Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning
Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning
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DOI：
10.1088/1361-6501/ac6569
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发表时间：
2022-04
影响因子：
2.4
通讯作者：
Ming-Yu Liu;N. Senin;Rong Su;R. Leach
中科院分区：
文献类型：
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作者：
Ming-Yu Liu;N. Senin;Rong Su;R. Leach
激光粉末床熔化（L-PBF）过程的质量监控，尤其是实时质量监控，对于保证零件质量和降低制造成本具有重要意义。测量层表面形貌对于质量监控至关重要，因为层表面上的任何异常都可能导致最终部件的缺陷。在本文中，我们提出了一种表面测量方法，基于散射光图案的使用和基于卷积自动编码器的无监督机器学习方法，使用散射模型从参考表面模拟的大量散射图案进行设计和训练。使用自动编码器的优点是可以仅使用来自可接受表面的数据来训练监控模型，而不需要确保所有类型的可能表面缺陷的代表性观察结果的存在。使用模拟数据进行训练的优点是，我们可以获得有效的监控解决方案，而无需大量的实验观察。在这里，我们报告的初步调查的结果所提出的解决方案的性能，其中训练的自动编码器进行测试的实验数据获得关闭的过程中，使用专用的实验装置产生和收集光散射图案从制造的L-PBF表面。我们的研究结果表明，所提出的监测解决方案是能够检测可接受的和异常的表面。虽然需要进一步验证才能在机上和过程中设置中全面评估性能，但我们的初步结果令人鼓舞，并提供了使用我们的表面测量解决方案进行L-PBF过程中监测的潜在优势。
Quality monitoring for laser powder bed fusion (L-PBF), particularly in-process and real-time monitoring, is of importance for part quality assurance and manufacturing cost reduction. Measurement of layer surface topography is critical for quality monitoring, as any anomaly on layer surfaces can result in defects in the final part. In this paper, we propose a surface measurement method, based on the use of scattered light patterns and a convolutional autoencoder-based unsupervised machine learning method, designed and trained using a large set of scattering patterns simulated from reference surfaces using a scattering model. The advantage of using an autoencoder is that the monitoring model can be trained using solely data from acceptable surfaces, without the need to ensure the presence of representative observations for all the types of possible surface defects. The advantage of using simulated data for training is that we can obtain an effective monitoring solution without the need for a large collection of experimental observations. Here we report the results of a preliminary investigation on the performance of the proposed solution, where the trained autoencoder is tested on experimental data obtained off-process, using a dedicated experimental apparatus for generating and collecting light scattering patterns from manufactured L-PBF surfaces. Our results indicate that the proposed monitoring solution is capable of detecting both acceptable and anomalous surfaces. Although further validation is required to fully assess performance within an on-machine and in-process setup, our preliminary results are encouraging and provide a glimpse of the potential benefits of using our surface measurement solution for L-PBF in-process monitoring.