Smart Resin Vat: Real-Time Detecting Failures, Defects, and Curing Area in Vat Photopolymerization 3D Printing

IF 1 Q4 ENGINEERING, MANUFACTURING
Yujie Shan, Aravind Krishnakumar, Zehan Qin, Huachao Mao
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Abstract

Real-time and in-situ printing performance diagnostic in vat photopolymerization is critical to control printing quality, improve process reliability, and reduce wasted time and materials. This paper proposed a low-cost smart resin vat to monitor the printing process and detect the printing faults. Built on a conventional vat photopolymerization process, we added equally spaced thermistors along the edges of the resin vat. During printing, polymerization heat transferred to the edges of the resin vat, which increased thermistors’ temperature and enhanced resistances. The heat flux received at each thermistor varied with the distance to the place of photopolymerization. The temperature profiles of all thermistors were determined by the curing image pattern in each layer, and vice versa. Machine learning algorithms were leveraged to infer the printing status from the measured temperatures of these thermistors. Specifically, we proposed a simple and robust Failure Index to detect if the printing was active or terminated. Gaussian process regression was utilized to predict the printing area using the temperature recordings within a layer. The model was trained, validated, and tested using the data set collected by printing six parts. Different printing abnormalities, including printing failures, manual printing pause, and missing features (incorrect printing area), were successfully detected. The proposed approach modified the resin vat only and could be easily applied to all vat photopolymerization processes, including SLA, DLP, and LCD based 3D printing. The limitation and future work are also highlighted.
智能树脂大桶:实时检测故障,缺陷,和固化区域在大桶光聚合3D打印
在还原釜光聚合过程中,实时、现场的打印性能诊断对于控制打印质量、提高工艺可靠性、减少时间和材料浪费至关重要。本文提出了一种低成本的智能树脂缸,用于印刷过程监控和印刷故障检测。基于传统的大桶光聚合工艺,我们沿着树脂大桶的边缘添加了等间距的热敏电阻。在印刷过程中,聚合热传递到树脂缸的边缘,这增加了热敏电阻的温度和增强电阻。每个热敏电阻接收到的热流随到光聚合点的距离而变化。所有热敏电阻的温度分布由每一层的固化图像模式决定,反之亦然。利用机器学习算法从这些热敏电阻的测量温度推断打印状态。具体来说,我们提出了一个简单而稳健的故障指数来检测打印是否处于活动状态或终止状态。利用层内温度记录,利用高斯过程回归预测打印面积。使用打印六个部分收集的数据集对模型进行训练、验证和测试。成功检测到各种打印异常,包括打印失败、手动打印暂停和缺少功能(打印区域不正确)。所提出的方法仅对树脂还原釜进行了修改,可以很容易地应用于所有还原釜光聚合工艺,包括SLA, DLP和基于LCD的3D打印。并指出了本文的局限性和今后的工作。
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来源期刊
Journal of Micro and Nano-Manufacturing
Journal of Micro and Nano-Manufacturing ENGINEERING, MANUFACTURING-
CiteScore
2.70
自引率
0.00%
发文量
12
期刊介绍: The Journal of Micro and Nano-Manufacturing provides a forum for the rapid dissemination of original theoretical and applied research in the areas of micro- and nano-manufacturing that are related to process innovation, accuracy, and precision, throughput enhancement, material utilization, compact equipment development, environmental and life-cycle analysis, and predictive modeling of manufacturing processes with feature sizes less than one hundred micrometers. Papers addressing special needs in emerging areas, such as biomedical devices, drug manufacturing, water and energy, are also encouraged. Areas of interest including, but not limited to: Unit micro- and nano-manufacturing processes; Hybrid manufacturing processes combining bottom-up and top-down processes; Hybrid manufacturing processes utilizing various energy sources (optical, mechanical, electrical, solar, etc.) to achieve multi-scale features and resolution; High-throughput micro- and nano-manufacturing processes; Equipment development; Predictive modeling and simulation of materials and/or systems enabling point-of-need or scaled-up micro- and nano-manufacturing; Metrology at the micro- and nano-scales over large areas; Sensors and sensor integration; Design algorithms for multi-scale manufacturing; Life cycle analysis; Logistics and material handling related to micro- and nano-manufacturing.
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