Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses |
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| Authors: | E Portillo M Marcos I Cabanes A Zubizarreta |
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| Affiliation: | 1. LATA2M, Laboratoire de Thermodynamique Appliquée et Modélisation Moléculaire, University of Tlemcen, Post Office Box 119, Tlemcen, 13000, Algeria;2. Laboratoire Multimatériaux et Interfaces, UMR 5615, Université de Lyon, Université Claude Bernard Lyon1, 69622, Villeurbanne, France;3. Université de Saint Etienne, Jean Monnet, F-42023, Saint Etienne, France;4. Group ThEA Thermodynamic Phases Equilibrium and Advanced Analysis, Faculty of Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon;1. Department of Mechanical Engineering, MGM’s Jawaharlal Nehru Engineering College, Aurangabad, (M.S.), 431003, India;2. Department of Civil Engineering, MGM’s Jawaharlal Nehru Engineering College, Aurangabad, (M.S.), 431003, India;1. Mechanical Engineering Department, Technical Education Faculty, Urmia University, Urmia, West Azerbaijan 57561-15311, Iran;2. Faculty of Mechanical Engineering, Sahand University of Technology, Sahand New Town, Tabriz, Iran;3. Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agriculture, Urmia University, Urmia, Iran;1. REQUIMTE – Laboratório Associado para a Química Verde, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal;2. ICETA – Instituto de Ciências, Tecnologias e Agroambiente da Universidade do Porto, Praça Gomes Teixeira, Apartado 55142, 4051-401 Porto, Portugal;3. REQUIMTE – UCIBIO, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal;4. REQUIMTE – Laboratório Associado para a Química Verde, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Monte de Caparica, Portugal |
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| Abstract: | This paper presents the use of artificial neural networks (ANN) to diagnose degraded behaviours in wire electrical discharge machining (WEDM). The detection in advance of the degradation of the cutting process is crucial since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy. Concerning this, previous investigations have identified different types of degraded behaviours in two commonly used workpiece thicknesses (50 and 100 mm). This goal was achieved by monitoring different functions of characteristic discharge variables. However, the thresholds achieved by these functions depended on the thickness of the workpiece. Consequently, the main objective of this work is to detect the degradation of the process when machining workpiece of different thicknesses using one unique empirical model. Since artificial neural network techniques are appropriate for stochastic and non-linear nature processes, its use is investigated here to cope with workpieces of different thicknesses. The results of this work show a satisfactory performance of the presented approach. The satisfactory performance is shown by two ratios: the validation ratio, which ranges between 85% and 100%, and the test ratio, which results between 75% and 100%. |
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