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Multi-cohort intelligence algorithm for solving advanced manufacturing process problems

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Abstract

In recent years, several nature-inspired optimization methods have been proposed and applied on various classes of problems. The applicability of the recently developed socio-inspired optimization method referred to as multi-cohort intelligence (Multi-CI) is validated by solving real-world problems from manufacturing processes domain, viz. non-traditional manufacturing processes. The problems are minimization of surface roughness for abrasive water jet machining (AWJM), electro-discharge machining (EDM), micro-turning and micro-milling processes. Furthermore, the taper angle for the AWJM, relative electrode wear rate for EDM, burr height and burr thickness for micro-drilling, flank wear for micro-turning process, machining time for micro-milling processes were minimized. It is important to mention that for the micro-drilling and micro-milling process different tool specifications were used. In addition, for EDM the material removal rate was maximized. The performance of the algorithm has been validated by comparing the results with other variations of CI algorithm and several contemporary algorithms such as firefly algorithm, genetic algorithm, simulated annealing and particle swarm optimization. In AWJM, Multi-CI achieved 5–8% and 8–23% minimization for surface roughness and taper angle, respectively. For EDM, 47–80% maximization of material removal rate; 2–13% and 92–98% minimization of surface roughness and relative electrode wear rate, respectively, have been attained. Furthermore, for micro-turning 2% minimization of flank wear and for micro-milling, 2–6% minimization of machining time were attained. For micro-drilling, 24% and 16–34% minimization of burr height and burr thickness were attained. In addition, the performance is compared with the regression and response surface methodology approaches and experimental solutions. The analysis regarding the convergence of all the algorithms is discussed in detail. The contributions in this paper have opened up several avenues for further applicability of the Multi-CI algorithm for solving real-world problems.

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Acknowledgements

Authors would like to thank anonymous referees for their valuable comments and suggestions that have resulted in a much improved manuscript.

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, Punjab, 144411, India

    Apoorva S. Shastri & Kamal Kumar Sharma

  2. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, India

    Apoorva S. Shastri, Aniket Nargundkar & Anand J. Kulkarni

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  1. Apoorva S. Shastri
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Shastri, A.S., Nargundkar, A., Kulkarni, A.J. et al. Multi-cohort intelligence algorithm for solving advanced manufacturing process problems. Neural Comput & Applic 32, 15055–15075 (2020). https://doi.org/10.1007/s00521-020-04858-y

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