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Kern, Thorsten A.; Matysek, Marc; Sindlinger, Stephanie

doi:10.1007/978-3-540-87644-1_9

Cited by 1 publication

(1 citation statement)

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“…Increasing the segment length from 0.1 s to 1 s, and, thus, predicting a force sampled at a frequency of 1 Hz, results in a partially worse accuracy. This can be explained by the fact that the force components of the tool force, which occur due to the user's motion, have a frequency of up to 10 Hz [34]. Therefore, these are included in the independent variable (operating parameters of the angle grinder) but not in the dependent variable (tool force) due to the filtering to 1 Hz.…”

Section: Application Mae [N] Rmae [%] R Mae [N] Rmae [%] R Mae [N] Rmae [%] Rmentioning

confidence: 99%

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

Dörr

Ott

Matthiesen

et al. 2021

Sensors

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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. 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.

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Section: Application Mae [N] Rmae [%] R Mae [N] Rmae [%] R Mae [N] Rmae [%] Rmentioning

confidence: 99%