改进支持向量机在SLA 3D打印模型尺寸误差预测的应用
发布时间：2018-09-01 09:05
【摘要】：采用SLA 3D打印机打印不同参数的同一模型,测量成型件模型尺寸参数,并利用改进的LSSVM模型对不同参数的成型件尺寸误差进行预测。首先分析主要影响SLA 3D打印模型质量的原因,确定四个主要因素:叠层厚度,模型摆放角度和支撑密度,接触点大小。设计试验,采用SLA 3D打印机在此参数下打印,再对打印成型件进行测量确定成型件尺寸信息及尺寸误差,基于已有数据建立改进的LS-SVM模型对不同打印参数下的成型件的尺寸误差进行预测。结果表明模型预测正确率达到92.6471%,改进的LS-SVM相较于原寻优方法及BP神经网络对SLA 3D打印尺寸误差预测有良好的效果。
[Abstract]:SLA 3D printer is used to print the same model with different parameters to measure the dimension parameters of the model, and an improved LSSVM model is used to predict the dimension error of the model with different parameters. Designing experiment, using SLA 3D printer to print under this parameter, then measuring and determining the size information and size error of the printed parts. Based on the existing data, an improved LS-SVM model is established to predict the size error of the formed parts under different printing parameters. The results show that the prediction accuracy of the model reaches 92. 6471%. Compared with the original optimization method and BP neural network, the improved LS-SVM has a good effect on the prediction of dimensional error of SLA 3D printing.
【作者单位】： 上海交通大学机械与动力工程学院;
【基金】：上海市科委项目(15111102203;16111106102) 上海交通大学医工(理)交叉基金资助(YG2014MS04;YG2015MS09)
【分类号】：TP18;TP334.8
，
本文编号：2216718
[Abstract]:SLA 3D printer is used to print the same model with different parameters to measure the dimension parameters of the model, and an improved LSSVM model is used to predict the dimension error of the model with different parameters. Designing experiment, using SLA 3D printer to print under this parameter, then measuring and determining the size information and size error of the printed parts. Based on the existing data, an improved LS-SVM model is established to predict the size error of the formed parts under different printing parameters. The results show that the prediction accuracy of the model reaches 92. 6471%. Compared with the original optimization method and BP neural network, the improved LS-SVM has a good effect on the prediction of dimensional error of SLA 3D printing.
【作者单位】： 上海交通大学机械与动力工程学院;
【基金】：上海市科委项目(15111102203;16111106102) 上海交通大学医工(理)交叉基金资助(YG2014MS04;YG2015MS09)
【分类号】：TP18;TP334.8
，
本文编号：2216718
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