Robot-based machining systems can be employed in a wide variety of workspaces. Industrial robots are easily adaptable to different machining tasks, and thanks to versatile end effectors, they can perform a broad range of processes. Potential applications include mechanical machining, assembly, and testing tasks.

At the same time, the use of robotic machining systems introduces technical challenges. Compared to conventional machine tools, industrial robots exhibit lower absolute and repeatable positioning accuracy. Furthermore, static and dynamic compliance forces, which depend heavily on the robot’s pose, can negatively affect process stability. These system-related characteristics complicate precise path planning and increase susceptibility to dynamic effects such as chatter and vibrations during machining.

To fully exploit the potential of robot-assisted manufacturing, application-specific strategies are required to compensate for these system-related limitations.

In many cases, full automation, for example through the use of industrial robots, is not an economically viable solution. One reason for this is limited accessibility and the need to parallelize different work steps within the same manufacturing system. Under these conditions, machining can only be performed using manually guided tools. In manual drilling, the position and angle relative to the workpiece, as well as the process execution. Therefore, the resulting machining quality are influenced by the operator. The experience level of the personnel, along with external factors such as ergonomics and individual working methods, leads to high process variability, which is reflected in fluctuations in machining quality. Additionally, process monitoring and documentation are more challenging in manual operations. Process parameters, such as the feed rate in manual drilling, are not clearly defined and are difficult to measure. At the IPMT, approaches are being developed to monitor manual drilling and support operators in their machining tasks.

In contrast to fully manual drilling machines, semi-automatic drilling feed units (ADU) are manually positioned and locked in a drilling template but execute the machining task automatically according to a predefined program. Although process parameters can be specified, the compact design and relatively low drive power of these machines can negatively affect adherence to the correct parameters. Local clamping within the drilling jig promotes deflections and vibrations in the machining system. The drilling tools used must be able to withstand these conditions. The IPMT is therefore engaged in the design and experimental investigation of drilling tools specifically for this type of machine.

Digital support for workers is playing an increasingly important role in improving manual and semi-automated manufacturing processes. Information systems can provide targeted real-time assistance to operators during machining tasks through visual, acoustic, or tactile signals. This may include displaying machining steps, tolerances, or safety-relevant information. A central element of such systems is process monitoring, which enables continuous recording and evaluation of process data, allowing quality deviations to be detected and corrected at an early stage.

As part of process monitoring, external systems such as machine localization can be employed to ensure compliance with process parameters and positions stored in the information system. The IPMT is researching real-time monitoring of manual drilling processes and the integration of planning and process data through precise geolocation of the machining equipment.