悬空区域侧损失双层次智能改善方法

doi:10.3785/j.issn.1008-9497.2023.06.013

浙江大学学报（理学版）

2023, Vol. 50

Issue (6): 781-794 DOI: 10.3785/j.issn.1008-9497.2023.06.013

第15届全国几何设计与计算学术会议专题

悬空区域侧损失双层次智能改善方法

李欣菁¹,潘万彬^1,^2,³(

),杨烨¹,王毅刚¹,林成¹

^1.杭州电子科技大学数字媒体与艺术设计学院，浙江杭州 310018
^2.虚拟现实技术与系统全国重点实验室（北京航空航天大学），北京 100191
^3.浙江大学计算机辅助设计与图形系统全国重点实验室，浙江杭州 310058

A double-level intelligent improvement approach for overhangs on side loss

Xinjing LI¹,Wanbin PAN^1,^2,³(

),Ye YANG¹,Yigang WANG¹,Cheng LIN¹

^1.School of Media and Design，Hangzhou Dianzi University，Hangzhou 310018，China
^2.State Key Laboratory of Virtual Reality Technology and Systems，Beihang University，Beijing 100191，China
^3.State Key Laboratory of CAD&CG，Zhejiang University，Hangzhou 310058，China

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摘要：

形状复杂的零件采用3D打印时通常存在悬空区域，悬空区域侧面成型后几何误差，即侧损失，往往非常明显，严重影响悬空区域及其零件的成型精度。为此，提出一种悬空区域侧损失双层次智能改善方法。首先，基于田口法设计了取不同关键设计参数和打印参数的一系列实验，并用所设计的测量方法测取打印的倒“L”形零件的侧损失样本数据。其次，针对倒“L”形零件悬空区域支撑结构迥异的两侧（即悬空侧和非悬空侧）构造了两类侧损失预测网络，可准确预测各尺寸下倒“L”形零件悬空区域两侧成型后的几何误差。再次，以悬空区域两侧的侧损失最小为目标，构造了单目标多变量非线性规划问题，得到优化的侧损失预测值及对应的工艺参数值。最后，依据侧损失预测值对悬空区域两侧实施反向几何偏移补偿，并用优化的工艺参数值进行打印，实现了对悬空区域侧损失的双层次智能改善。基于熔融沉积技术，用训练集以外的倒“L”形零件进行实验，验证了所提方法的有效性。结果表明，所提方法适用于悬空区域且具有显著改善悬空区域侧损失的巨大潜力。

关键词： 悬空区域; 侧损失; 工艺参数; 几何预补偿; 3D打印

Abstract:

Overhangs are usually inevitable when fabricating a part of complex shape in 3D printing. Meanwhile, the geometric error on the side surface of an overhang (i.e. the side loss) after fabricating is often significant, which seriously affects the accuracy of the overhang as well as its container (i.e. a part). To solve the above problem, a double-level intelligent improvement approach for overhangs on side loss (i.e. process parameter optimization and geometry pre-compensation) is proposed in this paper. Firstly, a series of experiments with different values concerning the critical design parameter and process parameters are designed based on the Taguchi method. Then, a deliberate measurement method is designed to get the side loss data from the fabricated inverted 'L'-shaped parts. Secondly, two types of side loss prediction networks are respectively constructed for the two sides (that is the overhang side and the non-overhang side) of each inverted 'L'-shaped part. They are mainly designed according to the requirements of support structures on an overhang. Aided with these networks, the geometric error of both sides of an overhang on an inverted 'L'-shaped part (with various values of the critical design parameter) can be predicted accurately. Thirdly, aiming at minimizing the side losses on both sides of an overhang, a single-objective and multiple-variables nonlinear programming problem is formulated. Hereby, the corresponding optimized side losses as well as their counterpart values of key process parameters can be determined. Finally, we compensate the geometries on the two sides of an overhang based on the above-optimized side losses by conducting an inverse modification first and then fabricate the overhang adopting the above-optimized values of key process parameters. Based on fused deposition modeling, experiments were implemented on various inverted 'L'-shaped parts except the ones used in constructing prediction networks, which verified the effectiveness of the proposed approach. Meanwhile, comparative analyses with state-of-the-art works were also carried out. The results show that our method is suitable for overhangs and has great potential to significantly improve their side losses.

Key words: overhang area side loss process parameters geometric pre-compensation 3D printing

收稿日期: 2023-06-12 出版日期: 2023-11-30

CLC:

TP 391.41

基金资助: 国家自然科学基金资助项目(61702147);虚拟现实技术与系统全国重点实验室（北京航空航天大学）开放课题项目(VRLAB 2022C08);浙江省重点研发计划项目(2021C03137);浙江大学计算机辅助设计与图形系统全国重点实验室开放课题（A2328）

通讯作者: 潘万彬 E-mail: panwanbin@hdu.edu.cn

作者简介: 李欣菁（1999—），ORCID：https//orcid.org/0009-0008-3624-9955，女，硕士研究生，主要从事3D打印研究.

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引用本文:

李欣菁,潘万彬,杨烨,王毅刚,林成. 悬空区域侧损失双层次智能改善方法[J]. 浙江大学学报（理学版）, 2023, 50(6): 781-794.

Xinjing LI,Wanbin PAN,Ye YANG,Yigang WANG,Cheng LIN. A double-level intelligent improvement approach for overhangs on side loss. Journal of Zhejiang University (Science Edition), 2023, 50(6): 781-794.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2023.06.013 或 https://www.zjujournals.com/sci/CN/Y2023/V50/I6/781

图1 侧损失示意

图2 流程图

图3 基准模型设计图

图4 与基准模型轮廓相关的几何特征集

图5 侧损失测量

图6 ANN模型灵敏度分析

图7 基于ANN的侧损失预测网络结构

图8 改进的粒子群优化算法

表1 基准模型参数的取值范围

表2 主要工艺参数因子水平取值

表3 主要设计参数因子水平取值

图9 测量示意

表4 数据集归一化结果

图10 实际测量的侧损失与预测的侧损失的对比

图11 悬空区域两侧几何预补偿示意

图12 基准模型1补偿前后成型效果

图13 基准模型2补偿前后成型效果

图14 模型3补偿前后成型效果

表5 相关文献方法比较

表6 效率与效果比较

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