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Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation

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Abstract

Face milling is extensively used machining operation to generate the various components. Usually the selection of the process parameters are incorporated by trial and error method, literature survey and the machining hand book. This kind of selection of process parameters turns out to be very tedious and time-consuming. In order to overcome this there is a need to develop a technique that could be able to find the optimal process parameters for the desired responses in machining. The present paper illustrates an application of response surface methodology (RSM) and particle swarm optimization (PSO) technique for optimizing the process parameters of milling and provides a comparison study among desirability and PSO techniques. The experimental investigations are carried out on metal matrix composite material AA6061-4.5%Cu-5%SiCp to study the effect of process parameters such as feed rate, spindle speed and depth of cut on the cutting force, surface roughness and power consumption. The process parameters are analyzed using RSM central composite face-centered design to study the relationship between the input and output responses. The interaction between the process parameters was identified using the multiple regression technique, which showed that spindle speed has major contribution on all the responses followed by feed rate and depth of cut. It has shown good prediction for all the responses. The optimized process parameters are acquired through multi-response optimization using the desirability approach and the PSO technique. The results obtained from PSO are closer to the values of the desirability function approach and achieved significant improvement.

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Abbreviations

AMMCs:

Aluminium metal matrix composites

ANOVA:

Analysis of variance

ANN:

Artificial Neural Network

CCFCD:

Central composite face-centered design

CNC:

Computer numerical control

DOE:

Design of experiment

GA:

Genetic algorithm

MMC:

Metal matrix composite

RSM:

Response surface methodology

PSO:

Particle swarm optimization

RCCD:

Rotatable central composite design

FX:

Cutting force, N

Ra:

Surface roughness, µm

Gbest:

Global best

pbest:

Particle best

R-sq:

Pre R-squared

R-sq(adj):

Adj R-squared

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Authors and Affiliations

  1. Department of Mechanical Engineering, NITK, Surathkal, 575025, India

    Rashmi Laxmikant Malghan & Shrikantha S. Rao

  2. Department of Mechatronics Engineering, MIT, Manipal, 576104, India

    Karthik M. C. Rao & Arun Kumar Shettigar

  3. Department of Mathematics and Computational Engineering, NITK, Surathkal, 575025, India

    R. J. D’Souza

Authors
  1. Rashmi Laxmikant Malghan
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  2. Karthik M. C. Rao
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  3. Arun Kumar Shettigar
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  4. Shrikantha S. Rao
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  5. R. J. D’Souza
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Correspondence to Karthik M. C. Rao.

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Technical Editor: Márcio Bacci da Silva.

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Malghan, R.L., Rao, K.M.C., Shettigar, A. et al. Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. J Braz. Soc. Mech. Sci. Eng. 39, 3541–3553 (2017). https://doi.org/10.1007/s40430-016-0675-7

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  • DOI: https://doi.org/10.1007/s40430-016-0675-7

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