Precise 3-D Temperature Field Reconstruction of Automotive Aluminum Forging Dies Using Deep Learning and Heterogeneous Multiview Vision

Three-dimensional temperature field monitoring of automotive aluminum alloy component forging dies under high temperature conditions is critical for process optimization and service life prediction. Addressing the limitations of existing single-point temperature measurement or two-dimensional thermal imaging technologies, which cannot provide complete three-dimensional temperature field information, this paper proposes a three-dimensional temperature field reconstruction method based on multi-sensor thermal field fusion. First, a heterogeneous three-camera system comprising two visible light cameras and one infrared thermal imaging camera was established. Then, a specially designed calibration plate with black-and-white checkerboard temperature differences was used to jointly calibrate the two visible light cameras and the infrared thermal imaging camera. Next, the three-dimensional geometric model of the mold is obtained using multi-view point cloud reconstruction technology from the two visible light cameras. To improve matching accuracy in areas with missing complex textures, an improved deep learning-based stereo matching algorithm, RAFT-Stereo, is introduced to overcome thermal noise issues. Furthermore, the two-dimensional temperature information collected by the infrared thermal imager is mapped to the three-dimensional point cloud. Through infrared-visible light camera multi-view geometric transformation temperature field fusion technology, a complete three-dimensional temperature field model containing both spatial coordinates and temperature attributes is generated. Experimental results show that compared to traditional stereo matching methods SGBM and PMS, the geometric reconstruction accuracy of this method has been improved by an average of approximately 68.2% and 51.9%, respectively; after fusion, the average accuracy of the three-dimensional temperature field temperature mapping reaches ±0.3°C. This method provides high-precision three dimensional data support for the thermal behavior analysis and thermal management of forging dies.