Sustainability is one of the biggest values of today and for the future of our society; a responsible usage of material in every sector is fundamental to achieving sustainability goals. Aluminum alloys are some of the most promising materials in terms of strength and weight, but their production implies the emission of a high amount of CO₂. For that reason, the study and development of aluminum alloys with increasing scrap content play a central role in future applications. In the current study, two sheet-aluminum 6181 alloys with different scrap content were analyzed and compared with a 6181 alloy coming from primary production. The alloys were compared in terms of chemical composition, microstructure, tensile properties, and forming behaviors. The results showed that the alloys coming from secondary productions contained a higher amount of manganese, iron, and copper. The metallurgical and mechanical behaviors were very similar to those of the primary produced alloy. Nevertheless, a drop in formability was shown in the aluminum alloys containing a high scrap amount when stressed in a biaxial condition. The study demonstrated the viability of 6181 alloy production using a high scrap amount, highlighting the main difference with the same alloy coming from primary route production.

The digitalization of manufacturing processes offers great potential in quality control, traceability, and the planning and setup of production. In this regard, process simulation is a well-known technology and a key step in the design of manufacturing processes. However, process simulations are computationally and time-expensive, typically beyond the manufacturing-cycle time, severely limiting their usefulness in real-time process control. Machine Learning-based surrogate models can overcome these drawbacks, and offer the possibility to achieve a soft real-time response, which can be potentially developed into full close-loop manufacturing systems, at a computational cost that can be realistically implemented in an industrial setting. This paper explores the novel concept of using a surrogate model to analyze the case of the press hardening of a steel sheet of 22MnB5. This hot sheet metal forming process involves a crucial heat treatment step, directly related to the final part quality. Given its common use in high-responsibility automobile parts, this process is an interesting candidate for digitalization in order to ensure production quality and traceability. A comparison of different data and model training strategies is presented. Finite element simulations for a transient heat transfer analysis are performed with ABAQUS software and they are used for the training data generation to effectively implement a ML-based surrogate model capable of predicting key process outputs for entire batch productions. The resulting final surrogate predicts the behavior and evolution of the most important temperature variables of the process in a wide range of scenarios, with a mean absolute error around 3 °C, but reducing the time four orders of magnitude with respect to the simulations. Moreover, the methodology presented is not only relevant for manufacturing purposes, but can be a technology enabler for advanced systems, such as digital twins and autonomous process control.

Since the popularization of press hardening in the early noughties, die and tooling systems have experienced considerable advances, with tool refrigeration as an important focus. However, it is still complicated to obtain homogeneous cooling and avoid hot spot issues in complex geometries. Additive Manufacturing allows designing cavities inside the material volume with little limitation in terms of channel intersection or bore entering and exit points. In this sense, this technology is a natural fit for obtaining surface-conforming cooling channels: an attractive prospect for refrigerated tools. This work describes a pilot experience in 3D-printed press hardening tools, comparing the performance of additive manufactured Maraging steel 1.2709 to conventional wrought hot work tool steel H13 on two different metrics: durability and thermal performance. For the first, wear studies were performed in a controlled pilot plant environment after 800 hot stamping strokes in an omega tool configuration. On the second, a demonstrator tool based on a commercial tool with hot spot issues, was produced by 3D printing including surface-conformal cooling channels. This tool was then used in a pilot press hardening line, in which tool temperature was analyzed and compared to an equivalent tool produced by conventional means. Results show that the Additive Manufacturing technologies can be successfully applied to the production of press hardening dies, particularly in intricate geometries where new cooling channel design strategies offer a solution for hot spots and inhomogeneous thermal loads.

Hot stamping is a hot metal forming technology increasingly in demand that produces ultra-high strength parts with complex shapes. A major concern in these systems is how to shorten production times to improve production Key Performance Indicators. In this work, we present a Reinforcement Learning approach that can obtain an optimal behavior strategy for dynamically managing the cycle time in hot stamping to optimize manufacturing production while maintaining the quality of the final product. Results are compared with the business-as-usual cycle time control approach and the optimal solution obtained by the execution of a dynamic programming algorithm. Reinforcement Learning control outperforms the business-as-usual behavior by reducing the cycle time and the total batch time in non-stable temperature phases.

Press Hardening offers the possibility to obtain a wide range of mechanical properties through microstructural tailoring. This strategy has been successfully applied in thin sheet components, for instance, through differential cooling strategies. The application of these added value features to truck components implies adapting the process to the manufacture of thick sheet metal. This introduces an additional layer of complexity, but also opportunity, in a process where the final microstructure and, thus the mechanical performance is generated in the press shop. This work presents a study on optimizing the crash worthiness and impact energy absorption on a press hardened thick 22MnB5 steel sheet. Different microstructure design strategies have been studied, including ferrite-Pearlite (representative of a differential heating and austenitization strategy), in-die generated Bainite (representative of differential cooling) and Tempered Martensite (generated through laser tempering), keeping a fully hardened martensite as a reference condition. The material performance has been compared in terms of the monotonic properties, useful for anti-intrusion performance, and Essential Work of Fracture, a well-suited parameter to predict the crash failure behavior of high strength steels. The results show that laser tempering offers properties similar to Bainite-based microstructures and can be a successful replacement in components where the sheet thickness does not allow for the fine control of the in-die thermomechanical evolution.