###### Abstract  
The solidified state after the melting of the forming layer in the laser powder bed fusion (LPBF) directly reflects the final forming quality. Compared with the powder layer and the melt pool, it is easier to recognize and remove the defects of contour parts in time by monitoring and processing the solidified region after the melting of the forming layer. To explore the application of a solidified region image in defect contour detection of the forming layer, an improved image segmentation model based on TranUNet is designed to extract the image features of the solidified region as process data, on which basis this paper analyzes the similarities and differences between forming process data and CT scanning results data. Addressing the characteristics of large data volume and significant feature scale variation in the solidified region image obtained during the LPBF process, an SA-TransUNet semantic segmentation model integrating SE attention mechanism and ASPP multi-scale feature extraction module is developed to achieve high-precision solidified region image segmentation, with an _IoU_ and a dice coefficient index up to 94.24% and 97.02%, respectively. By extracting the solidified region image of the LPBF forming layer through this model and calculating the geometric feature values of its contour, a comparative analysis is conducted with the corresponding contour geometric feature values of the formed part CT scan image, which verifies the feasibility of the solidified region image extraction method proposed in this paper for contour defect detection. LPBF; solidified region image; semantic segmentation; SA-TransUNet; contour detection 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2018-201 which lead to defects such as changes in the diameter of the parts or displacement of some structural positions, resulting in problems such as surface roughness, overweight, and deformation of the structural components [6]. As these defects are too minute for post-processing repair methods to address effectively, monitoring the forming process is essential for anomalies detection in time and online control of forming quality [7; 8; 9]. The commonly employed methods for online monitoring include the surveillance of powder bed after recoating, melt pool, and forming layer [10; 11]. As the fundamental stacking unit in solid parts formation, the solidified condition of the forming layer directly reflects the final forming quality. In comparison with other online monitoring methods, the monitoring of the forming layer does not rely on ultra-high time and spatial resolution requirements and the results are more intuitive, making it easier to identify defects and achieve online control [12]. [PERSON] et al. [13] developed a Bayesian classifier to detect areas with poor fusion quality and high defect formation rate while enabling in situ visualization of pores and other defects during the forming process. [PERSON] et al. [14] devised a bi-stream Deep Convolutional Neural Network-based model to identify defects by integrating process image information of LPBF powder layers and part slices, achieving an accuracy rate as high as 99.4% in identifying LPBF defect conditions. [PERSON] et al. [15] utilized various Graph Cuts variant methods to detect edges in LPBF-forming layer images and used 3D model slice maps as benchmarks to verify recognition accuracy. [PERSON] et al. [16] employed an improved YOLOv5 neural network to train infrared images of the forming layer, which is achieving a detection accuracy (mAP) of 0.856 for various forming defects. Through monitoring and analyzing images of the forming layer, preliminary realization of defect identification and classification can be achieved [17]. However, these algorithms only have a certain effect on macroscopic defect recognition, making it difficult to carry out online control work based on this, and cannot meet the recognition requirements of edge defects in elaborate structural components.