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@article{186931,
        author = {Dhiwar Abhay Ramdas and Kunde Aditi Sandeep and Longani Tejal Somnath and Nagare Aniket Sanjay},
        title = {Predictive quality control in automotive industry using supervised machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2883-2896},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186931},
        abstract = {The automotive industry stands at a crossroads, where its century-old reliance on manual quality checks is increasingly inadequate for the precision and efficiency demands of modern manufacturing. This survey paper explores a transformative solution: the integration of Supervised Machine Learning for predictive quality control. By moving beyond reactive inspections to a proactive, data-driven paradigm, the proposed system harnesses real-time sensor data—temperature, vibration, pressure, and tool wear—to predict faults before they result in defective products. Through a detailed examination of algorithms like Random Forest, SVM, and XGBoost,we demonstrate how this approach can achieve over 91% accuracy in defect prediction. The study concludes that this intelligent framework is not merely an incremental improvement but a fundamental shift, offering substantial reductions in downtime and rework while paving the way for the truly resilient and efficient "smart factories" of Industry 4.0.},
        keywords = {Supervised Machine Learning, Predictive Quality Control, Automotive Manufacturing, Fault Detection, Industry 4.0, Smart Factory.},
        month = {November},
        }