Friedrich Bleicher

Manufacturing-cell digital twins increasingly combine independently developed services that can influence shared physical resources. When several state-changing services are individually admissible but request incompatible values for the same control endpoint, runtime governance is required before execution. This work proposes a governance model that represents service–resource bindings, service and resource constraints, runtime context, arbitration-policy bindings, and enforcement relations for composed manufacturing-cell DTs. State-changing requests are evaluated individually before compatible requests are coalesced and incompatible admissible requests are resolved through a resource-specific governance policy. The model is implemented using ODRL for portable policy representation and OPA/Rego for executable runtime decisions. The evaluation examines functional validity, deterministic decision behavior, and request-to-enforcement latency using recorded CNC machining data and controlled replay scenarios in which two DT services share the feedOverride resource. All eight validation scenarios produced the expected decisions, including denial, delay, coalescing, and arbitration, while the simulated enforcement path produced at most one governed write per cycle. The OPA policy round trip showed a median latency of 4.962 ms (p95: 6.709 ms) and the complete governed request-to-enforcement path 15.003 ms (p95: 28.046 ms). The results demonstrate the feasibility of explicit runtime governance for competing DT-service actuation requests in the evaluated manufacturing-cell setting.

Thermal errors remain the most difficult type of manufacturing inaccuracy of cutting machine tools due to the challenges in predicting or avoiding them. Thermal compensation using joined submodels for all relevant machine subassemblies reduces the complexity for all submodels because they handle smaller, simpler geometries and less heat sources/sinks. Model training can be done using simulations, where the thermal errors of each subassembly can be computed. With enough training data, simple regression models suffice to predict the thermal error at the subassembly level. This method is demonstrated on a machine tool and validated using measurement data. The main drawbacks of the geometric compensation method are that it is difficult to train and optimize the subassembly models from measurement data of a specific machine. To solve this issue, the residual error is predicted with adaptive learning control using ARX models, which are trained from thermal measurements and enable the overall model to overcome differences between simulation and real machine. They also allow the model to adapt to changing thermal conditions and untrained thermal load cases, thereby increasing the overall accuracy and robustness significantly. This showed a reduction of the volumetric root mean square error from 44 to 7 µm. One final issue is the integration of thermal compensation models into the machine tool control. The paper describes different methods of realizing the control integration and challenges of obtaining real-time thermal position offsets.

The growing demand for automated production systems is driving continuous innovation in smart and data-driven manufacturing technologies. In the field of production metrology, the trend is shifting from using measurement laboratories to integrating measurement systems directly into production processes. This has led the Institute of Manufacturing Technology at TU Vienna together with its partners to develop a roughness measurement device that can be directly integrated into machine tools. Building on this foundation, this study tries to find applications beyond mere surface roughness assessment and demonstrates how the device could be applied in broader contexts of manufacturing process monitoring. By linking surface measurements with tool wear monitoring, the study establishes a correlation between surface roughness and wear progression of indexable inserts in milling. It demonstrates how in situ data can support predictive maintenance and the real-time adjustment of cutting parameters. This represents a first step toward integrating in situ metrology into closed-loop control in machining. The experimental setup followed ISO 8688-1 guidelines for tool life testing. Indexable inserts were operated throughout their entire service life while surface roughness was continuously recorded. In parallel, cutting edge conditions were documented at defined intervals using focus variation microscopy. The results show a consistent three-phase pattern: initially stable roughness, followed by a steady increase due to flank wear, and an abrupt decrease in roughness linked to edge chipping. These findings confirm the potential of integrated roughness measurement for condition-based monitoring and the development of adaptive machining strategies.

Highly automated and unmanned manufacturing requires process monitoring and in-process control to prevent damage to the workpiece or machine tool due to tool failure. The positioning of sensors close to the process is crucial to the success of such monitoring. One way of achieving this in machining applications is to equip toolholders with sensor systems. The Institute of Production Engineering and Photonic Technologies (IFT) has developed a sensory tool holder based on MEMS acceleration sensors that measures radial vibrations. The sensory tool holder system can be used to monitor production processes such as milling, drilling or tapping. In order to effectively use the signals from the sensory toolholder system for closed-loop control, it is necessary to convert these signals into characteristic values. This paper shows that wavelet decomposition of process-related acceleration signals is suitable for generating such a characteristic value for wear monitoring of end mills. Long-term roughing and finishing data from a real production process were analysed for this purpose.

Driven by the European Union’s commitment to achieving climate neutrality by 2050 and reducing EU emissions by 55% relative to 1990 levels by 2030, a critical assessment of manufacturing processes for their environmental impacts is necessary. During the strategic planning stage of product development and manufacturing, limited data is available to predict the environmental impacts across supply chains. However, decisions made at this phase significantly influence these impacts. This paper presents a data-provision method that aims to support decision-making in product development with quantifiable metrics of ecological sustainability. This is achieved through predicting carbon footprints, while leveraging the European data infrastructure Gaia-X, which enables secure service offerings and data sharing across the supply chain. The prediction service is implemented as a web application that facilitates the input and variation of hypothetical production scenarios. In a specific use-case, the method is applied to the supply chain of an injection-moulded cup. This concept offers a vision for carbon-optimized product development, enabling significant carbon avoidance in later production stages and showcasing the influence of different production parameters on the supply-chain carbon footprint.