Build surface study of single-layer raster scanning in selective laser melting: Surface roughness prediction using deep learning
Build surface study of single-layer raster scanning in selective laser melting: Surface roughness prediction using deep learning
复制标题
选择性激光熔化中单层光栅扫描的构建表面研究：使用深度学习预测表面粗糙度
DOI：
10.1016/j.mfglet.2022.07.088
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Chou, Kevin
中科院分区：
文献类型：
--
作者：
Fotovvati, Behzad;Chou, Kevin
选择性激光熔化(SLM)是一种广泛使用的粉末床熔融添加剂制造(AM)工艺，用于制造航空航天、医疗、汽车等各种工业中的金属粉末零件。尽管在设计灵活性和机械性能方面有了很大的改善，但表面光洁度的可预测性很差，而且往往变化很大，这仍然是SLM应用中的一大挑战。影响SLM制造的零件表面粗糙度的因素很多，文献中已有报道，但主要是针对由几层组成的大块样品。本工作设计并制作了Ti6Al4V样品的单层光栅扫描。采用部分析因设计研究了激光功率、扫描速度、填充间距和膜层厚度这四个最主要的SLM工艺参数对样品表面粗糙度的影响。通过白光干涉法从216个数据集中获取表面粗糙度数据，然后利用反向传播方法训练机器学习模型，并根据输入的工艺参数预测表面粗糙度。结果表明，激光功率是决定样品表面粗糙度的最重要参数。有趣的是，尽管所研究的样品是在具有相同参数集的固体SLM上建立的单层栅格扫描区域，但单层表面粗糙度的变化中，层厚的贡献为10%~15%。此外，机器学习算法实现了合理的预测性，对于单独的32个测试数据集，确定系数为98.8%。爱思唯尔有限公司出版。保留所有权利。
Selective laser melting (SLM) is a widely used powder-bed fusion additive manufacturing (AM) process for the fabrication of parts from metal powders in a variety of industries such as aerospace, medical, automotive, etc. Despite significant improvements in the design flexibility and mechanical performance, the poor predictability in surface finish, and yet oftentimes with large variability, remains a major challenge in the SLM use. Numerous factors affect the surface roughness of SLM-manufactured parts, which have been reported in the literature, but mostly for bulk samples composed of several layers. In this work, single-layer raster scanning of Ti6Al4V samples are designed and fabricated. The influence of the four most dominant SLM process parameters, ie, laser power, scanning speed, hatch spacing, and layer thickness on sample surface roughness is thoroughly investigated using a fractional factorial design. Surface roughness data, acquired by white-light interferometry, from 216 data sets are then used to train a machine learning model with the back-propagation method and predict the surface roughness based on the input process parameters. The results show that the laser power is the most significant parameter in determining the top surface roughness of samples. Interestingly, although the investigated samples are single layer raster scanning areas on a solid SLM-built sample with the same parameter set, the layer thickness has a contribution of 10% to 15% in the variations of the surface roughness of the single layers. Furthermore, the machine learning algorithm achieves reasonable predictability, showing a coefficient of determination of 98.8% for a separate 32 testing data set.© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.