Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating...

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Autores principales:	Matthias Dörr, Lorenz Ott, Sven Matthiesen, Thomas Gwosch
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	inertial measurement unit force estimation data logger tool forces manual grinding Gaussian process regression Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e8a36498b5cb4235bbab6de2264a6770
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spelling	oai:doaj.org-article:e8a36498b5cb4235bbab6de2264a67702021-11-11T19:08:54ZPrediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning10.3390/s212171471424-8220https://doaj.org/article/e8a36498b5cb4235bbab6de2264a67702021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7147https://doaj.org/toc/1424-8220Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application <i>cutting with a cut-off wheel</i> (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.Matthias DörrLorenz OttSven MatthiesenThomas GwoschMDPI AGarticleinertial measurement unitforce estimationdata loggertool forcesmanual grindingGaussian process regressionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7147, p 7147 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	inertial measurement unit force estimation data logger tool forces manual grinding Gaussian process regression Chemical technology TP1-1185
spellingShingle	inertial measurement unit force estimation data logger tool forces manual grinding Gaussian process regression Chemical technology TP1-1185 Matthias Dörr Lorenz Ott Sven Matthiesen Thomas Gwosch Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
description	Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application <i>cutting with a cut-off wheel</i> (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.
format	article
author	Matthias Dörr Lorenz Ott Sven Matthiesen Thomas Gwosch
author_facet	Matthias Dörr Lorenz Ott Sven Matthiesen Thomas Gwosch
author_sort	Matthias Dörr
title	Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
title_short	Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
title_full	Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
title_fullStr	Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
title_full_unstemmed	Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
title_sort	prediction of tool forces in manual grinding using consumer-grade sensors and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e8a36498b5cb4235bbab6de2264a6770
work_keys_str_mv	AT matthiasdorr predictionoftoolforcesinmanualgrindingusingconsumergradesensorsandmachinelearning AT lorenzott predictionoftoolforcesinmanualgrindingusingconsumergradesensorsandmachinelearning AT svenmatthiesen predictionoftoolforcesinmanualgrindingusingconsumergradesensorsandmachinelearning AT thomasgwosch predictionoftoolforcesinmanualgrindingusingconsumergradesensorsandmachinelearning
_version_	1718431618256863232

Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning

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