What question did this study set out to answer?

The aim is to explore the integration of supervised machine learning for enhancing predictive quality control in the automotive sector.

June 14, 2026Iconic Research and Engineering Journals

Predictive Quality Control in the Automotive Industry Using Supervised Machine Learning

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Authors

RPRokade P. P.WMWadghule Y. M.DADhiwar Abhay

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Implication

Survey paper demonstrates supervised machine learning improves defect prediction in automotive quality control, indicating a shift towards data-driven manufacturing.

Key Points

The aim is to explore the integration of supervised machine learning for enhancing predictive quality control in the automotive sector.
Detailed examination of algorithms including Random Forest, SVM, and XGBoost.
Utilization of real-time sensor data such as temperature, vibration, pressure, and tool wear.
Focus on transitioning from manual checks to proactive, data-driven inspection methods.
Achieved over 91% accuracy in defect prediction using machine learning algorithms.
Demonstrated substantial reductions in downtime and rework due to proactive quality control.
Highlighted the intelligent framework's potential to transform automotive manufacturing efficiency.

Cite This Study

P. et al. (2026) studied this question.

synapsesocial.com/papers/6a2e482cb1cc60ccdea8c737 https://doi.org/10.64388/irev9i12-1718894

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Implication

Survey paper demonstrates supervised machine learning improves defect prediction in automotive quality control, indicating a shift towards data-driven manufacturing.

Predictive Quality Control in the Automotive Industry Using Supervised Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Predictive Quality Control in the Automotive Industry Using Supervised Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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