Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding

• Published in: IEEE access Vol. 10; pp. 67615 - 67629
• Main Authors: Yin, Fangchen, Ji, Qingzhi, Cun, Changcai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cutting parameters; Cutting speed; Grinding; grinding complexity; Machining; Optimization; Optimization algorithms; Prediction models; Predictive models; Process parameters; Regression models; Response surface methodology; Rough surfaces; Special-shaped natural stone; Stone; Support vector machines; Surface properties; Surface roughness; Surface topography; Surface treatment
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3179818

This paper aims to accurately predict the surface quality of the special-shaped stone by robotic grinding and effectively guide the adjustment of process parameters to ensure stable grinding quality, applies a support vector machine model based on improved whale optimization algorithm (IWOA-SVR), so as to establish a prediction model of surface roughness of special-shaped stone and a selection method of process parameters. The proposed IWOA-SVR was used to improve the prediction accuracy of support vector machine regression model, and a prediction model of surface roughness (<inline-formula> <tex-math notation="LaTeX">R_{a}) </tex-math></inline-formula> for stone was established. On this basis, the relationship between the output parameters (surface roughness) and inutput parameters (spindle speed, feed speed, cutting depth and cutting width)was explored to obtain more suitable process parameters. Combining the grinding experiment data of special-shaped stone, the comparison was carried out between IWOA-SVR and the SVR model optimized by the commonly used optimization algorithms (grid-search optimization algorithm (GS) and whale optimization algorithm (WOA)). Under the same sample condition, the prediction error of GS-SVR is the most large, and the average prediction error of IWOA-SVR is only 86.1% if that of WOA-SVR, the training time is shortened by 54.4%. The influence of process parameters on surface roughness obtained by IWOA-SVR can effectively guide the selection and adjustment of process parameters. It has good guiding significance for maintaining the excellent grinding quality of special-shaped stone.