Machine learning-based optimization of geometrical accuracy in wire cut drilling

• Published in: International journal of advanced manufacturing technology Vol. 123; no. 11-12; pp. 4265 - 4276
• Main Authors: Ghasempour-Mouziraji, Mehran, Hosseinzadeh, Morteza, Hajimiri, Hossein, Najafizadeh, Mojtaba, Marzban Shirkharkolaei, Ehsan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2022; Springer Nature B.V
• Subjects: Accuracy; Advanced manufacturing technologies; Aerospace industry; Artificial neural networks; Avionics; CAE) and Design; Computer-Aided Engineering (CAD; Control equipment; Crack propagation; Design; Deviation; Drilling machines (tools); Electric discharge machining; Engineering; Feed rate; Genetic algorithms; Geometric accuracy; Industrial and Production Engineering; Machine learning; Manufacturing; Mechanical Engineering; Media Management; Neural networks; Optimization; Original Article; Process parameters; Sorting algorithms; Stainless steel; Taguchi methods; Wire; Wire cut machining; Artificial neural network; Geometrical tolerance; Coordinate measuring machine; Non-dominated sorting genetic algorithm; Machine learning
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-022-10351-8

Wire cut electrical discharge machining (EDM) equipment is run by computer numerically controlled (CNC) instruments and it is widely used in various industries such as aerospace, medical, and electronics. Thus, producing tight corners or very intricate patterns, wire EDM’s increased precision allows for intricate patterns and cuts. Not only dimensional but also geometrical precision of products does play a very important role in today’s industry. To the best of our knowledge, despite the dimensional precision, the geometrical precision has been studied by few researchers. Employing machine learning techniques, such as artificial neural networks (ANN) and non-dominated sorting genetic algorithm (NSGA), this research tries to minimize the geometrical deviation of parts produced by wire cut machining. To do so, firstly, samples have been produced based on the design matrix which contained input parameters, namely wire velocity, pulse time, and feed rate. The desired deviation from cylindricity, circularity, and symmetricity are investigated using NSGA and ANN. Then, the best and optimal combination of parameters are offered, which shows that the combination of ANN and NSGA has a significant effect on finding the optimum machining parameters. This study could supply a new viewpoint for studying geometric accuracy in wire cut drilling.