论文标题
用于确定气体金属弧焊接T-关节的焊片形状参数的机器学习模型 - 比较研究
Machine learning models for determination of weldbead shape parameters for gas metal arc welded T-joints -- A comparative study
论文作者
论文摘要
焊珠的形状对于评估焊接接头的质量至关重要。特别是，这对从数值分析获得的结果的准确性产生了重大影响。 This study focuses on the statistical design techniques and the artificial neural networks, to predict the weld bead shape parameters of shielded Gas Metal Arc Welded (GMAW) fillet joints.在厚度为3mm至10mm的厚度的低碳低碳钢板上进行了广泛的测试。焊接电压，焊接电流和移动的热源速度被认为是焊接参数。 Three types of multiple linear regression models (MLR) were created to establish an empirical equation for defining GMAW bead shape parameters considering interactive and higher order terms. Additionally, artificial neural network (ANN) models were created based on similar scheme, and the relevance of specific features was investigated using SHapley Additive exPlanations (SHAP).结果表明，在可预测性和错误评估方面，基于MLR的方法的性能优于基于ANN的模型。这项研究表明了预测工具在焊接的数值分析中的有用性。
The shape of a weld bead is critical in assessing the quality of the welded joint. In particular, this has a major impact in the accuracy of the results obtained from a numerical analysis. This study focuses on the statistical design techniques and the artificial neural networks, to predict the weld bead shape parameters of shielded Gas Metal Arc Welded (GMAW) fillet joints. Extensive testing was carried out on low carbon mild steel plates of thicknesses ranging from 3mm to 10mm. Welding voltage, welding current, and moving heat source speed were considered as the welding parameters. Three types of multiple linear regression models (MLR) were created to establish an empirical equation for defining GMAW bead shape parameters considering interactive and higher order terms. Additionally, artificial neural network (ANN) models were created based on similar scheme, and the relevance of specific features was investigated using SHapley Additive exPlanations (SHAP). The results reveal that MLR-based approach performs better than the ANN based models in terms of predictability and error assessment. This study shows the usefulness of the predictive tools to aid numerical analysis of welding.
