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On account of the complex lighting environmental impact of the machine tool, the tool body and wear boundary cannot be accurately extracted, which brings adverse effects to the wear detection. At the same time, most of the research on the side edge wear detection of end mill has focused on local wear, lacking the judgment and analysis of the overall side edge wear. In view of this situation, a "holistic to local" tool wear detection method based on the reconstruction idea was proposed. Firstly, the wear condition of the tool flank was identified as a whole, and then, the wear area affecting the remaining useful life of the tool was detected. The method mainly consists of three steps: the extraction of tool body, the reconstruction of tool wear edge, and tool wear detection. Aiming at the complex background of the tool image, a semantic segmentation algorithm based on U-net neural network was applied to separate foreground and background and extract the tool body. Secondly, based on pixel accuracy, the tool edge was extracted and the tool wear boundary was reconstructed. Finally, the range of the wear boundary coordinates was obtained, the number of pixels occupied by the maximum wear amount was gained, and the maximum wear of the tool was calculated according to the pixel length equivalent. Comparing the wear amount detected by the algorithm with the actual measured value, the results show that the method has high detection accuracy and the average error is controlled within 5%, which proves the effectiveness of the method and realizes the high-precision detection of the wear state of the tool during machining. [ABSTRACT FROM AUTHOR]

The machined surface in the industrial cutting environment is often covered by some interference factors, such as chips or coolant residuals, which make the existing methods have large detection errors. Therefore, the interference factors in the cutting environment seriously affect the sustainability detection and evaluation of the processed surface quality. This paper discusses the relationship between interference factors and roughness to study the sustainability detection of roughness. Firstly, a multidimensional feature parameter matrix with strong correlation with surface roughness is extracted and constructed based on the gray level co-occurrence matrix. On this basis, the adverse effects of interference factors (chips) on image feature parameters are quantitatively analyzed. According to the relationship between chip area and error change rate of feature parameters, an error correction model is constructed to optimize the feature parameters that change due to interference factors. The error correction model greatly reduces the negative influence of chip interference. This is the core of this paper. Further, the BP neural network model and support vector machine (SVM) model are used to predict the surface roughness with the optimized multi-dimensional feature parameters matrix as input, respectively. The above process realizes the sustainable detection of machined surface roughness. At the same time, in order to facilitate industrial applications, this paper uses LabVIEW software and MATLAB software to package the above research into a software system. Finally, the practicability and effectiveness of the sustainable detection research are verified by practical application in the industrial scene. This study promotes the sustainable development between product safety and environmental impact in the industrial manufacturing process. [ABSTRACT FROM AUTHOR]