Wu Ziqian, Li Qiling, Xu Zhenying
School of Mechanical Engineering, Jiangsu University, Zhenjiang, China.
3D Print Addit Manuf. 2023 Aug 1;10(4):723-731. doi: 10.1089/3dp.2021.0252. Epub 2023 Aug 9.
Laser welding quality forecast is highly significant during the laser manufacturing process. However, extracting the dynamic characteristics of the molten pool in the short laser welding process makes predicting of the welding quality in real time difficult. Accordingly, this study proposes a multimodel quality forecast (MMQF) method based on dynamic geometric features of molten pool to forecast the welding quality in real time. For extraction of geometric features of molten pool, an improved fully convolutional neural network is proposed to segment the collected dynamic molten pool images during the entire welding process. In addition, several dynamic geometric features of the molten pool are extracted by using the minimum enclosed rectangle algorithm with an evaluation of the performance by several statistical indexes. With regard to forecasting the welding quality, a nonlinear quadratic kernel logistic regression model is proposed by mapping the linear inseparable features to the high dimensional space. Experimental results show that the MMQF method can make an effective and stable forecast of welding quality. It performs well under small data and can satisfy the requirement of real-time forecast.
激光焊接质量预测在激光制造过程中具有重要意义。然而，在短激光焊接过程中提取熔池的动态特性使得实时预测焊接质量变得困难。因此，本研究提出了一种基于熔池动态几何特征的多模型质量预测（MMQF）方法，以实时预测焊接质量。为了提取熔池的几何特征，提出了一种改进的全卷积神经网络，用于在整个焊接过程中分割采集到的动态熔池图像。此外，利用最小包围矩形算法提取熔池的几个动态几何特征，并通过几个统计指标对性能进行评估。关于焊接质量预测，通过将线性不可分特征映射到高维空间，提出了一种非线性二次核逻辑回归模型。实验结果表明，MMQF方法能够对焊接质量进行有效且稳定的预测。它在小数据情况下表现良好，能够满足实时预测的要求。
