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A review: use of evolutionary algorithm for optimisation of machining parameters

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Abstract

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.

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Funding

The research works are done using the facilities at Universiti Malaysia Pahang for Research University Grant (RDU1803144) and Ministry of Higher Education for Fundamental Research Grant Scheme (FRGS) (FRGS/1/2019/TK10/UMP/03/2) (RDU1901193), through the course of this research.

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Authors and Affiliations

  1. Faculty of Mechanical and Automative Engineering Technology, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    Nor Atiqah Zolpakar

  2. Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 84600, Pagoh, Johor, Malaysia

    Mohd Fuad Yasak

  3. HiLase Centre, Institute of Physics, Czech Academy of Sciences, Za Radnici 828, 25241, Dolni Brezany, Czech Republic

    Sunil Pathak

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  1. Nor Atiqah Zolpakar
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  2. Mohd Fuad Yasak
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  3. Sunil Pathak
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Contributions

Nor Atiqah Zolpakar: writing – original draft, analysis of technical papers. Mohd Fuad Yasak: Conceptualization, writing – review and editing. Sunil Pathak: Critical review on advanced machining, writing – review and editing.

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Correspondence to Nor Atiqah Zolpakar.

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Zolpakar, N.A., Yasak, M.F. & Pathak, S. A review: use of evolutionary algorithm for optimisation of machining parameters. Int J Adv Manuf Technol 115, 31–47 (2021). https://doi.org/10.1007/s00170-021-07155-7

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