Research on the sustainable measurement of machined surface roughness under the influence of cutting environment

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.