In-Process monitoring of porosity during laser additive manufacturing process
By Bin Zhang, Shunyu Liu, Yung C. Shin. Additive Manufacturing, 2019
291 citations recorded by the source corpus.
Abstract This paper describes a deep-learning-based method for porosity monitoring in laser additive manufacturing process. A high-speed digital camera was mounted coaxially to the process laser beam for in-process sensing of melt-pool data, and convolutional neural network models were designed to learn melt-pool features to predict the porosity attributes in deposited specimens during laser additive manufacturing. With the image processing tools developed in this paper, the extraction of porosity information from raw quality inspection data, such as cross-section images and tomography data sets, can be automated. The CNN models with a compact architecture, part of whose hyperparameters were selected through cross-validation analysis, achieved a classification accuracy of 91.2% for porosity occurrence detection in the direct laser deposition of sponge Titanium powders and presented predictive capacity for micro pores below 100 μm. For local volume porosity prediction, the model also achieved a root mean square error of 1.32% and exhibited high fidelity for both high porosity and low porosity specimens.
Subfields: Automotive Engineering, Computational Mechanics, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/1f0822ad2b0f43cee9553d7ce9df7a5fdb72ac06
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In Computational Mechanics
In Mechanical Engineering
Citations in the corpus
- Porosity in wire arc additive manufacturing of aluminium alloys (2021, methodology): “Zhang et al. used process imaging in combination with convolutional neural network models to monitor porosity during laser additive manufacturing [31].”