Application of machine learning for seam profile identification in robotic welding

Abstract

This paper addresses critical challenges in automated robotic welding, emphasizing precise weld groove profiling for pipe welding applications. By integrating advanced laser scanning technology with the Local Outlier Factor (LOF) algorithm, the research effectively mitigates outliers and compensates for incomplete data—persistent issues in dynamic manufacturing environments. To further enhance accuracy, a robust neural network model is employed to predict weld groove alignment, a crucial factor in maintaining weld structural integrity. The LOF algorithm was chosen for its ability to detect spatial anomalies, ensuring the exclusion of erroneous data that could compromise welding precision. Experimental results demonstrate that the combined use of LOF and neural networks significantly improves the operational efficiency of robotic welding, delivering consistently strong and precise welds across diverse manufacturing scenarios. The model achieved an average mean square error of 0.078 and an R² value of 0.995, accurately predicting 99.5 % of data. Therefore, neural network modeling enables accurate interpolation of missing data and real-time adjustments to varying operational conditions.