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

Hadi Gholizadeh, Amir M. Fathollahi-Fard, Hamed Fazlollahtabar, Vincent Charles

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

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.

Original languageEnglish
Article number116419
JournalExpert Systems with Applications
Volume193
DOIs
StatePublished - 1 May 2022

Keywords

  • adaptive-network-based fuzzy inference system (ANFIS)
  • Electrical discharge machine (EDM)
  • Fuzzy possibility regression
  • Optimisation
  • Robust data envelopment analysis (RDEA)

Fingerprint

Dive into the research topics of 'Fuzzy data-driven scenario-based robust data envelopment analysis for prediction and optimisation of an electrical discharge machine's parameters'. Together they form a unique fingerprint.

Cite this