The digital transformation of manufacturing is accelerating through the integration of artificial intelligence (AI), particularly via real-time predictive models. These models enable manufacturers to transition from reactive to proactive strategies, intelligent optimization and decision-making. Within the frameworks of Industry 4.0 and Industry 5.0, which emphasize technologies such as cyber-physical systems, cloud computing, and human-centric innovation, AI-driven data models are pivotal for achieving smart, adaptive, and sustainable production systems. This paper investigates the impact of AI-based predictive modeling on manufacturing digitalization and its future potential. It examines how these models contribute to advanced frameworks such as online process advisory systems, digital shadows, and digital twins, while addressing their limitations and implementation challenges. Furthermore, the study reviews current practices in real-time data modeling across manufacturing processes—including direct-chill casting—supported by real-world case studies. These examples illustrate both the practical benefits and technical hurdles of deploying AI in dynamic industrial environments.

The digital transformation of manufacturing is accelerating through the integration of artificial intelligence (AI), particularly via real-time predictive models. These models enable manufacturers to transition from reactive strategies to proactive, intelligent optimization and decision-making. Within the frameworks of Industry 4.0 and Industry 5.0, which emphasize technologies such as cyber-physical systems, cloud computing, and human-centric innovation, AI-driven data models are pivotal for achieving smart, adaptive, and sustainable production systems. This paper investigates the impact of AI-based predictive modeling on manufacturing digitalization and its future potential. It examines how these models contribute to advanced frameworks such as online pro-cess advisory systems, digital shadows, and digital twins, while addressing their limitations and implementation challenges. Furthermore, the study reviews current practices in real-time data modeling across manufacturing processes—including direct-chill casting—supported by real-world case studies. These examples illustrate both the practical benefits and technical hurdles of deploying AI in dynamic industrial environments.

Background Minimising internal pressure drop in pipes is crucial for energy efficiency of fluid flow applications. Numerous computational optimisation tools that are capable of modifying flow geometries to reduce the pressure drop have been developed. Among these is a comparably simple heuristic optimisation al- gorithm which mimics erosion and sedimentation processes based on the shear stress in the vicinity of the domain boundaries. Although this method succeeds in modifying flow geometries for reduced pressure drop, it allows the fluid domain to widen during the reshaping process. Therefore, a reported reduction of pressure drop is not only caused by an improvement of the flow path, but also by an increase in the domain width. However, pipes with a constant circular diameter are favoured in many applications because they can be easily manufactured. Methods Here we combine the heuristic optimisation approach with a novel geometrical constraint that maintains constant average diameter throughout the reshaping process. To our knowledge, this is the first application of a diameter-preserving constraint to the heuristic optimality criterion, enabling assessment of pressure drop reduction achieved solely through flow path modification without diameter dilation effects. We determined the applicability of the new algorithm for 2D channel and 3D pipe ge- ometries, conducting numerical simulations using the Lattice Boltzmann method with Reynolds numbers ranging from 40 to 500. Results For the novel constant diameter constraint, the method successfully derived improved shapes across most tested Reynolds numbers while maintaining the initial average diameter. Notably, shapes derived at Re = 40 unexpectedly outperformed those derived at higher Reynolds numbers across all tested flow conditions, suggesting that low-Re geometries may capture fundamental flow features beneficial across wider Reynolds number ranges. Conclusions This finding suggests applicability to higher Reynolds number flows, potentially even at turbulent industrial flows to be investigated in fu- ture research.

Background: Minimising internal pressure drop in pipes is crucial for energy efficiency of fluid flow applications. Numerous computational optimisation tools that are capable of modifying flow geometries to reduce the pressure drop have been developed. Among these is a comparably simple heuristic optimisation algorithm which mimics erosion and sedimentation processes based on the shear stress in the vicinity of the domain boundaries. Although this method succeeds in modifying flow geometries for reduced pressure drop, it allows the fluid domain to widen during the reshaping process. Therefore, a reported reduction of pressure drop is not only caused by an improvement of the flow path, but also by an increase in the domain width. However, pipes with a constant circular diameter are favoured in many applications because they can be easily manufactured. Methods: Here we combine the heuristic optimisation approach with a new geometrical constraint that kept the average diameter constant. This way, a reduction of pressure drop caused solely by the modification of the flow path can be assessed. We determined the applicability of the modified algorithm for 2D channel and 3D pipe geometries, conducting numerical simulations using the Lattice Boltzmann method with Reynolds numbers ranging from 40 to 500. Results: For all investigated cases, potentially optimal shapes could be derived at all tested Reynolds numbers. However, the shape originally derived at a simulated flow with Re = 40 yielded a smaller pressure drop even for flows at Re ≥ 100 than shapes derived specifically at these higher Reynolds numbers. Conclusions: This observed trend should be kept in mind when employing this approach as a simple way to improve channel and pipe layouts.