Cross-section geometry prediction for laser metal deposition layer-based on multi-mode convolutional neural network and multi-sensor data fusion

Abstract

Laser metal deposition (LMD), a widely used additive manufacturing technology for materials, can produce complex parts with excellent mechanical properties. However, it suffers from geometric defects such as size drift and edge collapse. Real-time monitoring plays a vital role in improving the quality of deposited layers. This paper proposed an online monitoring method for LMD based on multi-sensor data fusion and a multi-mode convolutional neural network (M-CNN) for predicting the deposited layer size. To build the LMD dataset for training and validating the M-CNN, an experimental method was devised to acquire experimental data via a monitoring system. The M-CNN, consisting of Resnet18 and a fully connected neural network (FCNN), used deposition images, temperature and process parameters as inputs to predict the deposited layer sizes (including width, height and cross-sectional area). It established the relationship between the deposited size and process parameters. Batch analysis was performed to obtain the sensitivity of deposited size to the process parameters and the dependency between deposited sizes, facilitating the rapid selection of process windows. The learning process and prediction results of the neural network were visualized and analyzed. The model accuracy was verified by comparing with the baseline. This work can be used for developing tuning methods for LMD.