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Prediction of tool forces in manual grinding using consumer-grade sensors and machine learning

Dörr, Matthias; Ott, Lorenz; Matthiesen, Sven; Gwosch, Thomas

Abstract:
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 cutting with a cut-off wheel (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. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000139733
Veröffentlicht am 12.11.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktentwicklung (IPEK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000139733
Erschienen in Sensors
Verlag MDPI
Band 21
Heft 21
Seiten 7147
Schlagwörter inertial measurement unit; force estimation; data logger; tool forces; manual grinding; Gaussian process regression; artificial intelligence; hand-held power tool; angle grinder; machine learning regression
Nachgewiesen in Scopus
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