Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortio...

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Autores principales:	Arminder Singh Walia, Vineet Srivastava, Prashant S Rana, Nalin Somani, Nitin Kumar Gupta, Gurminder Singh, Danil Yurievich Pimenov, Tadeusz Mikolajczyk, Navneet Khanna
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electric discharge machining (EDM) out-of-roundness tool shape decision tree random forest generalized linear model Mining engineering. Metallurgy TN1-997
Acceso en línea:	https://doaj.org/article/f6a2482ddea745439233f7a06ed7985a
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id	oai:doaj.org-article:f6a2482ddea745439233f7a06ed7985a
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spelling	oai:doaj.org-article:f6a2482ddea745439233f7a06ed7985a2021-11-25T18:21:04ZPrediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques10.3390/met111116682075-4701https://doaj.org/article/f6a2482ddea745439233f7a06ed7985a2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4701/11/11/1668https://doaj.org/toc/2075-4701In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.Arminder Singh WaliaVineet SrivastavaPrashant S RanaNalin SomaniNitin Kumar GuptaGurminder SinghDanil Yurievich PimenovTadeusz MikolajczykNavneet KhannaMDPI AGarticleelectric discharge machining (EDM)out-of-roundnesstool shapedecision treerandom forestgeneralized linear modelMining engineering. MetallurgyTN1-997ENMetals, Vol 11, Iss 1668, p 1668 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electric discharge machining (EDM) out-of-roundness tool shape decision tree random forest generalized linear model Mining engineering. Metallurgy TN1-997
spellingShingle	electric discharge machining (EDM) out-of-roundness tool shape decision tree random forest generalized linear model Mining engineering. Metallurgy TN1-997 Arminder Singh Walia Vineet Srivastava Prashant S Rana Nalin Somani Nitin Kumar Gupta Gurminder Singh Danil Yurievich Pimenov Tadeusz Mikolajczyk Navneet Khanna Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques
description	In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.
format	article
author	Arminder Singh Walia Vineet Srivastava Prashant S Rana Nalin Somani Nitin Kumar Gupta Gurminder Singh Danil Yurievich Pimenov Tadeusz Mikolajczyk Navneet Khanna
author_facet	Arminder Singh Walia Vineet Srivastava Prashant S Rana Nalin Somani Nitin Kumar Gupta Gurminder Singh Danil Yurievich Pimenov Tadeusz Mikolajczyk Navneet Khanna
author_sort	Arminder Singh Walia
title	Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques
title_short	Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques
title_full	Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques
title_fullStr	Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques
title_full_unstemmed	Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques
title_sort	prediction of tool shape in electrical discharge machining of en31 steel using machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f6a2482ddea745439233f7a06ed7985a
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Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques

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