This paper describes the transformation of a rolling mill aggregate from a stand-alone solution to a fully integrated cyber physical production system. Within this process, already existing load cells were substituted and additional inductive and magnetic displacement sensors were applied. After calibration, those were fully integrated into a six-layer digitalization architecture at the Smart Forming Lab at the Chair of Metal Forming (Montanuniversitaet Leoben). Within this framework, two front end human machine interfaces were designed, where the first one serves as a condition monitoring system during the rolling process. The second user interface visualizes the result of a resilient machine learning algorithm, which was designed using Python and is not just able to predict and adapt the resulting rolling schedule of a defined metal sheet, but also to learn from additional rolling mill schedules carried out. This algorithm was created on the basis of a black box approach, using data from more than 1900 milling steps with varying roll gap height, sheet width and friction conditions. As a result, the developed program is able to interpolate and extrapolate between these parameters as well as different initial sheet thicknesses, serving as a digital twin for data-based recommendations on schedule changes between different rolling process steps. Furthermore, via the second user interface, it is possible to visualize the influence of this parameters on the result of the milling process. As the whole layer system runs on an internal server at the university, students and other interested parties are able to access the visualization and can therefore use the environment to deepen their knowledge within the characteristics and influence of the sheet metal rolling process as well as data science and especially fundamentals of machine learning. This algorithm also serves as a basis for further integration of materials science based data for the prediction of the influence of different materials on the rolling result. To do so, the rolled specimens were also analyzed regarding the influence of the plastic strain path on their mechanical properties, including anisotropy and materials’ strength.

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing.

The shot peening process is a common procedure to enhance fatigue strength on load-bearing components in the metal processing environment. The determination of optimal process parameters is often carried out by costly practical experiments. An efficient method to predict the resulting residual stress profile using different parameters is finite element analysis. However, it is not possible to include all influencing factors of the materials’ physical behavior and the process conditions in a reasonable simulation. Therefore, data-driven models in combination with experimental data tend to generate a significant advantage for the accuracy of the resulting process model. For this reason, this paper describes the development of a grey-box model, using a two-dimensional geometry finite element modeling approach. Based on this model, a Python framework was developed, which is capable of predicting residual stresses for common shot peening scenarios. This white-box-based model serves as an initial state for the machine learning technique introduced in this work. The resulting algorithm is able to add input data from practical residual stress experiments by adapting the initial model, resulting in a steady increase of accuracy. To demonstrate the practical usage, a corresponding Graphical User Interface capable of recommending shot peening parameters based on user-required residual stresses was developed.

The nickel-based superalloy Allvac 718Plus was developed in order to combine formability and cost advantages of Alloy 718 with high temperature strength of Alloy 720. Typical applications for this new material are turbine disks that are produced by closed die forging, e. g. by screw pressing. The final mechanical properties are strongly related to the microstructure, which forms during hot forming. It is therefore of great interest to model both the dynamic and static grain structure development during processing. In this work, dynamic and static recrystallization as well as normal grain growth models are described for this material. The calculated grain structure evolution in Allvac 718Plus is compared to that of Alloy 718 in the typical process range of disk forging. Additionally, both the experimental program and structure analysis methodology are specified.

The characterization of mechanical vibrations in a hammer forging process is a tremendously important parameter for machine design and production engineering. The dynamic response of a forging hammer to the reaction forces is affected by material behaviour, time, spring-damper system and foundation. In this research firstly, the effect of mass ratio and coefficient of restitution on the forging efficiency were theoretical characterized. The interesting influence of anvil initial velocity on the forging efficiency is also analytically presented. The mechanical vibrations of a LASCO HO U-315 hammer were experimentally investigated. Two steel grades a S355 and a 42CrMo4 were used to forge trial parts. The velocity of the ram and acceleration of the anvil during a hot die forging process were measured using a laser velocity meter type LSV-2000-45. The influences of forging time, coefficient of restitution, energy loss and time interval (delay) between blows on the efficiency of the forging process were examined. The energy loss before die contact was determined to be approximately 10%. The investigations also showed that a variation of the time interval between blows within the usual range has no effect on the intensity of the vibrations of the anvil nor on the energy loss of the hammer. The dependence of the free damped vibrations of the anvil on machine stiffness, damper coefficient and mass of machine has been confirmed. Additionally, the loss of energy due to hammer movement as well as the free damped mechanical vibrations of the anvil were theoretically analysed in order to verify the experimental findings. Theoretical analysis showed an anvil initial velocity of approximately 0.2 m/s results in a 4% increase of forging efficiency. A good agreement between the experimental and theoretical results was observed.

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.