Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
Sensors (Basel). 2021 Oct 28;21(21):7147. doi: 10.3390/s21217147.
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 (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.
工具力是手动手持动力工具磨削的决定性参数，可用于确定生产力、工作结果质量、振动暴露和工具寿命。一种确定工具力的方法是通过测量手持动力工具的操作参数来预测工具力。问题是，在手动磨削中，消费级传感器的工具力预测准确性仍不清楚。因此，本研究使用高斯过程回归检查了在四个不同应用中使用惯性测量单元、电流传感器和电压传感器的测量数据，在两种手持角磨机的三个方向上预测磨削法向力（rMAE = 11.44%，r = 0.84）和磨削切向力（rMAE = 18.21%，r = 0.82）以及应用的径向力（rMAE = 19.67%，r = 0.80）的准确性。对于三个测试应用，引导力的预测（rMAE = 87.02%，r = 0.37）仅在一定程度上是可行的。这项研究支持在实际应用中使用消费级传感器（如惯性测量单元）采集和评估手持动力工具的数据，从而为产品使用和产品开发带来新的潜力。
