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.

Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.

This paper explores the application of offline reinforcement learning in batch manufacturing, with a specific focus on press hardening processes. Offline reinforcement learning presents a viable alternative to traditional control and reinforcement learning methods, which often rely on impractical real-world interactions or complex simulations and iterative adjustments to bridge the gap between simulated and real-world environments. We demonstrate how offline reinforcement learning can improve control policies by leveraging existing data, thereby streamlining the training pipeline and reducing reliance on high-fidelity simulators. Our study evaluates the impact of varying data exploration rates by creating five datasets with exploration rates ranging from ε=0 to ε=0.8. Using the conservative Q-learning algorithm, we train and assess policies against both a dynamic baseline and a static industry-standard policy. The results indicate that while offline reinforcement learning effectively refines behavior policies and enhances supervised learning methods, its effectiveness is heavily dependent on the quality and exploratory nature of the initial behavior policy.

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.

This study introduces a hybrid topology optimization methodology aimed at improving heat sink efficiency through a data-driven approach. The method integrates CFD simulations in Ansys Fluent with a Monte Carlo-driven optimization algorithm, modeling the design of a heat sink domain as a porous medium. Porosity is used as a design variable, iteratively adjusted in a binary manner to optimize fluid-solid distribution. Three design variants were evaluated, with the selected optimized configuration reaching a maximum temperature of 57.11 °C, compared to 46.15 °C for a baseline serpentine channel. Despite slightly higher peak temperature, the optimized design achieved a substantial reduction in pressure drop, up to 91.57%, translating into significantly lower pumping power requirements and thus lower energy consumption. Experimental validation, using physical prototypes of both the reference and optimized channels, confirmed strong agreement with simulation results, with average surface temperatures of 29.27 °C and 30.03 °C, respectively. These findings validate the accuracy of the simulation-based approach and highlight the potential of data-driven optimization in thermal management system designs.