Machine vision and novel attention mechanism TCN for enhanced prediction of future deposition height in directed energy deposition

Laser Directed Energy Deposition (L-DED) has garnered significant attention due to its high flexibility and rapid processing capabilities. However, complex physical flow fields and drastic temperature variations are present during L-DED processing, leading to variations in deposition height at different layers and positions under the same processing parameters. Therefore, realtime monitoring of deposition height and timely knowledge of future deposition height are crucial for controlling geometries and arranging processing time effectively. To address this issue, a machine vision method for real-time monitoring of deposition height in noisy environments is proposed, demonstrating a remarkable similarity of 99.22% compared to values measured by a laser scanner. Addressing the complex physical phenomena during processing, specific data quantification was performed. A novel self-attention temporal convolutional network (SA-TCN) was then introduced as a data-driven model to replace physical models for predicting future deposition height, achieving an impressive accuracy of 99.71%. Experiments show that quantifying different physical phenomena with specific data to some extent improves the model prediction accuracy, providing significant support for future deposition height prediction and processing time control of parts in actual production.