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Investigation of Deep Learning for Real-Time Melt Pool Classification for Additive Manufacturing Final

Published

Author(s)

Zhuo Yang, Yan Lu, Ho Yeung, Sundar Krishnamurty

Abstract

Consistent melt pool geometry is an indicator of a stable laser powder bed fusion (L-PBF) additive manufacturing process. Melt pool size and shape reflect the impact of process parameters and scanning path on the interaction between the laser and the powder material, the phase change and the flow dynamics of the material during the process. Current L-PBF processes are operated based on predetermined toolpaths and processing parameters and consequently lack the ability to make reactions to unexpected melt pool changes. This paper investigated how melt pool can be characterized in real-time for feedback control. A deep learning-based melt pool classification method is developed to analyze melt pool size both fast and accurately. The classifier, based on a convolutional neural network, was trained with 2763 melt pool images captured from a laser melting powder fusion build using a serpentine scan strategy. The model is validated through 2926 new images collected from a different part in the same build using ‘island’ serpentine strategy with predictive accuracy of 91%. Compared to a traditional image analysis method, the processing time of the validation images is reduced by 90 %, from 9.72 s to 0.99 s, which gives the feedback control a reaction time window of 0.34 ms/image. Results show the feasibility of the proposed method for a real-time closed loop control of L-PBF process.
Proceedings Title
Investigation of Deep Learning for Real-Time Melt Pool Classification for Additive
Manufacturing_final
Conference Dates
August 22-26, 2019
Conference Location
Vancouver, -1
Conference Title
2019 IEEE 15th International Conference on Automation Science and Engineering

Keywords

Additive Manufacturing, Image processing, Real-time control, Deep Learning
Created September 19, 2019, Updated December 6, 2019