Fuzzy data-driven scenario-based robust data envelopment analysis for prediction and optimisation of an electrical discharge machine’s parameters

[Display omitted] •This study deals with the prediction and optimisation of an electrical discharge machine’s parameters.•According to the uncertainty conditions, a scenario-based fuzzy RDEA model is developed.•We propose a hybrid ANFIS approach and scenario-based robust optimisation.•The fuzzy poss...

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Bibliographic Details
Published in:Expert systems with applications 2022-05, Vol.193, p.116419, Article 116419
Main Authors: Gholizadeh, Hadi, Fathollahi-Fard, Amir M., Fazlollahtabar, Hamed, Charles, Vincent
Format: Article
Language:English
Subjects:
adaptive-network-based fuzzy inference system (ANFIS)
Corrosion rate
Customer satisfaction
Data analysis
Data envelopment analysis
EDM electrodes
Electric discharge machining
Electrical discharge machine (EDM)
Flow velocity
Fuzzy possibility regression
Neural networks
Optimisation
Optimization
Process parameters
Production management
Regression models
Robust data envelopment analysis (RDEA)
Robustness (mathematics)
Supply chains
Surface roughness
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Summary:[Display omitted] •This study deals with the prediction and optimisation of an electrical discharge machine’s parameters.•According to the uncertainty conditions, a scenario-based fuzzy RDEA model is developed.•We propose a hybrid ANFIS approach and scenario-based robust optimisation.•The fuzzy possibility regression with fuzzy hypothesis testing is contributed for our experiments. An electrical discharge machine (EDM) has a high impact on production management, with its process having many advantages over conventional machining processes, including the ability of the machine to create very high-quality material that is intricate to inner industrial sections. This study investigates the impact of EDM machining parameters on the volumetric flow rate, electrode corrosion percentage, and surface roughness. These machining parameters are increasingly important for the quality of the final product, leading to higher customer satisfaction and greater market share of the company. Due to dynamic changes in the machine’s parameters and production environmental changes, using an uncertain model is inevitable. To investigate the machining data under uncertainty, a mathematical modelling approach based on the fuzzy possibility regression integrated (FPRI) model is developed. One advantage of the proposed model is that it is able to predict the surface roughness, volumetric flow rate, and corrosion percentage of the electrode. An adaptive-network-based fuzzy inference system (ANFIS) is applied to achieve the optimal levels of each output. Since the results and numbers obtained from the neural network are uncertain and their distribution is not clear, a robust data envelopment analysis approach (RDEA) is employed to select the best tuned-level of the parameters. The findings confirm the accuracy and reliability of the proposed method for prediction and optimisation of the EDM’s parameters and encourage further tests for other production and supply chain applications.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.116419