Current Volume 9
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.
Citations
IRE Journals:
Rokade P. P., Wadghule Y. M., Dhiwar Abhay, Kunde Aditi, Longani Tejal; Nagare Aniket "Predictive Quality Control in the Automotive Industry Using Supervised Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 1331-1339 https://doi.org/10.64388/IREV9I12-1718894
IEEE:
Rokade P. P., Wadghule Y. M., Dhiwar Abhay, Kunde Aditi, Longani Tejal; Nagare Aniket
"Predictive Quality Control in the Automotive Industry Using Supervised Machine Learning" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1718894
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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