Jie Liu

In robotic surface UV printing, the irregular shape of the workpiece and frequent curvature changes require the printing robot to maintain the nozzle’s perpendicular orientation to the surface during path planning, which imposes high demands on trajectory accuracy and path smoothness. To address this challenge, this paper proposes a reinforcement-learning-based path planning method. First, an ideal main path is defined based on the nozzle characteristics, and then a robot motion accuracy model is established and transformed into a Markov Decision Process (MDP) to improve path accuracy and smoothness. Next, a framework combining Generative Adversarial Imitation Learning (GAIL) and Soft Actor–Critic (SAC) methods is proposed to solve the MDP problem and accelerate the convergence of SAC training. Experimental results show that the proposed method outperforms traditional path planning methods, as well as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Specifically, the maximum Cartesian space error in path accuracy is reduced from 1.89 mm with PSO and 2.29 mm with GA to 0.63 mm. In terms of joint space smoothness, the reinforcement learning method achieves the smallest standard deviation, especially with a standard deviation of 0.00795 for joint 2, significantly lower than 0.58 with PSO and 0.729 with GA. Moreover, the proposed method also demonstrates superior training speed compared to the baseline SAC algorithm. The experimental results validate the application potential of this method in intelligent manufacturing, particularly in industries such as automotive manufacturing, aerospace, and medical devices, with significant practical value.

During the production process of inkjet printing labels, printing defects can occur, affecting the readability of product information. The distinctive shapes and subtlety of printing defects present a significant challenge for achieving high accuracy and rapid detection in existing deep learning-based defect detection systems. To overcome this problem, we propose an improved model based on the structure of the YOLOv5 network to enhance the detection performance of printing defects. The main improvements include the following: First, we introduce the C3-DCN module to replace the C3 module in the backbone network, enhancing the model’s ability to detect narrow and elongated defects. Secondly, we incorporate the Large Selective Kernel (LSK) and RepConv modules into the feature fusion network, while also integrating a loss function that combines Normalized Gaussian Wasserstein Distance (NWD) with Efficient IoU (EIoU) to enhance the model’s focus on small targets. Finally, we apply model pruning techniques to reduce the model’s size and parameter count, thereby achieving faster detection. Experimental results demonstrate that the improved YOLOv5 achieved a mAP@0.5 of 0.741 after training, with 323.2 FPS, which is 2.7 and 20.8% higher than that of YOLOv5, respectively. The method meets the requirements of high precision and high efficiency for printing defect detection.

To evaluate the potential values in heat dissipation applications, this study investigated the microstructure, mechanical and thermal properties of the Mg-0.5Ca-xZr alloys (x = 0.5 and 1 wt.%) under the as-cast, as-extruded and aged states. The phase constituents of the Mg alloys were examined by X-ray diffraction analysis, and the microstructure was inspected by optical microscopy and scanning electron microscopy. The thermal conductivity and mechanical properties of the Mg alloys were measured by the laser flash method and tensile tests, respectively. The results showed that the Mg alloys exhibited the equiaxed microstructure which is composed of α-Mg, Zr and compound Mg₂Ca. Both the extrusion process and increase of Zr content remarkably enhanced the mechanical strength of the Mg alloys and deteriorated the thermal performance simultaneously. It was also found that the thermal conductivity and mechanical strength of the Mg alloys increased gradually with the increase of aging time due to the higher precipitation of Zr and compound Mg₂Ca during the aging treatment. TheMg-0.5Ca-0.5Zr alloy aged at 473 K for 48 h demonstratedhigher thermal conductivity than the required values of the Mg alloys used as heat dissipation materials. Moreover, theMg-0.5Ca-0.5Zr alloy exhibited similar mechanical strength to the commonly-used Mg alloys, highlighting its potential become a potential heat dissipation material in the future due to its good combination of high thermal performance and mechanical strength.

The thermal conductivity of the Mg-1Mn-2Zn-xNd alloys (x = 0.5, 1.0, 1.5 wt. %) was studied for the potential applications of heat dissipation. The phase constituents were examined by X-ray diffraction analysis, and the microstructure was observed by light and scanning electron microscopes. The thermal conductivity of the Mg alloys was gauged at room temperature using laser flash method. The experimental results indicate that the thermal conductivity of both the cast and extruded Mg alloys decreases slowly with Nd content, and the extrusion process remarkably reduces the grain sizes and thermal conductivity of the Mg alloys. The thermal conductivity of cast Mg-1Mn-2Zn-xNd alloys exceeds the required critical value (100 W/(m·k)) for the cast Mg alloys. Among them, the cast Mg-1Mn-2Zn-1Nd alloy has great potential to be a good candidate of heat dissipation materials due to its good combination of thermal and mechanical properties.