The manufacturing process carried out in the investment casting industry suffers from problems similar to other production processes. In addition, the high requirements of the customers and the industries that require these parts mean that high quality standards must be met. If those requirements are not achieved, this leads to the rejection of the manufactured parts. Therefore, given the current technology revolution (i.e., Industry 4.0) and the possibilities offered by tools such as digital twins and artificial intelligence, it is possible to work on a generation of intelligent systems that can reduce and even avoid these problems. Therefore, this study proposes the creation of a digital twin based on artificial intelligence to work on proactively identifying problems before they happen and, if they are detected, launch an optimization process that offers corrective actions to solve them. More specifically, this work focuses on the analysis of the manufacturing process (definition, KPI extraction, capture, distribution, and visualization), the creation of a base system for the integral management of process optimization, and experiments developed for determining the best method for making predictions. Additionally, we propose a recommender system to (i) avoid the appearance of porosities and (ii) keep the elongation of the parts in the ranges desired by the customer.

Industrial pouring processes operate under highly dynamic conditions where small deviations can lead to defects, scrap, and production losses. Although modern foundries are equipped with multiple sensors and visual inspection systems, most monitoring approaches remain fragmented, unimodal, and difficult to interpret. Furthermore, annotated anomalous samples in industrial settings are scarce, hindering the development of traditional methods. As a result, many critical pouring anomalies are detected too late or lack sufficient contextual information for effective decision making. In this work, we propose a multimodal framework for industrial scene characterisation that combines visual information and process signals through an explainable Mixture-of-Experts (MoE)-style expert-fusion strategy. First, we deploy an ensemble of specialised modules that collaborate to identify regions of interest, assess pouring quality, and contextualise events within the production process, thereby generating an interpretable description of pouring events. Second, we introduce a novel anomaly detection method for multimodal video data, combining a self-supervised transformer with an outlier-aware clustering algorithm. Our approach effectively identifies rare anomalies without requiring extensive manual labelling. The resulting information is structured into a digital twin-ready representation, supporting synchronisation between the physical system and its virtual counterpart. This solution provides a scalable, deployable pathway to transform heterogeneous industrial data into actionable knowledge, supporting advanced monitoring, anomaly detection, and quality control in real foundry environments.

Industrial pouring processes operate under highly dynamic conditions where small deviations can lead to defects, scrap, and production losses. Although modern foundries are equipped with multiple sensors and visual inspection systems, most monitoring approaches remain fragmented, unimodal, and difficult to interpret. Furthermore, annotated anomalous samples in industrial settings are scarce, hindering the development of traditional methods. As a result, many critical pouring anomalies are detected too late or lack sufficient contextual information for effective decision making. In this work, we propose a multimodal framework for industrial scene characterization that unifies visual information and process signals through a Mixture of Experts (MoE) strategy. First, we deploy an ensemble of specialized modules that collaborate to identify regions of interest, assess pouring quality, and contextualize events within the production process, generating an interpretable description of pouring events. Second, we introduce a novel anomaly detection method for video multimodal data, combining a self-supervised transformer with an outlier-aware clustering algorithm. Our approach effectively identifies rare anomalies without requiring extensive manual labeling. The resulting information is structured into a digital-twin-ready representation, enabling seamless synchronization between the physical system and its virtual counterpart. This solution provides a scalable, deployable pathway to transform heterogeneous industrial data into actionable knowledge, supporting advanced monitoring, anomaly detection, and quality control in real foundry environments.