DLAM: Deep Learning Based Real-Time Porosity Prediction for Additive Manufacturing Using Thermal Images of the Melt Pool

IEEE Access

卷:9

Khalil Dajani¹  Wenlu Zhang²  Matthew Buchholz²  Linkan Bian³  Mohammad Mozumdar⁴  Samson Ho⁴  Wesley Young⁴  Saleh Al Jufout⁴ 

[1] California Aerospace Technologies Institute of Excellence, Lancaster, CA, USA;

[2] Computer Engineering and Computer Science Department, California State University, Long Beach, CA, USA;

[3] Department of Industrial and Systems Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS, USA;

[4] Electrical Engineering Department, California State University, Long Beach, CA, USA;

关键词: Anomaly detection;    convolutional neural network;    deep learning;    metal additive manufacturing;    porosity prediction;    recurrent neural network;   

DOI  :  10.1109/ACCESS.2021.3105362

来源: DOAJ

【 摘 要 】

This paper presents an investigation of the rapid variations in the temperature of metal melt pool for Additive Manufacturing (AM) processes. The melt pool is created by scanning a high-power laser beam across a metal powder bed. Rapid heating and cooling processes are involved in the layer-by-layer fabrication of the metal part. Recent advances in Machine Learning and Deep Learning algorithms provide efficient ways to analyze large sets of data in search of correlations that would otherwise be extremely time-consuming. The use of Machine Learning and Deep Learning algorithms to understand temperature variations in AM fabrication process will allow to predict the formation of porosity before it occurs. The objective of this research is to advance the AM technology using enhanced Deep Learning techniques to provide in-situ analysis of the melt pool temperature that can lead to a reliable manufacturing of Three-Dimensional (3D) metal parts/components. In specific, Deep Learning based porosity prediction for Additive Manufacturing (DLAM) methods have been proposed. In DLAMs, several state-of-the-art Deep Learning algorithms such as Convolutional Neural Networks (CNN) using transfer learning, and Residual-Recurrent Convolutional Neural Networks (Res-RCNN) are proposed for effectively performing the end-to-end porosity prediction in real-time using thermal images of melt pool. Experimental results, in this research, show that the Res-RCNN has an overall accuracy of 99.49% and inference time of $8.67ms$ , and the Res-RCNN outperforms other baseline models. The Res-RCNN’s recursive architecture allows the network to view each input image multiples times and at varying feature levels, which enables a slight boost in porosity prediction accuracy over the commonly used transfer learning CNN models.

【 授权许可】

Unknown