Development of a melt pool characteristics detection platform based on multi-information fusion of temperature fields and photodiode signals in plasma arc welding

Abstract

Melt pool characteristics reflect the formation mechanisms and potential issues of flaws. Long-term, high-precision, and real-time detection of melt pool characteristics is one of the major challenges in the industrial application of additive manufacturing technology. This work proposes, for the first time, the melt pool characteristics detection platform based on multi-information fusion in the plasma arc welding (PAW) process, which fully utilizes real-time photodiode signals and high-precision, information-rich melt pool temperature fields. By optimizing the detection area and wavelength selection of the platform, particularly through the unique photodiode signal acquisition system capable of detecting the high-sensitivity area of the melt pool, we effectively mitigate the influences of intense arc light and welding wire obstruction on the temperature signals and photodiode signals. Through applying machine learning, the trained model integrates photodiode signals with temperature signals from the high-sensitivity area, thereby achieving real-time acquisition of high-precision average temperature. By combining the fused signals collected from the platform and the scanning results from micro-computed tomography (CT), we evaluate and verify the influence of flaws and droplets on the melt pool characteristics, realizing the determination of flaw occurrence based on the abnormal variations of average temperature. The experimental results demonstrated that the platform fully utilized the advantages of long-term and real-time acquisition of the photodiode signal and the high-precision and information-rich of the melt pool temperature field, achieving long-term, high-precision, and real-time detection of melt pool characteristics.