Study on Classification and Recognition of Laser Powder Bed Fusion Forming Layer Morphology and Prediction of Forming Quality Based on YOLO Algorithm

doi:10.3901/JME.260081

Abstract

Abstract: Laser powder bed fusion (LPBF) enables the high-precision fabrication of complex metal components; quality fluctuations during the process and the lack of reliable in-situ defect monitoring is one of the key research directions. Focusing on the LPBF fabrication of Ti-6Al-4V alloys, this study develops an in-situ vision-based method for online monitoring and classification of layer-wise build quality, which enables the prediction of forming quality. First, single-track experiments are conducted to investigate melt-pool behavior and optical morphology under different combinations of laser power and scanning velocity. Based on the resulting energy-density variations, the layer morphology is categorized into low-energy, optimal-energy, and high-energy regimes, establishing ground-truth criteria for subsequent classification. Nine groups of multi-parameter LPBF experiments are then performed to acquire layer-wise images and characterize the corresponding build quality, enabling the construction of a quantitative relationship among processing parameters, surface morphology, and final part quality. A multimodal augmented dataset (including geometric transformations, noise injection, and illumination adjustment) is built using the collected images. The YOLOv5s network is employed to learn the mapping between optical features of the layer morphology and the energy-input state, achieving real-time online recognition and prediction of quality categories. Experimental results show that after 100 training epochs, the model achieves over 97% classification accuracy (mAP > 0.90) for distinguishing high, medium, and low energy-density morphologies. This work elucidates the correspondence between LPBF processing parameters, optical layer morphology, and resultant build quality, providing an engineering-feasible pathway for online monitoring and real-time control of LPBF processes.

Key words: laser powder bed fusion (LPBF), in-situ monitoring, deep learning, layer-wise image classification, YOLO algorithm, prediction of forming quality

CLC Number:

TG665

LI Yulu, LI Junfeng, WAN Zhangyi, WEI Zhengying. Study on Classification and Recognition of Laser Powder Bed Fusion Forming Layer Morphology and Prediction of Forming Quality Based on YOLO Algorithm[J]. Journal of Mechanical Engineering, 2026, 62(3): 218-234.

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