A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions

As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs with...

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Autores principales:	Kriz George, Sathish Kannan, Ali Raza, Salman Pervaiz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machining AISI 630 machine learning Artificial Neural Network Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/a0cb98c7f64d4f539d5174fcf00d024b
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spelling	oai:doaj.org-article:a0cb98c7f64d4f539d5174fcf00d024b2021-11-11T18:12:45ZA Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions10.3390/ma142167171996-1944https://doaj.org/article/a0cb98c7f64d4f539d5174fcf00d024b2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6717https://doaj.org/toc/1996-1944As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs within the limitations of available resources. However, finite element simulations—the most common means to analyze and understand the machining of high-performance materials under various cutting conditions and environments—require high amounts of processing power and time in order to output reliable and accurate results which can lead to delays in the initiation of manufacture. The objective of this study is to reduce the time required prior to fabrication to determine how available inputs will affect the desired outputs and machining parameters. This study proposes a hybrid predictive methodology where finite element simulation data and machine learning are combined by feeding the time series output data generated by Finite Element Modeling to an Artificial Neural Network in order to acquire reliable predictions of optimal and/or expected machining inputs (depending on the application of the proposed approach) using what we describe as a backwards training model. The trained network was then fed a test dataset from the simulations, and the results acquired show a high degree of accuracy with regards to cutting force and depth of cut, whereas the predicted/expected feed rate was wildly inaccurate. This is believed to be due to either a limited dataset or the much stronger effect that cutting speed and depth of cut have on power, cutting forces, etc., as opposed to the feed rate. It shows great promise for further research to be performed for implementation in manufacturing facilities for the generation of optimal inputs or the real-time monitoring of input conditions to ensure machining conditions do not vary beyond the norm during the machining process.Kriz GeorgeSathish KannanAli RazaSalman PervaizMDPI AGarticlemachiningAISI 630machine learningArtificial Neural NetworkTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6717, p 6717 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machining AISI 630 machine learning Artificial Neural Network Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	machining AISI 630 machine learning Artificial Neural Network Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Kriz George Sathish Kannan Ali Raza Salman Pervaiz A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions
description	As machining processes are complex in nature due to the involvement of large plastic strains occurring at higher strain rates, and simultaneous thermal softening of material, it is necessary for manufacturers to have some manner of determining whether the inputs will achieve the desired outputs within the limitations of available resources. However, finite element simulations—the most common means to analyze and understand the machining of high-performance materials under various cutting conditions and environments—require high amounts of processing power and time in order to output reliable and accurate results which can lead to delays in the initiation of manufacture. The objective of this study is to reduce the time required prior to fabrication to determine how available inputs will affect the desired outputs and machining parameters. This study proposes a hybrid predictive methodology where finite element simulation data and machine learning are combined by feeding the time series output data generated by Finite Element Modeling to an Artificial Neural Network in order to acquire reliable predictions of optimal and/or expected machining inputs (depending on the application of the proposed approach) using what we describe as a backwards training model. The trained network was then fed a test dataset from the simulations, and the results acquired show a high degree of accuracy with regards to cutting force and depth of cut, whereas the predicted/expected feed rate was wildly inaccurate. This is believed to be due to either a limited dataset or the much stronger effect that cutting speed and depth of cut have on power, cutting forces, etc., as opposed to the feed rate. It shows great promise for further research to be performed for implementation in manufacturing facilities for the generation of optimal inputs or the real-time monitoring of input conditions to ensure machining conditions do not vary beyond the norm during the machining process.
format	article
author	Kriz George Sathish Kannan Ali Raza Salman Pervaiz
author_facet	Kriz George Sathish Kannan Ali Raza Salman Pervaiz
author_sort	Kriz George
title	A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions
title_short	A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions
title_full	A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions
title_fullStr	A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions
title_full_unstemmed	A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions
title_sort	hybrid finite element—machine learning backward training approach to analyze the optimal machining conditions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a0cb98c7f64d4f539d5174fcf00d024b
work_keys_str_mv	AT krizgeorge ahybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT sathishkannan ahybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT aliraza ahybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT salmanpervaiz ahybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT krizgeorge hybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT sathishkannan hybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT aliraza hybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions AT salmanpervaiz hybridfiniteelementmachinelearningbackwardtrainingapproachtoanalyzetheoptimalmachiningconditions
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A Hybrid Finite Element—Machine Learning Backward Training Approach to Analyze the Optimal Machining Conditions

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