Recent research in manufacturing increasingly focuses on energy efficiency, sustainable production, automation, and digitalization, driven by economic and environmental pressures. This progress is underpinned by digital technologies, advanced optimization methods, and smart manufacturing concepts, which collectively redefine the capabilities of modern production systems. Globalization and product individualization impose intensified competition, shortened lifecycles, and greater variability of workpieces. Conventional production systems cannot adequately respond to such dynamics. Within this context, autonomous machine tools constitute a key enabler, capable of independently planning, monitoring, and optimizing machining operations based on digital models and real-time data. This contribution reviews detailed solutions relevant for autonomous manufacturing and proposes a conceptual structuring of an autonomous manufacturing cell and its subsystems, outlines essential functional components, and discusses hierarchical control loops in the context of RAMI 4.0. Integrating industrial robots and mobile manipulators further enhances flexibility by automating material handling, thereby establishing machining cells with minimal human intervention.

Developing advanced hard coatings is crucial for improving machining performance. This study evaluates a newly created (Ti,Al,Ta,Ce)N coating realized by physical vapor deposition. Coated cemented carbide inserts were evaluated in dry longitudinal turning on C45E, benchmarked against TiN and (Ti,Al) at two cutting speeds (90 and 300 m center dot min-1) and two feeds (0.1 and 0.2 mm center dot rev-1). Tool wear, cutting forces, and rake face temperatures were monitored. At the cutting parameters of 300 m center dot min-1 and 0.2 mm center dot rev-1, the (Ti,Al,Ta,Ce)N coating outperformed (Ti,Al)N at a tool life of 750 m, reducing tool wear by 73 % and cutting forces by 11 %.

Temperature-induced geometric errors in manufacturing processes are a critical challenge for ensuring high precision of machined parts. These errors are largely influenced by the thermal behavior of machine components during operation, such as machine beds, travelling columns, fixtures and spindle units. This study presents the development and integration of an in-process temperature measurement system designed to monitor the thermal conditions of a machining center during the manufacturing process operated in a real production environment. The system incorporates temperature sensors placed in key areas of the machine tool structure, capturing the temperature field that impacts the machine tool components directly. The focus is put on the system's design, including sensor placement, wiring, data acquisition, and the integration of performance data gathered from the CNC such as spindle speed, feed rates and axis drive power consumptions. The data acquisition is supported by a post-process measurement of a geometrical feature on the machined workpieces. In a first step, the collected temperature and machine performance data are analyzed to identify patterns that contribute to the predication of thermal distortions, resulting in a geometric error. These results show a significant enhancement of machining accuracy and offer a promising methodology in precision machining for improving spatial thermal process stability by enabling adaptive control based on real-time thermal compensation feedback.

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.

Aerospace industry requires the production of complex, multi-functional components with very low geometrical tolerances using difficult to machine materials such as Ti-6Al-4V. One of the main challenges when machining such materials is the resulting part distortion after milling due to the release of residual stresses generated in previous manufacturing steps such as forging or heat treatment. To prevent costly scrap parts and manual rework, finite element method (FEM) can be utilized to predict resulting part distortion during the machining process development phase. This paper presents a novel approach for part distortion simulation by directly integrating relevant machining data from computer aided manufacturing (CAM) system for the FEM using Boolean subtraction operations. A developed software interface enables a step-by-step mechanical material removal simulation providing manufacturers with an efficient and flexible tool for automated process evaluation. The approach is implemented in a laboratory setup using commercial CAM and FEM systems and evaluated by using aerospace relevant demonstration parts.

Directed Energy Deposition (DED) as an additive manufacturing (AM) method offers great potential across various applications. However, challenges persist in controlling thermal history, grain structure, distortion, and residual stresses when manufacturing. Numerical simulation of the DED process enables prediction of these characteristics and supports first-time-right manufacturing. In most cases, computer-aided design (CAD) models of the post-machined component are used for toolpath planning and simulation. Since AM parts are produced layer by layer, their as-built geometry deviates from the idealized CAD model due to process-specific effects. This mismatch introduces errors in process simulations. To address this problem, a path-based geometry modeling tool is introduced that reconstructs a geometry closely approximating the as-built part, based on the actual toolpath and deposition bead dimensions. This model more accurately represents the final part’s volume and geometry. For thermal process simulation, material deposition is also modeled based on the same toolpath, establishing a unified workflow that uses standardized toolpath data for both geometry creation and thermal analysis. This ensures independence from specific machine types and enables early-stage simulation. The approach is validated through thermal simulations and experimental measurements, demonstrating high agreement and confirming that the proposed geometry modeling method improves the prediction of process parameters such as correct dwell times or interlayer temperatures.

Automatic repair of damaged metallic components using Wire-Arc Additive Manufacturing (WAAM) is a crucial instrument for sustainable manufacturing. Optical 3D geometry acquisition systems and reverse engineering algorithms are major cornerstones of this proposed WAAM process chain, however, challenges remain regarding point cloud registration for the resulting large data sets. This paper presents point cloud stitching and matching approaches using descriptors, thus improving the feasibility regarding computational time. The proposed algorithms are tested and validated in a laboratory setup consisting of a 6-axis industrial welding robot including an integrated 3D geometry acquisition system based on structured-light projection.

In volatile markets, resilient supply chains and shopfloors are essential to mitigate the impact of disruptions, such as crises, machine failures or quality issues, which result in significant costs. To address these challenges, information about products, processes and resources is required to design, test, and deploy resilience assessment and reconfiguration tools. Nowadays, this information is intended to be made available through data spaces and ecosystems, which necessitates preserving the data sovereignty of the respective companies involved. The architecture proposed by the Flex4res project accommodates these requirements. The implemented pre-pilot use case allows testing and eases the transition for companies.

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.

Wire-Arc Additive Manufacturing (WAAM) has been established as a new technology for industrial use-cases such as low-lot size manufacturing or part repair services. A key aspect when developing such WAAM processes is thermal management during the layer-by-layer metal deposition. To maintain a stable welding process in-depth knowledge about the heat distribution is required. Thus, predicting the heat flux for a given part geometry already in the process development stage using Computer-Aided-Manufacturing Systems (CAM) would be beneficial. However, current state-of-the-art approaches are computationally expensive and time intensive. Therefore, they are hardly applicable for WAAM applications. In this paper, a dexel-based metal cutting and deposition simulation is combined with a temperature prediction model, which is integrated in the toolpath planning algorithm when defining a build-up strategy for a given part geometry. The approach is based on a temperature prediction algorithm, that calculates temperature fields for deposited material volume considering basic material properties. Calculated temperature fields can be utilized for optimizing welding toolpath to achieve stable process conditions across the part geometry.

The production of large, near-net-shape components using additive manufacturing with plasma metal deposition (PMD) is gaining increasing importance. In this process, the arc plasma melts the wire material locally and builds up the geometry layer by layer through deposited metal droplets. Despite the advantages, such as flexibility in geometry design and Efficient material usage, achieving “First Time Right” production remains a challenge. Uncontrollable dynamic effects in the process often lead to deviations in layer height, which can even result in process termination. This paper presents a process application to compensate for these deviations by automatically adjusting the wire feed. Continuous monitoring of the arc voltage signal during the PMD process enables real-time detection of layer height variations. An automated data acquisition system is used to identify the process behavior to design the control algorithm. Experimental results demonstrate that the proposed layer height control significantly enhances process stability, advancing the PMD technology closer to achieving ”First Time Right” additive manufacturing.

Vitrified bonded aluminum oxide grinding wheels are widespread in use for many applications in grinding, such as internal plunge grinding. However, there are challenges when it comes to the measurement, analysis and (geometric) modeling of their topography, which is crucial to understand and model the influence of the topography on the process behavior. Methodological advances allow for the detailed digitization of the topography using optical profilometry despite the challenging optical properties of these grinding wheels. Based on the digitized grinding wheel topography, methods are presented to process the measurement data in order to create a representative set of geometric cutting edge models. This set is subsequently used to generate a full-sized virtual grinding wheel with realistic topography. Using established methods in an efficient implementation that scales to many CPU-cores, the interaction between the workpiece model and each individual cutting edge can be calculated at meso-scale. Therefore, it is possible to analyze for example the chip thickness or the material removal rate per cutting edge. Furthermore, additional models can be applied, based on the analysis of the engagement situation, which is demonstrated using a cutting force model.

The objective of this work is to compare three different hybrid metal additive-subtractive manufacturing processes to enable decision-making and future research. This was achieved by producing the same artifact out of 316L stainless steel with - Friction Surfacing, Wire Arc Additive Manufacturing, and Laser Powder Directed Energy Deposition. Key process outcomes including cycle time, resource consumption, distortion, energy consumption, microstructure, hardness, and tensile strength were analyzed. It was observed that: Friction Surfacing demonstrates higher hardness due to its solid-state nature; Wire Arc Additive Manufacturing offers lower cycle times and resource consumption; and Directed Energy Deposition provides near net shape geometries. (c) 2025 CIRP. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Shop floor operations are affected by uncertainties and disruptions. These can come from the supply chain causing shortages of materials, worker unavailability due to, e.g., unforeseen pandemics, and spontaneous machine breakdowns. To address these challenges, resilient manufacturing systems are essential. However, there is a lack of practical methods for quantifying how well a shop floor withstands and recovers from such disruptions and assessing its level of resilience. Therefore, we propose a new approach for evaluating the resilience of the shop floor by assessing the production schedule and its underlying scheduler. Our Severity of Failure (SoF) method assesses each scheduled task by calculating the impact of its failure on the entire production schedule and combining it with the probability of the failure occurring. The method has been validated by assessing production schedules generated by various dispatching rules, demonstrating both its practical applicability and its potential to evaluate the resilience of different scheduling algorithms. Furthermore, the beneficial effect of buffer times on resilience was also evaluated.

Collaboration in manufacturing networks is crucial to maximize the impact of digitalization and leveraging collected data. Addressing three key challenges overcoming data silos, achieving semantic interoperability and protecting intellectual property is essential. A promising solution is the combination of Asset Administration Shells and data space technologies. This systematic literature review investigates implementations of these two concepts in manufacturing, evaluating use cases based on their technological readiness levels and the technologies employed. The findings aim to guide researchers and practitioners and identify future research directions.

Aluminum casting products are a key component in aerospace and mobility. Since aluminum is lightweight and has a high strength-to-weight ratio, it also has many applications in the electrification of drive systems. The production of stator housing for electric drives, for example, is a combination of casting, heat treatment, and machining processes. Stator housings are thin-walled parts subjected to heavy distortions after machining the main stator housing central boring since residual stresses stemming from the casting and heat treatment process are released. Classical process improvement techniques solely focus on each chain in the process link: casting, heat treatment, and machining. Due to new frameworks and digital data transfer platforms, data transfer from and to other process chain links is possible. We present a holistic approach for process improvements along the whole process chain using digital twins, which are built from experiments and simulations of each process link.

Specific modeling techniques are necessary to accurately capture the physical mechanisms for simulating the macroscale thermo-mechanical behavior of an additive directed energy deposition (DED) process. When using the finite element method (FEM) to simulate the DED process, the material deposition typically requires the activation of grouped elements along the deposition path. G-code-based software interfaces usually handle the element grouping (clustering) procedure according to the planned deposition path. However, modern robotic systems often use individualized proprietary controller languages for DED, meaning no generic G-code is available for simulation. A concept that handles the element grouping mechanism without relying on G-code is proposed. The method uses a standardized neutral tool path information from the computer aided manufacturing (CAM) system to automate element grouping. This study demonstrates and analyzes the generally applicable approach for modeling a multi-axis deposition process. The implementation of pre-processing in the FEM as well as the mathematical formulations and methods required for automated element selection and grouping are described.

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.