AISI 52100 steel is often used as material for highly loaded rolling bearings in machine tools. An improved surface integrity, which can be achieved by means of mechanical surface layer finishing, can avoid premature failure. One of these finishing processes is machine hammer peening (MHP) which is a high-frequency incremental forming process and mostly used on machining centers. However, the influence of robot-guided MHP processing on the surface integrity of AISI 52100 steel is still unknown. Therefore, the objective of this work is to investigate experimentally the robot-based influences during MHP processing and the resulting surface integrity of unhardened AISI 52100 steel. The results show that the axial and lateral deviations of the robot due to process vibrations are in the lower µm range, thus enabling stable and reproducible MHP processing. By selecting suitable MHP process parameters and thus defined contact energies, even ground surfaces can be further smoothed and a hardness increase of 75% in the energy range considered can be achieved. In addition, compressive residual stress maxima of 950 MPa below the surface and a grain size reduction to a surface layer depth of 150 µm can be realized.

Optimizing manufacturing processes to reduce scrap and enhance process stability presents significant challenges, particularly when multiple conflicting objectives must be addressed concurrently. As the number of objectives increases, the complexity of the optimization task escalates. This difficulty is further intensified in online optimization scenarios, where optimal parameter settings must be delivered in real time within active production environments. In this work, we propose a reinforcement learning-based framework for the multi-objective optimization of manufacturing parameters, demonstrated through a case study on pinion gear manufacturing. The framework utilizes the Multi-Objective Maximum a Posteriori Optimization (MO-MPO) algorithm to train a reinforcement learning agent. A high-fidelity simulation of the pinion manufacturing process is constructed in Simufact, serving both data generation and validation purposes. The agent’s performance is assessed using a hold-out test set along with additional simulations of the physical process. To ensure the generalizability of the approach, further validation is performed using open-source manufacturing datasets and synthetically generated data. The results demonstrate the feasibility of the proposed method for real-time industrial deployment. Moreover, Pareto-optimality is verified via half-space analysis, emphasizing the framework’s effectiveness in managing trade-offs among competing objectives.

-Influence of surface treatments on the fatigue strength of cross bores in shafts from EN-GJS700-2 UHLMANN Lars, DADGAR Mohammad, MÜLLER Martina, HERRIG Tim

Deep rolling is a mechanical surface treatment for the specific modification of edge zone properties. In this process, the surface is plastically deformed by a rolling element. The roughness, residual stresses and hardness in particular can be positively influenced. An external pressure unit is usually used to generate the forces required for plastic deformation. The acquisition costs associated with the pressure unit and the space required reduce the attractiveness of deep rolling. In this work, the possibility of generating the required forces by exploiting the rotational speed of machining center spindles for processing bores is shown.

Lightweight construction and performance requirements in the automotive industry have resulted in increased power density. At the same time, this increases the load on the workpieces. To counteract the resulting wear, either a more wear-resistant material may be applied or the functional surface may be specifically modified against wear. Machine hammer peening (MHP) is a process for such a targeted adjustment of the functional surface and the surface area properties. MHP is a mechanical surface treatment, which increases the wear resistance of the workpieces by introducing residual compressive stresses and work hardening as well as by smoothing or structuring the surface. To ensure an accurate adjustment of the surface properties, the influencing factors (material, process parameters and lubricant) and their interactions must be sufficiently well studied and understood. In particular, the influence of the lubricant on the surface area properties has not yet been adequately investigated. The objective of the work presented in this paper was to provide a deeper insight into the influence of the type of lubricant on the resulting surface properties in terms of roughness, residual stresses and hardness. The lubricant’s influence was investigated using a partial factorial experimental design. Additional factors investigated, besides the choice of lubricant, were stroke, distance of indentation and step over distance. The results show a strong influence of the lubricant selection, especially on the resulting surface roughness. For the same process parameters, a deviation of 540 % in the resulting surface roughness was measured between two surfaces machined with different lubricants.

Bipolar plates are key components of fuel cell systems, as they significantly determine efficiency, power density, and service life. In aerospace applications, their importance is further emphasized due to the dual requirement of corrosion resistance and strict weight reduction. Titanium Grade 1 combines low density and excellent corrosion resistance. However, its industrial application is limited by restricted formability. The aim of this paper is a systematic investigation of the forming behavior of Titanium Grade 1 foil material in order to define forming limits and derive manufacturing-oriented design recommendations for bipolar plates in aviation. Procedure. Sixteen distinct geometry features were developed to represent characteristic forming conditions. In addition to cross-section variations, the flow field angle was systematically altered to assess its influence on local stress and strain distribution. Furthermore, the key process parameters forming speed, forming force, and lubricant amount were varied to evaluate their impact on the forming quality. The assessment focused on form filling and material thinning. For this purpose, metallographic cross-sections were prepared, and optical 3D measurements were conducted using a Keyence system to precisely capture local wall thickness variations. Key findings Process parameters: The forming behavior of Titanium Grade 1 is strongly influenced by the applied forming force and lubrication. Form filling becomes sufficient only above 350 MPa (3.000 kN), while the lubricant amount is decisive for achievable forming depths due to the hydrostatic oil cushion effect. In contrast, forming speed shows no significant influence. Anisotropy remains a critical factor, particularly in 0° rolling direction, where premature thinning leads to fracture. Geometry parameters: Small radii are highly critical, while feature depth leads to expectedly higher thinning. Steeper flank angles improve form filling but at the cost of increased thinning. Pitch shows limited influence, although it may become relevant at very small values. Channel design is challenging, as sharp flow field angles consistently result in severe thinning and pose difficulties in tool manufacturing.