Percorrer por autor "Pires, Rui"
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- Exploring automotive quality correlations through explainable machine learning what-if simulationPublication . Oliveira Júnior, Alexandre de; Calvo-Rolle, JoséLuis; Pires, Rui; Leitão, PauloHigh-dimensional variability in manufacturing processes presents significant challenges for quality control, demanding predictive strategies capable of capturing complex parameter dependencies. Machine learning (ML) offers robust mechanisms for this purpose, but reliance on black-box models often limits interpretability and hinders producing stakeholders’ identification of meaningful correlations for model optimization. This paper introduces an interactive what-if simulation platform designed to explore structural quality correlations in automotive assembly through explainable ML techniques, enhancing transparency and enabling uncertainty quantification. The platform is based on a modular Digital Twin (DT) architecture aligned with the ISO 23247 standard, guiding expert and non-expert users through correlation-driven feature selection, regression modelling and SHapley Additive exPlanations (SHAP) based post-hoc explanations. A case study using real inspection data from a vehicle assembly line demonstrates the tool’s capacity to support variable relevance assessment, dimensionality reduction, and model interpretability. Furthermore, an uncertainty-aware SHAP analysis enhances confidence in the model’s prediction stability, reinforcing the platform’s suitability for quality-driven decision support and integration into future DT ecosystems.
- Industrial Metaverse Digital Twin: ISO 23247 Compliant Architecture for AI-Driven SimulationPublication . Oliveira Júnior, Alexandre de; Calvo-Rolle, José Luis; Pires, Rui; Leitão, PauloThe Industrial Metaverse marks a new stage in Industry 4.0, raising the representation level of Digital Twins (DT) from discrete elements to an interconnected network of assets covering the entire production ecosystem. This paradigm changing reflects advances in enabling technologies such as Artificial Intelligence (AI) and complex what-if simulations. As complexity increases, adopting established industrial standards for implementing DT functionalities becomes imperative to specify guidelines for companies and researchers. This paper proposes a functional architecture for DT implementation in compliance with ISO 23247 standard, aiming to support the development of interoperable and standardized solutions combined within the Industrial Metaverse. The architecture was employed to develop a DT framework for an automotive assembly line, covering the quality inspection process and embedding AI-based mechanisms to leverage the what-if simulation of deviations in structural parameters of the vehicle's body. Experimental results demonstrate the tool's ability to accurately predict outputs for critical quality parameters according to hypothetical measurement scenarios, leveraging the production stakeholders' understanding regarding the correlated impact of deviations at different structural points, and demonstrating the versatility and potential of combining AI strategies for what-if simulations.
- Real-time rule-based monitoring tool to achieve zero defect manufacturingPublication . Costa, Jullo; Oliveira Júnior, Alexandre de; Barbosa, José; Alves, Gleifer; Borges, Andre; Garcia, Gisela; Pires, Rui; Leitão, PauloThe demands of innovative production systems are shifting from mass production to the creation of smaller quantities with a focus on high quality. To achieve these evolving demands, Zero Defect Manufacturing has emerged as a key paradigm. This approach requires an innovative architectural monitoring tool where real-time data is continuously gathered and analysed to predict defects and assess their potential impacts. It also necessitates the seamless integration of diverse data sources, advanced processing algorithms, and Digital Twins to align with industrial requirements. In this paper we present a real-time, rule-based monitoring tool applied to a real-world car manufacturing use case. The tool successfully generated early alerts for quality deviations, enabling production engineers to shift from a reactive to a proactive approach by detecting potential quality issues early in the process.