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Volume 45 Issue 4
Aug.  2025
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Article Navigation >  Diamond & Abrasives Engineering  > 2025  >  45(4): 551-560
ZHANG Shihan, WEI Jinhui, WANG Yang, ZHU Guang, LI Lun, LIU Dianhai. Prediction model of robot grinding and polishing contact force based on EWOA-LSSVR[J]. Diamond & Abrasives Engineering, 2025, 45(4): 551-560. doi: 10.13394/j.cnki.jgszz.2024.0089
Citation: ZHANG Shihan, WEI Jinhui, WANG Yang, ZHU Guang, LI Lun, LIU Dianhai. Prediction model of robot grinding and polishing contact force based on EWOA-LSSVR[J]. Diamond & Abrasives Engineering, 2025, 45(4): 551-560. doi: 10.13394/j.cnki.jgszz.2024.0089
shu

Prediction model of robot grinding and polishing contact force based on EWOA-LSSVR

doi: 10.13394/j.cnki.jgszz.2024.0089
  • 1.

    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

  • 2.

    School of Mechanical Engineering & Automation, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China

More Information
  • Received Date: 2024-05-21
  • Accepted Date: 2024-11-21
  • Rev Recd Date: 2024-10-15
    Abstract
  •   Objectives  High-pressure turbine blades, as the core components of aviation engines, are subjected to harsh working environments of high temperature, high pressure, and high load for a long time, which places strict requirements on their high-temperature mechanical properties and structural stability. Therefore, the material of turbine blades is often selected as single-crystal high-temperature alloys and the blades are made through precision casting processes. Due to the casting characteristics of the blades, the material distribution of the workpiece is uneven, that is, the deviations of the design sizes from different positions on the blade surfaces vary. Therefore, the fixed-point quantitative removal of the blade surface material plays a very important role in the blade production and manufacturing process.  Methods  Blade grinding and polishing processing experiments are established by considering various technological parameters. The experimental data are used as the training set for the prediction model, and a prediction model based on the least squares support vector machine (LSSVR) is constructed. In the LSSVR hyperparameter setting stage, the enhanced whale optimization algorithm (EWOA) is used to improve algorithm accuracy, enhance optimization capability, and prevent local optima while optimizing the LSSVR hyperparameters. The prediction models optimized by other algorithms are established for comparison of model prediction capabilities. The prediction results are applied to the reproduction experiments of the material removal amount, and the performance of the prediction model is evaluated by using the processing results.  Results  From the perspective of model establishment and result prediction, the processing parameter prediction model EWOA-LSSVR based on the enhanced whale optimization algorithm (EWOA)-optimized least squares support vector machine (LSSVR) exhibits high prediction accuracy and good model fitting degree, with a determination coefficient of 96.031% and a mean absolute error RMAE of 0.012 128 mm. The prediction models of LSSVR optimized by the whale optimization algorithm (WOA) and particle swarm optimization (PSO) have determination coefficients of 89.457% and 92.228%, and mean absolute errors (RMAE) of 0.012 358 and 0.012 462 mm, respectively. In contrast, the prediction results of EWOA-LSSVR are more accurate with lower errors. The prediction results of EWOA-LSSVR are used as the process parameters for blade processing. When the dimensional error of the processed area of the blade enters the design tolerance zone of ±0.05 mm, it is considered qualified. The qualified rate of the sampling points in the two processing experiments reaches 93.59%, which plays a certain guiding role in the actual processing of the blade.  Conclusions  A prediction model for process parameters is established by using the least squares support vector machine suitable for small sample sizes. To improve the algorithm accuracy of model establishment and avoid falling into local optima, the enhanced whale algorithm is adopted to optimize the hyperparameters of the least squares support vector machine, and a prediction model with a determination coefficient of 96.031% and an average absolute error of 0.012 128 mm is established. By comparing with the prediction models optimized by WOA and PSO, the established prediction model has certain advantages in terms of determination coefficient, mean absolute error and mean square error. The reproduction experiment of the removal amount is carried out. After two processing experiments, a processing result with a qualified rate of 93.59% at the sampling points is achieved, proving the feasibility of using this method to achieve fixed-point and quantitative removal of the blade surface material.

     

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