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.

For the control of dynamic systems such as an Electro-Hydraulic Actuator (EHA), there is a need to optimize the control based on simulations, since a prototype or a physical system is usually not available during system design. In consequence, no system identification can be performed. Therefore, it is unclear how well a simulation model of an EHA can be used for multicriteria optimization of the position control due to the uncertain model quality. To evaluate the suitability for control optimization, the EHA is modeled and parameterized as a grey-box model using existing parameters independent of test bench experiments. A method for multi-objective optimization of a controller is used to optimize the position control of the EHA. Finally, the step responses are compared with the test bench. The evaluation of the step responses for different loads and control parameters shows similar behavior between the simulation model and the physical system on the test bench, although the essential phenomena could not be reproduced. This means that the model quality achieved by modeling is suitable as an indication for the optimization of the control by simulation without a physical system.

Capturing data about manual processes and manual machining steps is important in manufacturing for better traceability, optimization, and better planning. Current manufacturing research focuses on sensor-based recognition of manual activities across multiple tools or power tools, but little on recognition within a versatile power tool type. Due to the strong influence of operator skill on process performance and consistency as well as many disturbance variables, activity recognition is a challenge in manual grinding. It is unclear how accurately manual activities can be recognized within one handheld grinder type across diverse trials. Therefore, this article investigates how manual activities can be recognized in diverse trials within an angle grinder type in a leave-one-trial-out cross-validation in comparison to classical cross-validation to identify the effect of diverse trials with four different classifies. An experimental study was conducted to collect measurement data with data loggers attached to two angle grinders, four manual activities with different abrasive tools, and three operators. Results show very good accuracies (97.68%) with cross-validation and worse accuracies (70.48%) with leave-one-trial-out cross-validation for the ensemble learning classifier. This means that recognition of the four chosen manual activities within an angle grinder is feasible but depends on how much the trial deviates from the reference training data. For further research on activity recognition in manual manufacturing, we propose the explicit consideration and evaluation of disturbance variables and diversity in data collection for the training of machine learning models.