Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008-2018)

The evolutionary optimisation algorithms appeared as an effective alternative to conventional statistical methods that have certain limitations in optimising complex manufacturing processes. Considering works published in the last decade, this paper presents an analysis of the particle swarm optimisation (PSO) implementation in designing parameters of heterogeneous manufacturing processes, both conventional and emerging, new processes. The literature review and analysis was structured according to the complexity of the optimisation problem (single response and multiresponse problems), and the development of an objective function for PSO. The tuning of the PSO algorithm-specific parameters was analysed in detail. The PSO algorithm performance was benchmarked with the results of other methods, including evolutionary algorithms, in designing process parameters. The concerns in applying PSO for multiresponse manufacturing problems were highlighted, and recommendations for future research were drawn. Such a comprehensive review on the PSO application in optimising manufacturing processes, including the detailed discussion on the algorithm characteristics and benchmark with other optimisation procedures, has not been pursued so far. Therefore, this review analysis provides hands on information for researchers and engineers at one place, and it is believed that the findings could serve as a basis for the future research and implementation directions. (C) 2019 Elsevier B.V. All rights reserved.