A review: Use of evolutionary algorithm for optimisation of machining parameters
Optimisation of machining parameters is crucial to ensure higher productivity and optimum outcomes in machining processes. By optimising machining parameters, a particular machining process can produce better machining outcomes within equivalent resources. This paper reviews past studies to achieve...
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| Format: | Article |
| Language: | English English |
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Springer-Verlag
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33830/ http://umpir.ump.edu.my/id/eprint/33830/1/A%20review-use%20of%20evolutionary%20algorithm%20for%20optimisation.pdf http://umpir.ump.edu.my/id/eprint/33830/2/A%20review-use%20of%20evolutionary%20algorithm%20for%20optimisation%20of%20machining%20parameters.pdf |
| _version_ | 1848824356107452416 |
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| author | Zolpakar, N. A. Mohd Fuad, Yasak Pathak, Sunil |
| author_facet | Zolpakar, N. A. Mohd Fuad, Yasak Pathak, Sunil |
| author_sort | Zolpakar, N. A. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Optimisation of machining parameters is crucial to ensure higher productivity and optimum outcomes in machining processes. By optimising machining parameters, a particular machining process can produce better machining outcomes within equivalent resources. This paper reviews past studies to achieve the desired outputs; minimum surface roughness (SR), highest material removal rate (MRR), lowest production cost, and the shortest production time of machining processes and various optimisation attempts in terms of varying parameters that affect the outcomes. The review deliberates the optimisation methods employed and analyses the performance discussing the relevant parameters that must have been considered by past researchers. To date, most studies have been focusing on optimising conventional machining processes such as turning, milling, and drilling. Optimisation works have been performed parametrically, experimentally, and numerically, where discrete variations of the parameters are investigated, while others are remained constant. Lately, evolutionary algorithm, statistical approaches such as genetic algorithm (GA), particle swarm optimisation (PSO), and cuckoo search algorithm (CSA) have been utilised in simultaneous optimisation of the parameters of the desired outputs and its great potential in optimising machining processes is recognisable. |
| first_indexed | 2025-11-15T03:11:43Z |
| format | Article |
| id | ump-33830 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:11:43Z |
| publishDate | 2021 |
| publisher | Springer-Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-338302022-04-21T04:29:39Z http://umpir.ump.edu.my/id/eprint/33830/ A review: Use of evolutionary algorithm for optimisation of machining parameters Zolpakar, N. A. Mohd Fuad, Yasak Pathak, Sunil TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Optimisation of machining parameters is crucial to ensure higher productivity and optimum outcomes in machining processes. By optimising machining parameters, a particular machining process can produce better machining outcomes within equivalent resources. This paper reviews past studies to achieve the desired outputs; minimum surface roughness (SR), highest material removal rate (MRR), lowest production cost, and the shortest production time of machining processes and various optimisation attempts in terms of varying parameters that affect the outcomes. The review deliberates the optimisation methods employed and analyses the performance discussing the relevant parameters that must have been considered by past researchers. To date, most studies have been focusing on optimising conventional machining processes such as turning, milling, and drilling. Optimisation works have been performed parametrically, experimentally, and numerically, where discrete variations of the parameters are investigated, while others are remained constant. Lately, evolutionary algorithm, statistical approaches such as genetic algorithm (GA), particle swarm optimisation (PSO), and cuckoo search algorithm (CSA) have been utilised in simultaneous optimisation of the parameters of the desired outputs and its great potential in optimising machining processes is recognisable. Springer-Verlag 2021-07 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33830/1/A%20review-use%20of%20evolutionary%20algorithm%20for%20optimisation.pdf pdf en http://umpir.ump.edu.my/id/eprint/33830/2/A%20review-use%20of%20evolutionary%20algorithm%20for%20optimisation%20of%20machining%20parameters.pdf Zolpakar, N. A. and Mohd Fuad, Yasak and Pathak, Sunil (2021) A review: Use of evolutionary algorithm for optimisation of machining parameters. International Journal of Advanced Manufacturing Technology, 115 (1-2). 31 -47. ISSN 0268-3768 (Print), 1433-3015 (Online). (Published) https://doi.org/10.1007/s00170-021-07155-7 https://doi.org/10.1007/s00170-021-07155-7 |
| spellingShingle | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Zolpakar, N. A. Mohd Fuad, Yasak Pathak, Sunil A review: Use of evolutionary algorithm for optimisation of machining parameters |
| title | A review: Use of evolutionary algorithm for optimisation of machining parameters |
| title_full | A review: Use of evolutionary algorithm for optimisation of machining parameters |
| title_fullStr | A review: Use of evolutionary algorithm for optimisation of machining parameters |
| title_full_unstemmed | A review: Use of evolutionary algorithm for optimisation of machining parameters |
| title_short | A review: Use of evolutionary algorithm for optimisation of machining parameters |
| title_sort | review: use of evolutionary algorithm for optimisation of machining parameters |
| topic | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/33830/ http://umpir.ump.edu.my/id/eprint/33830/ http://umpir.ump.edu.my/id/eprint/33830/ http://umpir.ump.edu.my/id/eprint/33830/1/A%20review-use%20of%20evolutionary%20algorithm%20for%20optimisation.pdf http://umpir.ump.edu.my/id/eprint/33830/2/A%20review-use%20of%20evolutionary%20algorithm%20for%20optimisation%20of%20machining%20parameters.pdf |