HYBRID AI-BASED PREDICTIVE QUALITY CONTROL FOR AUTOMOTIVE CUTTING PROCESSES: A SMART MANUFACTURING APPROACH UNDER IATF 16949

Saloua Yahyaoui; Mounia Zaim; Faical Zaim

doi:10.5937/jaes0-62872

Saloua Yahyaoui LASTIMI Laboratory, Mohammadia school of engineers, Mohammed V University in Rabat, Morocco https://orcid.org/0009-0001-7637-0943
Mounia Zaim Mohammed V University, Mohammadia School of Engineers, LASTIMI Laboratory, Rabat, Morocco https://orcid.org/0000-0003-0045-8815
Faical Zaim Hassan II University of Casablanca, School of Textile and Clothing Industries, REMTEX & CELOG Laboratory, Casablanca, Morocco https://orcid.org/0009-0000-7912-6025

DOI: https://doi.org/10.5937/jaes0-62872

Keywords: automotive industry, IATF 16949, smart supply chain, supervised learning -linear regression, predictive quality

Abstract

Ensuring product quality in the automotive industry becomes a technical strategic challenge in the era of digital and networked supply chains. Traditional final inspection methods, often reactive, are not sufficient to attempt the rigorous expectations of IATF 16949. The case of covers seating industry must be at the forefront of industrial excellence. Evoking comfort, functionality, and aesthetics for customer, taking into account that the cutting area in such industry is the focal point to loop considered as internal supplier in the covers supply chain, cutting output is the input for all assembly lines, which means that an uncontrolled quality KPI will disturb the manufacturing process. A hybrid AI –based predictive quality control modeling in cutting process is used combines expert-based validation through the Fuzzy Delphi Method to define the factors affecting the quality of cut products. The integration of data driven prediction (AI and IoT) represented in a linear-regression-based supervised learning model trained and learned with a simulated dataset from real production conditions according to literature review and experts feedback to detect and prevent failures in the early stage of production. The analysis shows the three variates (cutting speed, cutting temperature, vibration intensity) predefined from the literature review were validated as input data. The results indicate a high predictive quality accuracy since the coefficient of determination R² =0.87 and the model statistically very significant ANOVA results. Concluding that the vibration factor have the most significant impact on quality cutting defect. This hybrid AI-based predictive approach provides an improvement lever of the smart automotive manufacturing chains supporting data driven decisions under the IATF 16949.

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