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Modeling and combined application of MOEA/D and TOPSIS to optimize WEDM performances of A286 superalloy

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Abstract

Superalloys are categorized as difficult to process materials with a broad spectrum of applications in industries. Process modeling and optimization of WEDM performances on nickel- and titanium-based superalloys are widely investigated. However, such investigations on iron-based superalloy are still lacking and hence probed in the present article. Thus, the paper targets modeling the correlation between the performance parameters and the control parameters with two popular techniques: response surface methodology (RSM) and artificial neural network (ANN) for WEDM of a typical iron-based superalloy, i.e., A286 superalloy. A comparison between the model estimates and the experimental values is made to check ANN and RSM's prediction accuracy. The estimates by the ANN model are exact and consistent with the experimental results. An analysis of variance (ANOVA) test is performed to perceive the degree of statistical significance of parameters. Moreover, a novel two-stage procedure, i.e., MOEA/D in collaboration with TOPSIS method, is implemented to search the optimal condition for process performances. The quality of Pareto-optimal solutions acquired using MOEA/D is compared to that of Pareto-optimal solutions obtained using NSGA II, PESA II, and MMOPSO through the use of a hypervolume (HV) parameter. Wilcoxon’s test is performed to identify the statistical difference between MOEA/D and competing algorithms. The optimal parametric combination recommended by the proposed optimization approach is Ton = 130 µs, Toff = 52 µs, Ipeak = 12 A, Wf = 5 m/min and SV = 30 V. The proposed optimization technique can also be exploited in other manufacturing processes.

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Abbreviations

WEDM:

Wire electric discharge machining

MRR :

Material removal rate

SR :

Surface roughness

T on :

Pulse-on time

T off :

Pulse-off time

I peak :

Peak current

W f :

Wire feed rate

SV:

Servo voltage

Cs:

Cutting speed

L :

Plate thickness

\(\lambda_{c}\) :

Cutoff length

MOEA:

Multi-objective evolutionary algorithm

MOEA/D:

Multi-objective evolutionary algorithm based on decomposition

MOP:

Multi-objective optimization problem

NSGA II:

Non-dominated sorting genetic algorithm II

PESA II:

Pareto-envelope-based selection algorithm II

MMOPSO:

Multi-objective particle swarm optimization

HV:

Hypervolume

TOPSIS:

Technique for order preference by similarity to ideal solution (TOPSIS)

\(S^{ + }\) :

Positive ideal solution

\(S^{ - }\) :

Negative ideal solution

\(E_{i}^{ + }\) :

Separation from the positive ideal solution

\(E_{i}^{ - }\) :

Separation from the negative ideal solution

\(CC_{i}\) :

Relative closeness coefficient

MCDM:

Multiple-criteria decision-making

RSM:

Response surface methodology

ANN:

Artificial neural network

MLP:

Multilayer perceptron

\(d_{\max }\) :

The maximum value of the response parameter

\(d_{\min }\) :

The minimum value of the response parameter

\(d_{i}\) :

The nominal value of the response parameter

trainlm:

Levenberg–Marquardt algorithm

learngd:

Gradient descent learning function

PBMOO:

Preference-based multi-objective optimization

TLBO:

Teaching–learning-based optimization

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Subhankar Saha, Saikat Ranjan Maity & Sudip Dey

  2. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Saykat Dutta

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  1. Subhankar Saha
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  2. Saikat Ranjan Maity
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  3. Sudip Dey
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  4. Saykat Dutta
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Correspondence to Saikat Ranjan Maity.

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Appendix

Appendix

See Table 8.

Table 8 Allocation of ranks to non-dominated optimal solutions exploiting TOPSIS method
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Saha, S., Maity, S.R., Dey, S. et al. Modeling and combined application of MOEA/D and TOPSIS to optimize WEDM performances of A286 superalloy. Soft Comput 25, 14697–14713 (2021). https://doi.org/10.1007/s00500-021-06264-5

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