Comparison of Early Stopping Neural Network and Random Forest for In-Situ Quality Prediction in Laser Based Additive Manufacturing
Comparison of Early Stopping Neural Network and Random Forest for In-Situ Quality Prediction in Laser Based Additive Manufacturing
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DOI：
10.1016/j.promfg.2021.06.065
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Matthew Behnke;Shenghan Guo;W. Guo
中科院分区：
文献类型：
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作者：
Matthew Behnke;Shenghan Guo;W. Guo
基于激光的增材制造（LBAM）是一种很有前途的制造工艺，可以生产具有多种功能的复杂零件，用于大量工程应用。熔池是LBAM工艺的一个众所周知的特征。孔隙缺陷，这阻碍了LBAM的广泛采用，是与整个LBAM过程中发生的熔池特性。可以捕获LBAM过程的高速监视器为现场监视缺陷和异常创造了可能性。本文着重于增加知识的LBAM过程和孔隙度之间的关系，并提供模型，可以有效地，准确地，一致地预测缺陷和异常原位LBAM过程。本文提出了两种模型，随机森林分类器和早期停止神经网络，用于分类高温计图像和分类，如果这些图像将导致缺陷。这两种方法都可以有效地实现99%以上的准确度，这将为LBAM过程中的质量预测提供一种原位方法。
Laser-Based Additive Manufacturing (LBAM) is a promising process in manufacturing that allows for capabilities in producing complex parts with multiple functionalities for a large array of engineering applications. Melt pool is a well-known characteristic of the LBAM process. Porosity defects, which have hampered the expansive adoption of LBAM, is correlated with the melt pool characteristic that occurs throughout the LBAM process. High-speed monitors that can capture the LBAM process have created the possibility for in-situ monitoring for defects and abnormalities. This paper focuses on augmenting knowledge of the relation between the LBAM process and porosity and providing models that could efficiently, accurately, and consistently predict defects and anomalies in-situ for the LBAM process. Two models are presented in this paper, Random Forest Classifier and Early Stopping Neural Network, which are used to classify pyrometer images and categorize if those images will result in defects. Both methods can achieve over 99% accuracy in an efficient manner, which would create an in-situ method for quality prediction in the LBAM process.