The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.

New and more complex casting technologies are growing, and foundries are using innovative methods to reduce cost and energy consumption and improve their product qualities. Numerical techniques, as tools to design and examine the process improvements, are also evolving continuously to embrace modelling of more dynamic systems for industrial applications. This paper will present a fresh approach towards the numerical simulation of dynamic processes using an evolving and dynamic mesh technique. While the conventional numerical techniques have been employed for these dynamic processes using a fixed domain approach, the more realistic evolving approach is used herein to match the complex material processes in new foundries. The underpinning of this new dynamic approach is highlighted by an evolving simulation environment where multiple mesh entities are appended to the existing numerical domain at timesteps. Furthermore, the change of the boundary and energy sources within casting process simulations have rationally been presented and its profound effects on the computational time and resources have been examined. The discretization and solver computational features of the technique are presented and the evolution of the casting domain (including its material and energy contents) during the process is described for semi-continuous casting process applications.

New data science and real-time modeling techniques facilitate better monitoring and control of manufacturing processes. By using real-time data models, industries can improve their processes and identify areas where resources are being wasted. Despite the challenges associated with implementing these data models in transient and multi-physical processes, they can significantly optimize operations, reduce trial and error, and minimize the overall environmental footprint. Implementing real-time data analytics allows industries to make quicker, informed decisions and immediate corrections to material processes. This ensures that manufacturing sustainability targets are regularly met and product quality is maintained. New concepts such as digital twins and digital shadows have been developed to bridge the gap between physical manufacturing processes and their virtual counterparts. These virtual models can be continuously updated with data from their physical counterparts, enabling real-time monitoring, control, and optimization of manufacturing processes. This paper demonstrates the predictive power of real-time reduced models within the digital twin framework to optimize process parameters using data-driven and hybrid techniques. Various reduced and real-time model-building techniques are investigated, with brief descriptions of their mathematical and analytical foundations. The role of machine learning (ML) and ML-assisted data schemes in enhancing predictions and corrections is also explored. Real-world applications of these reduced techniques for extrusion and additive manufacturing (AM) processes are presented as case studies.

Reduced and real-time modelling is one of the main pillars of digital “process models” for twinning of manufacturing processes. Starting from the data processing and model building, a digital twin of additive manufacturing (AM) processes involves creating virtual replica where predictions and corrections can be made in real-time. Developing such fast predictive/corrective digital models involve data training and machine learning (ML) routines, where dynamic and accurate models can be employed for process optimisation and control. In this research work, the overview of the real-time modelling and ML data training have been presented for AM processes using hybrid and reduced order modelling (ROM) techniques. Hence, variations of processing parameters (e.g., temperature, power and feeding speed) for wire arc AM processes are considered to develop a tailored process data base and its associated snapshot matrix. Furthermore, the accuracy and reliability of these digital models for monitoring and optimizing AM processes are investigated using a real-world case study. The performances of different reduced model building, and data interpolation techniques have subsequently been scrutinized to create the most accurate and efficient solver-interpolator combinations for integration of real-time models into digital twins for AM processes.

The challenging task of bringing together the advanced computational models (with high accuracy) with reasonable computational time for the practical simulation of industrial process applications has promoted the introduction of innovative numerical methods in recent decades. The time and efforts associated with the accurate numerical simulations of manufacturing processes and the sophisticated multiphysical and multiscale nature of these processes have historically been challenging for mainstream industrial numerical tools. In particular, the numerical simulations of industrial continuous and semicontinuous casting processes for light metal alloys have broadly been reinvigorated to investigate the optimization of casting processes. The development of advanced numerical techniques (e.g., multiscale/physical, finite zoning, and evolving domain techniques) for industrial process simulations including the transient melt flow, heat transfer, and evolution of stress/strain and damage during continuous casting processes have endeavored many new opportunities. However, smarter and broader improvements are needed to capture the underlying physical/chemical phenomena including multiscale/physical transient fluid‐thermal‐mechanical coupling and dynamic heat‐transfer changes during these processes. Within this framework, the cooling system including its fluid flow and its characteristic heat transfer has to be modelled. In the research work herein, numerical studies of a novel transient evolving technique including the thermal‐mechanical phenomena and Heat Transfer Coefficient (HTC) estimation using empirical and reverse analyses are presented. The phase change modeling during casting process including liquid/solid interface and also the implementation of dynamic HTC curves are also considered. One of the main contributions of this paper is to show the applicability and reliability of the newly developed evolving numerical simulation approach for in‐depth investigations of continuous casting processes.