Improved Modeling of Kinematics-Induced Geometric Variations in Extrusion-Based Additive Manufacturing Through Between-Printer Transfer Learning
Improved Modeling of Kinematics-Induced Geometric Variations in Extrusion-Based Additive Manufacturing Through Between-Printer Transfer Learning
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DOI：
10.1109/tase.2021.3063389
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发表时间：
2022-07
影响因子：
5.6
通讯作者：
J. Ren;An-Tsun Wei;Zhengqian Jiang;Hui Wang;Xiaolin Wang
中科院分区：
文献类型：
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作者：
J. Ren;An-Tsun Wei;Zhengqian Jiang;Hui Wang;Xiaolin Wang
对于基于挤出的添加剂制造，材料沉积的变化会显著影响印刷材料的分布，导致填充不均匀和缺陷。这些变化是由印刷机挤出机的运动变化引起的。这种填充不均匀在协作打印系统的应用中更为显著，通过该应用，多个打印机的挤出物共同创建相同的结构，因为涉及更多的加减速运动循环。对印刷运动学对这种变化的影响缺乏定量的了解，以指导印刷过程控制。本文通过建立一个数学模型来解决这一挑战，该模型量化了打印速度和加速引起的打印路径上的打印宽度变化。该模型为预测填充图案的不一致性提供了重要信息，并有可能在未来的研究中使用G代码调整来补偿填充误差。此外，由于该模型捕捉到了运动学诱导变化的机理，因此它提供了一种印刷机之间关于估计印刷误差的知识转移的方式。本文进一步提出了一种基于信息先验的转移学习算法，通过利用互连的3-D打印机的共享数据来改进有限历史数据打印机的质量预测模型。基于实验的案例研究验证了该方法的有效性。从业人员注意-本文定量研究了挤出机运动学对基于挤出的3-D打印过程中的几何变化和打印质量的影响。该模型可以帮助预测几何印刷质量和相关缺陷，如充填或欠填问题，给定运动学设置G-CODE。这项研究可以加快新印刷商学习运动学引起的印刷变异的过程，以便在数据有限的情况下，在生产的早期阶段建立监控和G代码调整以进行过程控制。从长远来看，这种打印机之间的迁移学习具有实现互联协作3-D打印系统的迁移学习的潜力，从而提高打印效率和质量。
For extrusion-based additive manufacturing, the variation in material deposition can significantly affect printed material distribution, causing infill nonuniformity and defects. These variations are induced by kinematic variations of the printer extruder. Such infill nonuniformity is more significant in an application of collaborative printing systems by which multiple printers’ extrudes co-create the same structure since more accelerate–decelerate kinematic cycles are involved. There is a lack of a quantitative understanding of the impact of printing kinematics on such variations to guide the printing process control. This article deals with the challenge by establishing a mathematical model that quantifies the printing width variations along the printing paths induced by printing speed and acceleration. The model provides vital information for predicting infill pattern nonuniformity and potentially enables using G-code adjustment to compensate for the infill errors in future research. In addition, since the model captures the mechanism of kinematics-induced variations, it provides a way of between-printer knowledge transfer on estimating printing errors. This article further proposes an informative-prior-based transfer learning algorithm to improve the quality prediction model for a printer with limited historical data by leveraging the shared data from interconnected 3-D printers. A case study based on experiments validated the effectiveness of the proposed methodology. Note to Practitioners—This article quantitatively studies the impact of extruder kinematics on geometric variations and printing quality in extrusion-based 3-D printing processes. The model can help predict the geometric printing quality and related defects, such as overfill or underfill problems given kinematics setup by G-code. This study can expedite the learning process of printing variations induced by kinematics for new printers to set up monitoring and G-code adjustment for process control in the early stage of production when the data are limited. In the long run, such between-printer transfer learning has the potential to enable the transfer learning for interconnected collaborative 3-D printing systems with improved printing efficiency and quality.