Machine learning-enhanced process optimization for exit burr minimization in inclined micro dimple milling: A multi-model ensemble approach with uncertainty quantification

Abstract

Unwanted material edges or burrs seriously reduce the quality of tiny surface dimples in automotive and aerospace parts. This study creates a computer-based system to optimize angled micro dimple machining by combining several prediction methods that estimate reliability. Experimental data comprising 81 measurements from 27 unique machining conditions on aluminum workpieces using different machining speeds of 1000–18,000 r/min, feed rates of 500–3000 mm/min, and tool angles of 15–45° were analyzed with four computer prediction methods: random forest, advanced process modeling, automatic feature selection, and combined approaches. The dataset was split into 65 training samples and 16 testing samples for model validation. Smart data processing expanded four basic measurements into 16 useful variables, improving prediction accuracy by 28.5% on average. The best combined method achieved 97.6% prediction accuracy, with feed-per-tooth confirmed as the most important factor. Advanced process modeling provided reliability estimates with 95% confidence levels, enabling safer process improvements. The system's fast computing performance makes it suitable for real-time process control and smart manufacturing applications.