Intelligent Grinding System for Medium/Thick Plate Welding Seams in Construction Machinery Using 3D Laser Measurement and Deep Learning

With the rapid development of the construction machinery industry, thick plate welds are increasingly needing efficient, accurate, and intelligent processing. This study proposes an intelligent grinding system using 3D line laser measurement and deep learning algorithms to solve the problems of inefficiency and inaccuracy existing in traditional weld grinding methods. This study makes use of 3D line laser measurement technology and deep learning algorithms in tandem, which perform automated 3D measurement and analysis to extract key parameters of the weld seam, in conjunction with deep learning algorithms applied on image data of the weld seam for the automatic classification, positioning, and segmentation of the weld seam. The entire work is divided into the following: image acquisition, motion control, and image processing. Based on various weld seam detection algorithms, the selected model was MNet-based DeepLab-V3. An intelligent trimming system for welding seams based on deep learning was constructed. Experiments were conducted to verify the feasibility and accuracy of the 3D line laser measurement technology for weld seam inspections, and that the deep learning algorithm can effectively identify the type and location of the weld seam, thus predicting the trimming strategy. With an accuracy far superior to conventionally based methods in accurate detection and regrinding of weld surface defects, the system proves advantageous for improved weld regrinding productivity and quality. It was determined that the system presents significant advantages in reinforcing weld regrinding when it comes to efficiency and quality, thus initiating a paradigm of using intelligent treatments for medium/thick plate welds in the construction machinery industry.