During the joining of two metal sheets by welding, a process-specific geometry of the weld is created. The local geometry of the created weld has a decisive influence on its fatigue strength. This is due to stress concentration at the geometric notches. In this paper, a process known from mechanical engineering called deep rolling is applied on butt welds. The influence on the local weld geometry and the local stress concentration after deep rolling is investigated. Additionally, a novel automated measurement system using optical laser line scanning is presented. The system is qualified for the evaluation of the local weld geometry regarding its flank angles and toe radii. The presented investigations show that the deep rolling process influences the stress concentrations determined by 2D-FE-simulations using real scan data. A correlation between the difference in toe radii or local notch stresses before and after deep rolling and the initial flank angle was found. This indicates that there are process and geometry specific conditions for the successful application of the deep rolling process.

The automated process chain of an unmanned production system is a distinct challenge in the technical state of the art. In particular, accurate and fast raw-part recognition is a current problem in small-batch production. This publication proposes a method for automatic optical raw-part detection to generate a digital blank shadow, which is applied for adapted CAD/CAM (computer-aided design/computer-aided manufacturing) planning. Thereby, a laser-triangulation sensor is integrated into the machine tool. For an automatic raw-part detection and a workpiece origin definition, a dedicated algorithm for creating a digital blank shadow is introduced. The algorithm generates adaptive scan paths, merges laser lines and machine axis data, filters interference signals, and identifies part edges and surfaces according to a point cloud. Furthermore, a dedicated software system is introduced to investigate the created approach. This method is integrated into a CAD/CAM system, with customized software libraries for communication with the CNC (computer numerical control) machine. The results of this study show that the applied method can identify the positions, dimensions, and shapes of different raw parts autonomously, with deviations less than 1 mm, in 2.5 min. Moreover, the measurement and process data can be transferred without errors to different hardware and software systems. It was found that the proposed approach can be applied for rough raw-part detection, and in combination with a touch probe for accurate detection.

In modern production, Wire Arc Additive Manufacturing (WAAM) is becoming an essential technology for manufacturing complex components. However, the complexity of planning such processes constrains their widespread use in production cycles. Using various numerical simulation approaches allows for the investigation of resulting geometries with respect to process parameters, reducing the need for experiment-based process planning. Similar to various subtractive processes, there is increased interest in integrating simulation approaches into digital twin applications for planning and optimization of WAAM processes. This requires dynamic geometry mapping and simulation time comparable to the process duration. In this paper, a numerical simulation employing a Dexel-based geometry representation and a model for single-bead geometry parameter prediction is investigated as a vital alternative to Finite Element Method (FEM)-based simulations. The focus lies on the accuracy of the simulated components with respect to the simulation settings, the time needed for it to complete, and the degree of compliance between the simulated and produced multi-layer structures. Using optimized simulation settings achieves an accuracy loss of under 7% due to geometry discretization, with a simulation time that is approximately 37% faster than the process duration. The simulated components closely correspond to the experimental ones in terms of width and height, with a volumetric similarity ranging from 63.3% to 88.8%.

Digital Twin Systems (DTSs) are increasingly recognized as enablers of data-driven manufacturing, yet many implementations remain limited to monitoring or visualization without closed-loop control. This study presents a fully integrated DTS for CNC milling that emphasizes real-time bidirectional coupling between a real machine and a virtual counterpart as well as the use of machine-native signals. The architecture comprises a physical space defined by a five-axis machining center, a virtual space implemented via a dexel-based technological simulation environment, and a digital thread for continuous data exchange between those. A full-factorial simulation study investigated the influence of dexel density and cycle time on engagement accuracy and runtime, yielding an optimal configuration that minimizes discretization errors while maintaining real-time feasibility. Latency measurements confirmed a mean response time of 34.2 ms, supporting process-parallel decision-making. Two application scenarios in orthopedic implant milling validated the DTS: process force monitoring enabled an automatic machine halt within 28 ms of anomaly detection, while adaptive feed rate control reduced predicted form error by 20 µm. These findings demonstrate that the DTS extends beyond passive monitoring by actively intervening in machining processes; enhancing process reliability and part quality; and establishing a foundation for scalable, interpretable digital twins in regulated manufacturing.

Due to impacts and constant stress, the leading edges of aircraft engine blades often lose their shape, while the other parts of the blade are still functional. This results in unnecessary performance losses. Currently, there is no method for a fast and effective repair process as the initial shape of the blade cannot be restored. This paper presents an automated re-contouring process chain for leading edges without prior material application. Thus, it is a sustainable approach to extend the lifespan until an energy-consuming welding process can be performed. It consists of an in-machine scanning process to obtain information about the worn shape, a subsequent target model generation based on the worn shape, and an automated process planning. The process chain is evaluated using a universal, leading edge workpiece. The results show that the target requirements for shape and smoothness are fulfilled.