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Quality prediction/classification of a production system under uncertainty based on Support Vector Machine

Abstract : With the emergence of the IoT paradigm, manufacturing industries are opting for new technologies for data collection and analysis to evaluate the quality of their manufacturing systems. Machine learning and classification methods provide various solutions to quality management such as defect detection and conformity prediction. However, manufacturing data are affected by uncertainties, which affect the performances of classification techniques. Accordingly, the thesis aims to study and manage the impact of measurement uncertainties on the predictive performances of support vector machine (SVM). Two groups of approaches are thus proposed: the former aiming to quantify the impact of measurement uncertainties on the prediction accuracy of SVM using several propagation techniques and data mining techniques, and the latter aiming to improve the robustness of SVM to uncertainties using robust optimization techniques. The various approaches provide a better understanding of the SVM robustness and how to improve it. The proposed approaches are evaluated through case studies with industrial partners.
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Submitted on : Tuesday, January 26, 2021 - 2:42:33 PM
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  • HAL Id : tel-03121560, version 1

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Wahb Zouhri. Quality prediction/classification of a production system under uncertainty based on Support Vector Machine. Artificial Intelligence [cs.AI]. HESAM Université, 2020. English. ⟨NNT : 2020HESAE058⟩. ⟨tel-03121560⟩

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