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Browsing by Author "Costa, Pedro Alexandre da Ponte"

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  • Injection Molding Process Monitoring Based on MAchine Learning Algorithms
    Publication . Costa, Pedro Alexandre da Ponte; Mendes, Sílvio Priem; Loureiro, Paulo Jorge Gonçalves
    This thesis addresses the detection of non-conformities in an injection moulding process using unsupervised and semi-supervised learning techniques, with the dual objective of identifying faulty production time zones that prompt the creation of non-conforming parts and enhancing process explainability for machine operators. The early detection of machine parameter deviations, such as abnormal temperature, pressure, torque, and cycle time variations, is essential to minimize economic losses, material waste, and sub-optimal product quality, while maintaining the machine in an optimal production state for longer periods. A diverse array of methods, including Local Outlier Factor (LOF), Isolation Forest (IF), One-Class Support Vector Machine (One-Class SVM), OPTICS, Mean Shift, and Long Short-Term Memory (LSTM) networks, were experimented with to achieve this goal. Extensive feature engineering and selection were performed to balance dimensionality reduction with domain-relevant interpretability, enabling actionable feedback for process optimization. The model pipeline was developed using Python, PyTorch, and Scikit-learn, containerized with Docker and accelerated using CUDA. Real-world sensor data spanning six months of continuous 24/7 operation were used for training and evaluation. The proposed LSTM-based approach, designed for time series modeling, achieved a weighted average F1-score of 0.94 on test data in predicting faulty production time zones of approximately seven-minute intervals. Evaluation metrics included accuracy, precision, and recall, with particular emphasis on the F1-score due to the imbalanced nature of the dataset and the critical need to minimize both false positives and false negatives. A key aspect of this work lies in its commitment to data-driven development, grounded in the use of real, unfiltered sensor data from live industrial production. While raw data introduces noise and operational variability, it also provides a more faithful representation of the production environment, revealing edge cases and failure modes often absent in curated datasets. Addressing these challenges required robust preprocessing, careful validation, and a strong understanding of the process domain, ultimately enabling the development of a model better suited for real-world deployment and generalization. The results demonstrate that this methodology provides a robust baseline for anomaly detection and process monitoring in injection moulding. Contributions include a reproducible framework for explainable unsupervised anomaly detection, validation of LSTM’s effectiveness over static models, and novel feature reduction strategies, such as applying PCA to sensor groups while preserving domain interpretability. This work serves both as a practical tool for deployment and as a methodological reference and compendium of techniques for future research in data-driven industrial process optimization.
    2025-11-24Master thesis Open access Show more