变组分复合材料3D打印工艺中机器学习算法对工艺参数预测有效性研究

doi:10.3901/JME.2024.21.263

机械工程学报 ›› 2024, Vol. 60 ›› Issue (21): 263-274.doi: 10.3901/JME.2024.21.263

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变组分复合材料3D打印工艺中机器学习算法对工艺参数预测有效性研究

牛静宜^1,2,3, 鲁思伟^1,2,3, 张倍宁^1,2,3, 杨春成⁴, 李涤尘^1,2,3

1. 西安交通大学精密微纳制造技术全国重点实验室西安 710049;
2. 西安交通大学机械工程学院西安 710049;
3. 西安交通大学国家药监局医用增材制造器械研究与评价重点实验室西安 710054;
4. 陕西聚康高博医疗科技有限公司西安 710004

收稿日期:2023-12-01 修回日期:2024-04-18 发布日期:2024-12-24
通讯作者: 李涤尘，男，1964年出生，博士，教授，博士研究生导师。主要研究方向为增材制造(3D打印)技术与应用、生物3D打印技术与临床应用。E-mail：dcli@mail.xjtu.edu.cn
作者简介:牛静宜,女,1997年出生。主要研究方向为增材制造变组分复合材料。E-mail:3121101170@stu.xjtu.edu.cn
基金资助:
国家自然科学基金(52375348，52175331)、山东省高等学校青创科技支持计划(2020KJB003)、山东省自然科学基金面上(ZR2022ME014，ZR2021ME139)和山东省自然科学基金重大基础研究(ZR2020ZD04)资助项目。

Study on the Effectiveness of Machine Learning Algorithms for Process Parameter Prediction in 3D Printing Process of Variable-component Composites

NIU Jingyi^1,2,3, LU Siwei^1,2,3, ZHANG Beining^1,2,3, YANG Chuncheng⁴, LI Dichen^1,2,3

1. State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an 710049;
2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049;
3. National Medical Products Administration (NMPA) Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an 710054;
4. Shaanxi Jugao-IM Technology Co., Ltd., Xi'an 710004

Received:2023-12-01 Revised:2024-04-18 Published:2024-12-24

摘要/Abstract

摘要： 复合材料变组分3D打印是增材制造发展的前沿方向，是实现梯度材料结构的重要技术，在线调控工艺参数适应3D打印过程中的材料组分变化是制造变组分复合材料的难点问题。将机器学习算法与3D打印工艺相结合，在增材制造小样本训练的基础上，建立工艺参数与挤出体积关系的算法模型，探讨机器学习算法调控变组分复合材料3D打印工艺参数的有效性。采用螺杆挤出成形3D打印设备，针对不同材料组分采集试验数据，并使用SVR支持向量回归、BP神经网络、RF随机森林、RBF神经网络和Kriging模型五种机器学习算法，对挤出体积量进行预测，从而根据不同材料组分调整相应的工艺参数。由对比结果可知，在进行挤出体积预测时，为保证预测结果，训练样本量应多于30组，五种机器学习算法中SVR算法最适合小样本量预测情况，挤出体积预测准确性最高。进行变组分复合材料3D打印试验，打印过程中根据材料组分调整工艺参数，样件打印质量好，验证了SVR算法调控工艺参数的有效性。

关键词: 增材制造, 变组分复合材料, 机器学习, 工艺参数预测

Abstract: 3D printing of variable-component composites is a cutting-edge direction in the development of additive manufacturing, which is an important technology to realise gradient material structure. Online regulation of process parameters to adapt to the material component changes during 3D printing is a difficult problem in manufacturing variable-component composites. Combining machine learning algorithms with 3D printing process, and on the basis of small-sample training in additive manufacturing, establishing an algorithmic model of the relationship between process parameters and extruded volume, so as to explore the effectiveness of machine learning algorithms to regulate the process parameters of variable-component composites 3D printing. The screw extrusion 3D printing equipment is used to collect experimental data for different material components. Five machine learning algorithms, SVR support vector regression, BP neural network, RF random forest, RBF neural network and Kriging model, were used to predict the extrusion volume, which is used to adjust the process parameters according to different material components. From the results, it can be seen that when extrusion volume prediction is performed, the training sample size should be more than 30 groups in order to ensure the prediction results. Moreover, SVR algorithm is the most suitable for the small sample size prediction situation among the five machine learning algorithms, and it has the highest extrusion volume prediction accuracy. 3D printing experiments of variable-component composites are carried out, and the process parameters are adjusted according to the material components during the printing process. The sample pieces are printed with good quality, which verifies the effectiveness of the SVR algorithm to regulate the process parameters.

Key words: additive manufacturing, variable component composites, machine learning, prediction of process parameters

中图分类号:

TP181

牛静宜, 鲁思伟, 张倍宁, 杨春成, 李涤尘. 变组分复合材料3D打印工艺中机器学习算法对工艺参数预测有效性研究[J]. 机械工程学报, 2024, 60(21): 263-274.

NIU Jingyi, LU Siwei, ZHANG Beining, YANG Chuncheng, LI Dichen. Study on the Effectiveness of Machine Learning Algorithms for Process Parameter Prediction in 3D Printing Process of Variable-component Composites[J]. Journal of Mechanical Engineering, 2024, 60(21): 263-274.

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变组分复合材料3D打印工艺中机器学习算法对工艺参数预测有效性研究

Study on the Effectiveness of Machine Learning Algorithms for Process Parameter Prediction in 3D Printing Process of Variable-component Composites

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