Quantitative and Real-Time Control of 3D Printing Material Flow Through Deep Learning
Quantitative and Real-Time Control of 3D Printing Material Flow Through Deep Learning
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DOI：
10.1002/aisy.202200153
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发表时间：
2022-09-11
影响因子：
7.4
通讯作者：
Pattinson, Sebastian W.
中科院分区：
文献类型：
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作者：
Brion, Douglas A. J.;Pattinson, Sebastian W.
3D打印可以通过本地化和按需生产彻底改变制造业，同时实现独特的复杂和定制产品。然而，3D打印的生产错误倾向阻碍了自主操作和实现这一愿景所需的质量保证。人工操作人员无法持续监控或实时纠正错误，而自动化方法主要只检测错误。新的方法要么离线纠正参数，要么响应时间慢，预测粒度差，限制了它们的效用。利用常用的3D打印过程元数据，以及打印过程的视频，构建一个独特的图像数据集。回归模型经过训练，可以精确地预测打印材料流应该如何改变以纠正错误，这应该用于构建能够进行3D打印参数发现和少量校正的快速控制回路。实验结果表明，该系统能够学习未知复杂材料的最优参数，并实现对新零件的快速纠错。类似的元数据存在于许多制造过程中，这种方法可以在制造业中更广泛地采用快速数据驱动的控制系统。
3D printing could revolutionize manufacturing through local and on-demand production while enabling uniquely complex and custom products. However, 3D printing's propensity for production errors prevents autonomous operation and the quality assurance necessary to realize this vision. Human operators cannot continuously monitor or correct errors in real time, while automated approaches predominantly only detect errors. New methodologies correct parameters either offline or with slow response times and poor prediction granularity, limiting their utility. A commonly available 3D printing process metadata is harnessed, alongside the video of the printing process, to build a unique image dataset. Regression models are trained to precisely predict how printing material flow should be altered to correct errors and this should be used to build a fast control loop capable of 3D printing parameter discovery and few-shot correction. Demonstrations show that the system can learn optimal parameters for unseen complex materials, and achieve rapid error correction on new parts. Similar metadata exists in many manufacturing processes and this approach could enable the adoption of fast data-driven control systems more widely in manufacturing.