This review article delves into the conceptual framework of digital twins and their diverse applications across research domains, highlighting the pivotal role of machine learning in shaping the development and integration of digital twin technology across multiple disciplines. Emphasising key features like multidisciplinarity and multi-scale aspects, the paper explores how data-driven techniques are employed for modelling, visualisation, monitoring, and optimisation within the digital twin framework, pinpointing the benefits introduced in the current state-of-the-art applications, and elucidates persisting challenges across various research fields, including advanced materials, smart buildings, and manufacturing systems.

Composite material parts are typically laid out in near-net-shape, i.e., very close to the finished product configuration. However, further machining processes are often required to meet dimensional and tolerance requirements. Drilling, edge trimming and slotting are the main cutting processes employed for carbon fiber-reinforced plastic (CFRP) composite materials. In particular, drilling stands out as the most widespread machining process of CFRP composite parts, chiefly in the aerospace industrial sector, due to the extensive use of mechanical joints, such as rivets, rather than welded or bonded joints. However, CFRP drilling is markedly challenging: due to CFRP abrasiveness, inhomogeneity and anisotropic properties, tool wear rates are inherently high leading to superior cutting forces and detrimental effects on workpiece surface quality and material integrity. Damage such as delamination, cracks or matrix thermal degradation is often observed as the result of uncontrolled tool wear or improper machining conditions. Sensor monitoring of drilling operations is, therefore, highly desirable for process conditions’ optimization and tool life maximization. The development of this kind of automated control technologies for process and tool state evaluation can notably contribute to the reduction of scraps and tool costs as well as to the improvement of process productivity in the drilling of CFRP composite material parts. In this paper, multi-sensor process monitoring based on thrust force and torque signal detection and analysis was applied during drilling of CFRP/CFRP laminate stacks for the assembly of aircraft fuselage panels with the scope to evaluate the tool wear state. Different signal-processing methods were utilised to extract diverse types of features from the detected sensor signals. A machine-learning approach based on an artificial neural network (ANN) was implemented to make smart decisions on the timely execution of tool change, which is highly functional for CFRP drilling process automation.

Welding is one of the most complex industrial processes because it is challenging to model, control, and inspect. In particular, the quality inspection process is critical because it is a complex and time-consuming activity. This research aims to propose a system of online inspection of the quality of the welded items with gas metal arc welding (GMAW) technology through the use of neural networks to speed up the inspection process. In particular, following experimental tests, the deviations of the welding parameters—such as current, voltage, and welding speed—from the Welding Procedure Specification was used to train a fully connected deep neural network, once labels have been obtained for each weld seam of a multi-pass welding procedure through non-destructive testing, which made it possible to find a correspondence between welding defects (e.g., porosity, lack of penetrations, etc.) and process parameters. The final results have shown an accuracy greater than 93% in defects classification and an inference time of less than 150 ms, which allow us to use this method for real-time purposes. Furthermore in this work networks were trained to reach a smaller false positive rate for the classification task on test data, to reduce the presence of faulty parts among non-defective parts.

Nowadays, sustainability of manufacturing processes is a major concern which calls for special efforts to reduce their environmental impact and energy consumption. In additive manufacturing, this issue is even more challenging due to the usually high energy demands of these processes. However, in the era of Industry 4.0, machine learning (ML) techniques, combined with metaheuristic optimization algorithms, offer a powerful solution to explore new, unproven combinations of process parameters that better align with sustainability goals of manufacturing. These methodologies can minimize the need for extensive experimental campaigns and provide a valuable decision-making support tool for goal-oriented process parameters optimization. In line with such approach, this research work introduces an intelligent data-driven methodology using ML to optimize wire arc additive manufacturing (WAAM) of Invar 36 alloy considering both the resulting layer geometry and quality as well as the process energy consumption. An experimental campaign involving WAAM deposition of 15 walls made of Invar 36 alloy using a natural dip transfer welding process was carried out. The data acquired from the WAAM experimental tests were used to develop and train an artificial neural network (ANN) which, on the basis of the process parameters, was able to predict the layer geometry, the specific energy consumption and a specified quality score indicative of the presence of defects. The ANN achieved a high accuracy with 100% F2 score for quality classification, 0.4 mm mean absolute error for layer geometry, and 20 J/mm MAE for specific energy consumption. A genetic algorithm (GA) was then used to identify optimal process parameters able to minimize the specific energy consumption while maintaining quality and smoothness of the deposited layer. The experimental validation carried out using the GA-optimized process parameters in the WAAM process confirmed the reliability of the model, resulting in energy-efficient and defect-free walls.