Machine Learning Based Raw Material Quality Prediction

Abstract

In today's competitive manufacturing environment, ensuring consistent raw material quality is critical to production efficiency, product safety, and profitability. Traditional quality assessment methods that rely on manual inspection and laboratory testing are often slow, subjective, and error-prone. This paper proposes an intelligent machine learning-based system — QualityAI — for automated raw material quality prediction across three major industrial sectors: Food Processing, Textile, and Cosmetics. Ten supervised classification algorithms are trained and evaluated on domain-specific datasets comprising industry-relevant physicochemical and mechanical features. The system achieves a peak prediction accuracy of 99% and is deployed as an interactive web application using the Flask framework, enabling real-time grade prediction with probability breakdowns. Results demonstrate that ensemble methods such as Gradient Boosting and Random Forest consistently outperform linear and single-tree classifiers across all industries.