Optimized batch identification of metallic alloy materials for precision machining operations using machine learning and image processing techniques

ABSTRACT In modern machining operations, real-time identification of metallic alloy materials is critical to maintaining high-quality standards, reducing errors, and enhancing operational efficiency. This study introduces a robust machine learning and image processing framework designed for the rapid and precise identification of material batches during machining processes. The framework evaluates multiple machine learning models, including Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), k-nearest Neighbors (k-NN), Artificial Neural Networks (ANN), Radial Basis Function Neural Networks (RBFNN), and Logistic Regression, to determine their suitability under varying cutting conditions with high and low data volumes. The models were benchmarked on accuracy, training time, and inference speed to identify the most effective solution for real-time applications. SVM emerged as the most accurate model, achieving a precision of 91.2% with an inference time of 0.8 milliseconds, making it ideal for real-time tasks. ANN exhibited a comparable accuracy of 89.3% but suffered from a significantly higher inference time of 210.2 milliseconds, limiting its real-time feasibility. RF demonstrated quick training times but incurred a relatively high inference delay. Naive Bayes offered the fastest training and inference times with an accuracy of 79.8%, suitable for scenarios prioritizing speed over precision. This comparative analysis provides valuable insights for selecting the optimal machine learning model tailored to specific manufacturing requirements, fostering a data-driven approach to optimizing machining operations.