1. 沈阳航空航天大学,沈阳,110136
2. 中国科学院沈阳自动化研究所,沈阳,110016
3. 中国科学院机器人与智能制造创新研究院,沈阳,110169
纸质出版:2024
移动端阅览
蔡鸣,朱光,李论,赵吉宾, 王奔,王正佳. 复杂曲面机器人砂带磨抛材料去除深度预测模型及试验研究[J]. 航空制造技术, 2024, 67(18): 100-107.
CAI Ming, ZHU Guang, LI Lun, ZHAO Jibin, WANG Ben, WANG Zhengjia. Prediction Model and Experimental Study on Material Removal Depth of Robotic Abrasive Belt Polishing Complex Curved Surfaces[J]. Aeronautical Manufacturing Technology, 2024, 67(18): 100-107.
蔡鸣,朱光,李论,赵吉宾, 王奔,王正佳. 复杂曲面机器人砂带磨抛材料去除深度预测模型及试验研究[J]. 航空制造技术, 2024, 67(18): 100-107. DOI: 10.16080/j.issn1671-833x.2024.18.100.
CAI Ming, ZHU Guang, LI Lun, ZHAO Jibin, WANG Ben, WANG Zhengjia. Prediction Model and Experimental Study on Material Removal Depth of Robotic Abrasive Belt Polishing Complex Curved Surfaces[J]. Aeronautical Manufacturing Technology, 2024, 67(18): 100-107. DOI: 10.16080/j.issn1671-833x.2024.18.100.
针对复杂曲面零件的磨抛过程中曲率半径对机器人砂带磨抛加工中型面精度的影响,开展基于多曲率半径镍基高温合金的机器人磨抛加工试验研究。主要探究不同曲率半径的试验件的磨抛加工性,对不同曲率半径的镍基高温合金试验件进行砂带磨抛加工设置相应的砂带粒度和磨抛工艺参数,采集试验件的材料去除深度,并对试验结果进行研究分析。试验结果表明,曲率半径的变化对材料去除深度存在着一定的影响,在曲率半径由大到小的变化中,材料去除深度也随之增加,即材料去除深度与曲率半径呈负相关关系。基于多元非线性回归模型提出关于砂带粒度、进给速度、接触力、工件曲率半径等影响因素的机器人砂带磨抛材料去除深度预测模型,模型的平均预测误差为1.45 μm,准确率达到91.04%,预测误差区间为–5.34~4.57 μm,并对预测模型进行显著性检验,表明该预测模型可为实际机器人砂带磨抛加工前期工艺参数设计提供重要的理论支持。
In response to the impact of curvature radius on the precision of robot abrasive belt polishing in the polishing process of complex curved parts
an experimental study on robot abrasive belt polishing based on multi curvature radius nickel based high-temperature alloy was carried out. The main focus was on exploring the machining performance of test pieces with different curvature radii
setting corresponding sand belt particle size and polishing process parameters for abrasive belt polishing of nickel based high-temperature alloy test pieces with different curvature radii
collecting the material removal depth of the test piece and analyzing the experimental results. The experimental results show that the variation of curvature radius has a certain impact on the depth of material removal. As the curvature radius changes from large to small
the depth of material removal also increases
indicating a negative correlation between the depth of material removal and the curvature radius. Based on a multiple nonlinear regression model
prediction model for the depth of material removal in robot abrasive grinding and polishing is proposed on the abrasive belt particle size
feed rate
contact force and workpiece curvature radius. The average prediction error of the prediction model is 1.45 μm
an accuracy rate is 91.04%
and a prediction error range is – 5.34–4.57 μm. The significance test of the prediction model indicates that the prediction model can provide important theoretical support for the early process parameter design of actual robot abrasive belt grinding and polishing processing.
0
浏览量
214
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621
