基于小波神经网络熔丝堆积三维打印精度预测模型

摘要/Abstract

摘要：

摘要：熔融沉积快速成形是一个多参数耦合的非线性过程，大量成形参数对成形件精度具有重要影响.为了弄清各个工艺参数对成形零件精度的影响，提高熔丝堆积三维打印产品精度，运用Matlab软件建立了利用成形工艺参数预测产品精度的小波神经网络模型，完成了算法设计.通过熔丝堆积三维打印实验采集样本，利用训练样本对所建立的网络进行训练，完成网络输入输出精度映射关系，并利用测试样本对所训练网络进行检验.仿真试验表明，产品精度预测模型具有很高的精度，验证了该预测模型在理论和实践上的可行性、有效性.把小波神经网络方法运用于熔丝堆积三维打印参数与成形产品精度之间的建模，解决了难以用数学方法建立精确模型的问题.

关键词: , 熔丝堆积三维打印, 产品精度, 小波神经网络, 正交法, 预测模型

Abstract:

Abstract： Technological parameters in fused deposition modeling three-dimensional printing are coupled and the forming process is a non-linear forming process. A large number of modeling parameters affect the quality of the product precision in fused deposition modeling of three-dimensional printing. In order to clarify the effects of various parameters on the forming precision and improve the precision of three-dimensional printing, the wavelet neural network prediction model of product precision was build by using the Matlab software. The arithmetic was designed and the samples were acquired by fused deposition modeling experiment. The training samples were used to train the network to accomplish the mapping relation between the input and output of the network. The test samples were used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The prediction model of product precision is feasible and valid in theory and in practice. The wavelet neural network method was used to model the relation between the processing parameters and the product precision in fused deposition modeling of threedimensional printing. The difficult problem to create the accurate mathematics model was solved.

Key words: fused deposition modeling of three-dimensional printing, product precision, wavelet neural network, orthogonal plan method, prediction model

中图分类号:

纪良波. 基于小波神经网络熔丝堆积三维打印精度预测模型[J]. 上海交通大学学报（自然版）, 2015, 49(03): 375-378.

JI Liangbo. Precision Prediction Model in Fused Deposition Modeling of Three-Dimensional Printing Based on Wavelet Neural Network[J]. Journal of Shanghai Jiaotong University, 2015, 49(03): 375-378.

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