Alexander Brosius

Automated operation-sequence planning for 3 + 2-axis computer numerical control (CNC) milling remains challenging because material removal changes both feature accessibility and the surfaces available for fixturing. This paper introduces an explicit state-transition formulation for constructing a solver-independent planning space from evolving workpiece geometry. The workpiece state combines volumetric material occupancy, surface-state availability, and completed machining features; it determines geometry-induced precedence and the availability of surfaces for workpiece fixturing. These relations are integrated with admissible tools, predefined tool-access directions, and machining-related cost contributions before optimisation begins. An Evolving Ant Colony System (EACS) algorithm then searches the resulting candidate space. Evaluation uses a 24-part benchmark with 58 EACS runs and 1000 random admissible samples per part. Functional and geometric verification covers precedence construction, 573 fixturing situations, and model-internal clamping-placement comparison. Compared with random admissible sampling, EACS achieved lower mean objective values for all 24 parts, with complete observed distributional separation for 23 parts. This comparison does not establish superiority over alternative optimisation algorithms. Four experienced CAM practitioners additionally assessed the generated plans for technological plausibility. The results demonstrate how evolving geometry can be converted into explicit state-dependent admissibility conditions, while mechanical fixture validation and scalability remain open limitations.

Deep rolling can effectively increase the very high cycle fatigue strength of gear shafts. However, selecting the optimal deep rolling force and feed is challenging, as hardening, residual stress state and surface roughness interact in complex ways and preliminary testing is time-consuming and costly. This study provides material-specific recommendations regarding deep rolling force and feed based on theoretical and experimental investigations. Hertz’s theory is used to determine the contact conditions during the rolling process in order to evaluate the local pressures and overlap factors. The experimental part comprises deep rolling and fatigue strength tests. The deep rolling tests on round bars indicate the material-dependent maximal values for contact pressure and overlap factor, taking into account the resulting surface roughness, which constitutes the upper limit of the process window. Residual stress measurements and staircase tests on notched specimens allow the estimation of the lower pressure limit ensuring the intended fatigue strength improvement. The objective of this work is to introduce a material-specific process window for the deep rolling of shafts as a novel contribution and to offer practical guidance on setting optimal machine parameters.

Modeling metal forming processes, and specifically ring rolling as considered in this research, is crucial for process design and the development of new product variants. Although finite element simulations and analytical modeling are common practices to perform process predictions, they can still present limitations such as high computational time, adherence to reality, and modeling accuracy. Trying to address all the above, this research proposes a novel generative machine learning solution for the creation of accurate and physics-compliant synthetic data for the ring rolling process, enabling almost real time and accurate process modeling within latent space. The proposed data generation method is based on multivariate generative adversarial network (GAN) combined with a physics-guided neural network (PGNN) to incorporate constraints relevant to the ring rolling process and integrate them into an auxiliary loss granting physical consistency. The proposed GAN architecture is then annotated as a physics-guided auxiliary GAN (PG-A-GAN). The physical constraints introduced are relevant to an analytical slip-line model for the force, a surrogate model for the torque, volume consistency, and an analytical model for the time–diameter relationship. Considering the duality of the physical modeling approach for the force and torque, where data-driven machine learning is stirred by analytical models, the proposed approach is defined as analytical transfer learning. Findings reveal that employing PGNNs in the GANs learning process improves physical loss in data generation, combined with a slight increase in data distribution similarity with respect to experimental instances. To enable modeling of specific and customized rolling instances, a condition based on the final sought ring geometry was introduced, within an auxiliary classifier generative adversarial network (ACGAN) framework. The proposed architecture allows the generation of multivariate, physically constrained, rolling time series and highlights the feasibility of such a modeling approach within the latent space and might be extended to other manufacturing processes by adapting modeling and retraining the proposed architecture.

Press-fit connections are widely used shaft-hub connections in gearbox design. Rising power density and cost-efficiency requirements necessitate improved utilisation of load-bearing capacity in critical shaft regions. Deep rolling offers an effective means to introduce beneficial residual stresses and enhance fatigue strength while being easily integrated into manufacturing processes. Designing deep rolled shafts commonly relies on integral approaches such as DIN 743 or the FKM guideline, which model strengthening through the surface hardening factor K _V . Differentiated approaches, particularly the FVA guideline, incorporate the actual residual stress state and therefore promise higher predictive accuracy. This study evaluates these concepts through extensive experiments on deep rolled press-fit connections with and without undercuts under rotating bending, cyclic torsion, and rotating bending with superimposed quasi-static torsion. The results show notable discrepancies between the calculation methods. The FVA guideline provides the most accurate predictions of component fatigue safety, whereas integral concepts exhibit systematic deviations due to their insufficient consideration of axial residual stresses—identified as the primary mechanism driving strength increases. Moreover, torsional loading yields significantly lower strengthening potential than rotating bending, reflecting the different effectiveness of residual stresses in the critical cross-section. The findings demonstrate the advantages of differentiated approaches for assessing deep rolled press-fit connections and provide a robust experimental basis and supporting simulation models for improving future fatigue design methodologies.

Sheet metal components with complex geometries are typically recycled by remelting. Direct remanufacturing necessitates the flattening of parts, which requires the implementation of cuts to facilitate unwinding. The exact positioning of these cuts is a complex planning task, because several influencing factors can be considered, such as material usage, ease of flattening, or minimal forming required. This study presents a geometry-based concept addressing this challenge and demonstrates its use for a test geometry. The finite element method is applied to simulate the flattening process of the resulting sections, and the results are evaluated in terms of planarity and induced plastic strain. The findings of the present work indicate a discernible dependency of results on the selection of the flattening directions. In particular, curved areas impact the induced plastic deformation and springback of flattened sections. This is a crucial consideration when planarity is prioritised over material utilisation.

The growing prevalence of aluminum sheets in future body-in-white concepts highlights the necessity for unceasing developments in joining techniques. This necessity arises from the distinctive characteristics of aluminum in comparison to conventional steel components. A significant challenge is the optimization of electrode lifespan in resistance spot welding, a process that is widely employed due to its reliability. The brief operational lifespan of electrodes during aluminum welding can be attributed to the insulating properties of the aluminum oxide layer that forms on the surface of the metal. One potential solution to this problem is to mechanically fracture the oxide layer prior to welding. The study investigates the influence of different relative movement configurations between electrodes and sheets on electrode wear and contact resistance during spot welding processes. Experimental setups with varying movement types—rolling frictionless, rolling with friction, translational, and stationary—were analyzed to determine their impact on sheet surface, surface shear stresses, and electrode wear. Confocal microscopy revealed noticeable surface changes depending on the movement configuration, with translational and rolling with friction movements exhibiting noticeable abrasive effects. Simulations supported experimental findings, highlighting variations in shear stress distribution and oxide layer disruption.