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An Intelligent Framework for Automated Defect Detection in Laser Beam Machining Using Deep Learning
Abstract
This paper puts forward a conceptual framework for intelligently detecting defects in Laser Beam Machining (LBM) by bringing together computer vision and machine learning. In LBM, traditional quality control is often a manual, subjective process that struggles to keep up with the speed and complexity of modern manufacturing. Our proposed framework tackles this by using real-time image capture and advanced deep learning models to automatically find and classify different kinds of LBM-induced defects. The core of our methodology is a robust vision system designed to handle a variety of defect patterns and changing industrial environments. Ultimately, this research aims to dramatically improve manufacturing quality, cut down on material waste, and boost the overall efficiency of LBM operations. We see this as a way to bridge the gap between traditional mechanical process control and modern computational intelligence.
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