{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:40:23Z","timestamp":1770990023356,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>To achieve enhanced surface characteristics in wire electrical discharge machining (WEDM), the present work reports the use of an artificial neural network (ANN) combined with a genetic algorithm (GA) for the correlation and optimization of WEDM process parameters. The parameters considered are the discharge current, voltage, pulse-on time, and pulse-off time, while the response is fractal dimension. The usefulness of fractal dimension to characterize a machined surface lies in the fact that it is independent of the resolution of the instrument or length scales. Experiments were carried out based on a rotatable central composite design. A feed-forward ANN architecture trained using the Levenberg-Marquardt (L-M) back-propagation algorithm has been used to model the complex relationship between WEDM process parameters and fractal dimension. After several trials, 4-3-3-1 neural network architecture has been found to predict the fractal dimension with reasonable accuracy, having an overall R-value of 0.97. Furthermore, the genetic algorithm (GA) has been used to predict the optimal combination of machining parameters to achieve a higher fractal dimension. The predicted optimal condition is seen to be in close agreement with experimental results. Scanning electron micrography of the machined surface reveals that the combined ANN-GA method can significantly improve the surface texture produced from WEDM by reducing the formation of re-solidified globules.<\/jats:p>","DOI":"10.3390\/ma12030454","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T11:19:58Z","timestamp":1549019998000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Modeling and Optimization of Fractal Dimension in Wire Electrical Discharge Machining of EN 31 Steel Using the ANN-GA Approach"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4942-722X","authenticated-orcid":false,"given":"Arkadeb","family":"Mukhopadhyay","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India"}]},{"given":"Tapan Kumar","family":"Barman","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1538-0646","authenticated-orcid":false,"given":"Prasanta","family":"Sahoo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India"}]},{"given":"J. Paulo","family":"Davim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Davim, J.P. (2016). Response surface modelling of fractal dimension in WEDM. Design of Experiments in Production Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-319-23838-8"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.ijmachtools.2004.04.017","article-title":"State of the art in wire electrical discharge machining (WEDM)","volume":"44","author":"Ho","year":"2004","journal-title":"Int. J. Mach. 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