This work aims to improve the low accuracy caused by interference information during real-time weld surface detection. Firstly, a weld surface dataset with 7580 pictures containing 8 types of defects was established. Secondly, an improved detection model named Yolo-MSAPF was designed based on Yolo-v5 model and verified by the self-established database. Finally, a real-time detection system was built to analyze the performance of the detection model in an industrial environment. The design principle of the Yolo-MSAPF is to eliminate interference information but enhance necessary features in each scale as much as possible by multi-scale alignment fusion (MSAF) with parallel features filtering (PFF) modules (i.e., MSAPF strategy). For the MSAF, not only the accuracy but the richness of fused features are guaranteed by aligning features at one level to fuse all other scales. After that, the fused features in each scale were individually filtered out in parallel spatial and channel in the PFF module. The results show that rate for the images with missing detection for 8 types of defects sharply drops from 21.47% to 1.68% when the MSAPF strategy is used. Moreover, it is worth noting that the mAP @0.5 of the Yolo-MSAPF model reaches 95.3%, which is similar to the Yolo-v7 model, while the number of parameters is reduced by 30.1%, compared with the baseline model. Additionally, the great ability to screen out unqualified weld is also verified in the industrial environment. Soon, code and dataset will be available at https://github.com/Luckycat518/Yolo-MSAPF.

In this letter, we propose a novel Non-Line-of-Sight (NLOS) identification and error-mitigation method for dynamic object positioning and ultra-wideband (UWB) ranging. By utilizing inverse estimation on known Anchor Points (APs) and improved robust unscented Kalman filter (IRUKF), while fusing Gyroscope and Accelerometer data, the proposed technology identifies and compensates for NLOS occlusions between tag and APs, reducing positioning errors. The approach has been verified through simulation and experiment, with identification precision of 97.02%. After mitigating errors, substantial error reductions of 91.80% and 98.90% were observed in LOS and NLOS situations, respectively. Moreover, the developed IRUKF effectively minimizes mislocalization by 50.48% in harsh scenarios.

The mitigation of the drift of the odometer and the ghost trail effect in the map is a challenge for simultaneous localization and mapping (SLAM). Facing the challenge, a novel method, called LIWOM-GD, is proposed via factor graph optimization, which tightly fuses LiDAR, IMU, and wheel encoder measurements. Firstly, the front-end module provides undistorted LiDAR scans, the pose states to be optimized, and the LiDAR-Inertial (LI) odometry factor. Secondly, the point cloud and pre-integration processing module implements ground segmentation, IMU, and encoder pre-integration to form the ground plane, IMU, and encoder factors. Furthermore, an enhanced dynamic point removal method is integrated into the processing module. Thirdly, a typical loop closure detection method is used to obtain a loop closure factor. Finally, all the above factors are added into the factor graph to obtain optimized results based on the GTSAM library. The proposed SLAM system tightly fuses three sensor measurements based on the factor graph, adding global plane constraint and dynamic points removal module based on spatiotemporal normals. Experimental tests are conducted on both benchmark and self-collected datasets. The results demonstrate that our method not only achieves more robust localization and mapping in degraded environments but also effectively implements dynamic point removal compared with the state-of-the-art SLAM methods.

The essence of the difficulties for weld surface detection is that there is a lot of interference information during detection. This study aims to enhance the detection accuracy while keeping great deployment capabilities of a detection model for weld surface defects. To achieve this goal, an improved Yolo-graph convolution head (GCH) model is proposed based on the stable and fast Yolo-v5. The improvements primarily involve introducing a graph convolution network combined with a self-attention mechanism in the head part (i.e. GCH). This component focuses on improving the insufficient recognition capability of convolutional neural networks for similar defects in complex environments. Furthermore, to address the presence of potentially ambiguous samples in complex welding environments, the label assignment strategy of simOTA is implemented to optimize the anchor frame. Additionally, a streamlined structure, aiming to improve model detection speed while minimizing performance impact, has been designed to enhance the applicability of the model. The results demonstrate that the cooperation of GCH and simOTA significantly improves the detection performance while maintaining the inference speed. These strategies lead to a 2.5% increase in mAP@0.5 and reduce the missing detection rates of weld and 8 types of defects by 32.9% and 84.1% respectively, surpassing other weld surface detection models. Furthermore, the impressive applicability of the model is verified across four scaled versions of Yolo-v5. Based on the proposed strategies, the FPS increases by more than 30 frames in the fast s and n versions of Yolo-v5. These results demonstrate the great potential of the model for industrial applications.

This study takes large size samples after hot-upsetting as research objects and aims to investigate the optimization double-stage annealing parameters for improving the mechanical properties of hot-upsetting samples. The double-stage annealing treatments and uniaxial tensile tests for hot-upsetting GH4169 superalloy were finished firstly. Then, the fracture mode was also studied. The results show that the strength of hot-upsetting GH4169 superalloy can be improved by the double-stage annealing treatment, but the effect of annealing parameters on the elongation of GH4169 alloy at high temperature and room temperature is not significant. The fracture mode of annealed samples at high-temperature and room-temperature tensile tests is a mixture of shear fracture and quasi-cleavage fracture while that of hot-upsetting sample is a shear fracture. The macroscopic expressions for the two fracture modes belong to ductile fracture. Moreover, it is also found that the improvement of strength by the double-stage annealing treatment is greater than the single-stage annealing treatment. This is because the homogeneity of grains plays an important role in the improvement of strength for GH4169 superalloy when the average grain size is similar. Based on a comprehensive consideration, the optimal annealing route is determined as 900 °C × 9–12 h(water cooling) + 980 °C × 60 min(water cooling).

Typically, in the manufacturing of GH4169 superalloy forgings, the multi-process hot forming that consists of pre-deformation, heat treatment and final deformation is required. This study focuses on the microstructural evolution throughout hot working processes. Considering that δ phase can promote nucleation and limit the growth of grains, a process route was designed, including pre-deformation, aging treatment (AT) to precipitate sufficient δ phases, high temperature holding (HTH) to uniformly heat the forging, and final deformation. The results show that the uneven strain distribution after pre-deformation has a significant impact on the subsequent refinement of the grain microstructure due to the complex coupling relationship between the evolution of the δ phase and recrystallization behavior. After the final deformation, the fine-grain microstructure with short rod-like δ phases as boundaries is easy to form in the region with a large strain of the pre-forging. However, necklace-like mixed grain microstructure is formed in the region with a small strain of the pre-forging. In addition, when the microstructure before final deformation consists of mixed grains, dynamic recrystallization (DRX) nucleation behavior preferentially depends on kernel average misorientation (KAM) values. A large KAM can promote the formation of DRX nuclei. When the KAM values are close, a smaller average grain size of mixed-grain microstructure is more conductive to promote the DRX nucleation. Finally, the interaction mechanisms between δ phase and DRX nucleation are revealed.