Percorrer por autor "Garcia, Gisela"
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- Agent-based distributed data analysis in industrial cyber-physical systemsPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, Eugenio; Garcia, GiselaCyber-Physical System (CPS) is a key concept in the fourth industrial revolution, acting as the backbone infrastructure to develop Industry 4.0 compliant solutions based on decentralized networks of cyber-physical entities. In such systems, the distribution of intelligence and data analytics capabilities by different computational layers assumes a crucial relevance to support monitoring, diagnosis, prediction, and optimization tasks. Multiagent Systems (MAS) is a suitable approach to support this distribution through vertical and horizontal dimensions. In this context, this article introduces a modular agent-based architecture to balance the distribution of data analysis capabilities in industrial CPS. The proposed MAS solution was implemented in an industrial automotive factory plant, which applicability, novelty, and benefits were recognized, e.g., through its inclusion in the Innovation Radar of the Great EU-funded Innovations due to its contribution for the implementation of Zero Defects Manufacturing strategies in multistage production systems.
- An agent-based industrial cyber-physical system deployed in an automobile multi-stage production systemPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, Eugénio; Garcia, GiselaIndustrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers. © 2020, Springer Nature Switzerland AG.
- Multistage quality control using machine learning in the automotive industryPublication . Peres, Ricardo Silva; Barata, José; Leitão, Paulo; Garcia, GiselaProduct dimensional variability is a crucial factor in the quality control of complex multistage manufacturing processes, where undetected defects can easily be propagated downstream. The recent advances in information technologies and consequently the increased volume of data that has become readily available provide an excellent opportunity for the development of automated defect detection approaches that are capable of extracting the implicit complex relationships in these multivariate data-rich environments. In this paper, several machine learning classifiers were trained and evaluated on varied metrics to predict dimensional defects in a real automotive multistage assembly line. The line encompasses two automated inspection stages with several human-operated assembly and pre-alignment stages in between. The results show that non-linear models like XGBoost and Random Forests are capable of modelling the complexity of such an environment, achieving a high true positive rate and showing promise for the improvement of existing quality control approaches, enabling defects and deviations to be addressed earlier and thus assist in reducing scrap and repair costs.