基于YOLO算法的激光粉末床熔融成形层形貌分类识别与成形质量预测研究

doi:10.3901/JME.260081

摘要/Abstract

摘要： 激光粉末床熔融(Laser powder bed fusion, LPBF)技术能够高精度制造复杂金属构件，其成形过程的质量波动与缺陷在线监测是目前研究的重点方向之一。本研究面向Ti-6Al-4V合金LPBF过程，构建了一种基于原位视觉感知的成形层形貌在线监测与分类识别方法，可实现对成形质量的预测。首先，通过单道熔道实验系统分析不同激光功率与扫描速度组合下的熔池行为及成形层光学形貌特征，将成形层形貌依据能量密度划分为低能区、适能区与高能区，为后续分类标注建立实验基准。随后开展9组不同工艺参数的成形实验，并采集逐层成形图像，表征成形质量，构建“工艺参数—成形层形貌—成形质量”之间的定量关联。基于采集的图像数据构建多模态增强数据集(包括几何增强、噪声注入与光照调整)，并采用YOLOv5s模型学习成形层光学特征与能量输入状态之间的映射关系，实现对成形质量区间的在线识别与预测。实验结果表明，模型在100个Epoch训练后，可对高、中、低能量密度形貌的识别达到 97% 以上准确率(mAP>0.90)。研究揭示了成形工艺参数驱动下的成形质量与成形层光学形貌之间的对应关系，为LPBF过程质量在线监测与实时调控提供了可工程化的技术路径。

关键词: 激光粉末床熔融, 在线监测, 深度学习, 成形层图像分类识别, YOLO算法, 成形质量预测

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

中图分类号:

TG665

李雨露, 李俊峰, 万章艺, 魏正英. 基于YOLO算法的激光粉末床熔融成形层形貌分类识别与成形质量预测研究[J]. 机械工程学报, 2026, 62(3): 218-234.

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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