In order to fully exploit the potential of the rapidly progressing digitalisation of technical systems, it is necessary to provide reliable and significant process and condition related data. In this context, solutions are especially aspired to allow a simple integration into the surrounding system and to influence it as little as possible. The main challenges regarding the measurement of process and condition data in the operation and control of technical systems as well as in test environments are identified and presented at the beginning of this article. A promising approach to meet the resulting requirements is the integration of sensory functions into simple standardised machine elements. In order to facilitate the discussion and interdisciplinary development of machine elements with sensory functions, an extension of the existing classification of mechatronic machine elements is introduced and illustrated with examples. The introduced classification takes into account the classification according to Stücheli and Meboldt and is based on a comparison of conventional and mechatronic machine elements on a functional level with regard to the function structure. As a result, the three classessensor carrying machine elements,sensor integrating machine elementsandsensory utilizable machine elementsare introduced and subsequently discussed in more detail on the basis of examples. Finally, an outlook is given on the main research areas with regard to the development of mechatronic machine elements. Key aspects include working principles and effects for application in mechatronic machine elements, system analysis with regard to load conditions, power supply of sensor and data processor in the environment of the machine element as well as data processing and signal transmission under typical environmental conditions of mechanical engineering.

Repetitive overhead work with a heavy load increases the risk for work-related shoulder disorders. Occupational exoskeletons supporting arm elevation are potential solutions to reduce that risk by lowering the physical strains on the shoulder. Many studies have reported a reduction in shoulder stress in various overhead tasks by using such exoskeletons. However, the support demand can vary in each phase of motion as well as in each individual task. This paper presents a laboratory study with five participants to evaluate the influence of the support level of an active shoulder exoskeleton in different motion phases (e.g., arm lifting, screw-in, and arm lowering of two overhead tasks) to identify the potential optimization of support at each phase. Results show that the support level of the exoskeleton should be adapted to the motion phase of the two chosen tasks. A higher support force is desired for the screw phase compared to the arm lifting and lowering phases, and the support level needs to be reduced immediately for arm lowering after the screw phase. The time for switching the support levels can be recognized by the electric current of the screwdriver.

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.