基于EWOA-LSSVR的机器人磨抛接触力预测模型

张诗涵; 魏锦辉; 王阳; 朱光; 李论; 刘殿海

doi:10.13394/j.cnki.jgszz.2024.0089

基于EWOA-LSSVR的机器人磨抛接触力预测模型

doi: 10.13394/j.cnki.jgszz.2024.0089

张诗涵^{1, 2},
魏锦辉¹,
王阳¹,
朱光^1, ,,
李论¹,
刘殿海¹

1.
中国科学院沈阳自动化研究所, 沈阳 110016
2.
辽宁科技大学机械工程与自动化学院, 辽宁鞍山 114051

基金项目: 辽宁省自然科学基金（2023-MS-034）；研究所基础研究面上项目（20222JK2K09）；国家资助博士后研究人员计划（GZC20232882）；中国博士后面上科学基金（2023M743703）。

详细信息

通讯作者:
朱光，男，1991年生，副研究员、博士。主要研究方向：机器人制造与工艺数字化。E-mail：gzhusdu@outlook.com

中图分类号: TH161; TG580
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- 文章访问数: 40
- HTML全文浏览量: 16
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-21
- 修回日期: 2024-10-15
- 录用日期: 2024-11-21
- 刊出日期: 2025-08-20

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

ZHANG Shihan^{1, 2},
WEI Jinhui¹,
WANG Yang¹,
ZHU Guang^{1
, ,},
LI Lun¹,
LIU Dianhai¹

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

摘要

摘要: 为确定航空发动机叶片机器人磨抛过程中材料去除深度与工艺参数之间的关系，获得加工所需的工艺参数，实现叶片表面材料的定点定量去除，建立叶片机器人磨抛加工系统，将各工艺参数考虑在内进行多组正交实验；利用实验数据建立基于最小二乘支持向量回归机（least squares support vector regression，LSSVR）模型，利用增强型鲸鱼优化算法（enhanced whale optimization algorithm，EWOA）提高算法精度、寻优能力和避免陷入局部最优并对LSSVR的超参数进行优化；对比标准鲸鱼优化算法（whale optimization algorithm，WOA）和粒子群优化（particle swarm optimization，PSO）算法预测模型的结果，并利用模型预测的工艺参数进行实验验证。结果表明：EWOA-LSSVR预测模型的决定系数R为96.031%，平均绝对误差R_MAE为0.012128 mm，相较于WOA-LSSVR和PSO-LSSVR模型具有更好的拟合度；且验证实验结果证明EWOA-LSSVR预测模型具有较好的预测准确性，并可为叶片表面材料的定点定量去除提供可靠依据。
- 机器人砂带磨抛 /
- 工艺参数 /
- 机器学习 /
- 最小二乘支持向量回归机 /
- 增强型鲸鱼优化算法
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 R_MAE 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 (R_MAE) 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.
- robot abrasive belt grinding and polishing /
- process parameter /
- machine learning /
- least squares support vector regression machine (LSSVR) /
- enhanced whale optimization algorithm (EWOA)

HTML全文

图 1 叶片机器人砂带自动磨抛系统

Figure 1. Blade robot sand belt automatic grinding and polishing system

下载: 全尺寸图片幻灯片

图 2 叶片磨抛加工区域示意图

Figure 2. Schematic diagram of blade grinding and polishing processing area

下载: 全尺寸图片幻灯片

图 3 叶片机器人砂带磨抛系统加工技术路线流程图

Figure 3. Flow chart of processing technology of blade robot abrasive belt grinding and polishing system

下载: 全尺寸图片幻灯片

图 4 EWOA优化LSSVR方式流程图

Figure 4. EWOA optimization LSSVR method flowchart

下载: 全尺寸图片幻灯片

图 5 叶片材料去除深度采点截面示意图

Figure 5. Cross section diagram of blade material removal depth sampling point

下载: 全尺寸图片幻灯片

图 6 适应度迭代次数过程图

Figure 6. Process diagram of fitness iteration times

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图 7 预测结果与真实值对比图

Figure 7. Comparison chart between predicted results and actual values

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图 8 实际加工深度误差分布图

Figure 8. Distribution map of actual machining depth error

下载: 全尺寸图片幻灯片

表 1 部分样本数据

Table 1. Partial sample data

样本编号 n	曲率半径 K / m⁻¹	砂带线速度 v / (m·s⁻¹)	磨料代号 m₁	进给速度 v_w / (mm·s⁻¹)	磨抛接触力 F / N	材料去除深度 h / mm
1	−52.032 3	5.86	P180	6	3	0.024
2	47.391 2	5.86	P180	6	3	0.022
3	50.485 4	5.86	P180	6	3	0.025
4	50.472 2	5.86	P180	6	3	0.025
…	…	…	…	…	…	…
1 472	11.545 8	10.89	P320	10	9	0.069

下载: 导出CSV

表 2 模型精度对比

Table 2. Comparison of prediction model accuracy

模型类型	评价指标
模型类型	R	R_MAE / mm	R_RMSE / mm²
EWOA-LSSVR	0.960 31	0.012 128	0.017 908
WOA-LSSVR	0.894 57	0.012 358	0.019 127
PSO-LSSVR	0.922 28	0.012 462	0.018 231

下载: 导出CSV

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