHTS LEGEND: Use, duplication, or disclosure by the Government is subject to restrictions as set forth in subparagraph (c)(1)(ii) of the Rights in Technical Data and Computer Software Clause at DFARS 252.227-7013. (b) for any other unit or agency: NOTICE-Notwithstanding any other lease or license agreement that may pertain to, or accompany the delivery of, the computer software and accompanying documentation, the rights of the Government regarding its use, reproduction and disclosure are as set forth in Clause 52.227-19(c)(2) of the FAR. Other product or brand names are trademarks or registered trademarks of their respective holders.
851 citations
TL;DR: A multi-objective optimization technique, based on genetic algorithms, to optimize the cutting parameters in turning processes: cutting depth, feed and speed is presented.
253 citations
TL;DR: In this article, a model for the optimization of machining conditions in a multi-pass turning operation is presented, where both rough cutting and finishing cutting are considered in the model and dual optimization of cost functions for each subproblem is pursued.
Abstract: This paper presents a model for the optimization of machining conditions in a multi-pass turning operation. Both rough cutting and finishing cutting are considered in the model and dual optimization of cost functions for each subproblem is pursued. The preventive tool replacement strategy used in practice is incorporated. Machining idle time is also regarded as a variable. After practical constraints are established, optimization is carried out using the dynamic programming method. An example illustrates the formulation of the problem and the optimization procedure
198 citations