Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data

TL;DR

This paper develops a multi-modal CNN model to directly correlate molten pool images and temperature data with final part quality in wire-feed laser additive manufacturing, enabling real-time in situ quality monitoring.

Contribution

It introduces a novel multi-modal CNN approach that combines image and temperature data for in situ quality estimation in metal additive manufacturing.

Findings

CNN outperforms baseline regression models in accuracy

Model accurately predicts geometric and microstructural properties

Framework supports real-time quality assurance

Abstract

The advancement of machine learning promises the ability to accelerate the adoption of new processes and property designs for metal additive manufacturing. The molten pool geometry and molten pool temperature are the significant indicators for the final part's geometric shape and microstructural properties for the Wire-feed laser direct energy deposition process. Thus, the molten pool condition-property relations are of preliminary importance for in situ quality assurance. To enable in situ quality monitoring of bead geometry and characterization properties, we need to continuously monitor the sensor's data for molten pool dimensions and temperature for the Wire-feed laser additive manufacturing (WLAM) system. We first develop a machine learning convolutional neural network (CNN) model for establishing the correlations from the measurable molten pool image and temperature data directly to the geometric shape and microstructural properties. The multi-modality network receives both the camera image and temperature measurement as inputs, yielding the corresponding characterization properties of the final build part (e.g., fusion zone depth, alpha lath thickness). The performance of the CNN model is compared with the regression model as a baseline. The developed models enable molten pool condition-quality relations mapping for building quantitative and collaborative in situ quality estimation and assurance framework.