DETECTION OF HAZARDOUS MATERIALS IN LASER CUTTING USING DEEP LEARNING AND SPECKLE SENSING

Abstract

Abstract. The technology of laser cutting is widely used in various industries for processing materials. However, it generates a substantial amount of harmful dust, smoke, and aerosols, which pose a threat to the environment and endanger the health of workers. One potential method that has emerged to monitor the cutting process and identify materials in real-time is speckle sensing. This paper presents a novel material classification technique that employs a new deep-learning model architecture designed for speckle pattern images to classify materials according to the speckle patterns of the material's surface. The proposed approach involves training a convolutional neural network (CNN) on a large dataset of laser speckle patterns to recognize various material types for safe and efficient cutting. Material classification using speckle sensing enhances the process, reducing the time required to train the speckle images and the inference time for predicting the material from the speckle images. Experimental results demonstrate that the suggested method achieves high precision in categorizing materials, particularly hazardous ones. The model was evaluated on a test dataset of 3,000 new images, achieving an F1-score of 0.9781. The utilization of speckle sensing enables the proposed method to offer a fast, reliable, and accurate approach to material-aware laser cutting while mitigating the potential risks associated with the process.