With growing interest in Laser Additive Manufacturing (LAM) of High-entropy alloys (HEAs) during most recent years, the design of compositional elements and process strategies are primary methods to overcome undesirable microstructures and defects. Here we propose a new approach, a novel real-time Laser Shocking of Melt Pool (LSMP), to obtain melt pool modifications for yielding HEAs with desired characteristics. LSMP utilizes a pulsed laser shocking a liquid melt pool caused by a continuous wave laser, enabling non-destructive and real-time modulations for high-performance HEAs. The numerical simulation reveals the convection mechanism of the melt pool in the LSMP process, and the intervention of the pulsed laser promotes melt pool flow type to convert the Marangoni effect into a multi-convective ring, which accelerates melt pool flow and inhibits columnar crystal growth. Experimental results show the evolution law of the microstructure in the LSMP process. The microstructure of CrFeCoNi HEAs undergoes a Columnar-Equiaxed Transition (CET), and higher hardness is obtained. Laser shock is demonstrated to be an effective in-situ modulative tool for controlled additive manufacturing.
摘要：A machine-vision-based method of locating crops is described in this research. This method was used to provide real-time positional information of crop plants for a mechanical intra-row weeding robot. Within the normalized red, green, and blue chromatic coordinates (rgb), a modified excess green feature (g-r>T & g-b>T) was used to segment plant material from back ground in color images. The threshold T was automatically selected by the maximum variance (OTSU) algorithm to cope with variable natural light. Taking into account the geometry of the camera arrangement and the crop row spacing, the target regions covering the crop rows were defined based on a pinhole camera model. According to the statistical variation in the pixel histogram in each target region, locations of the crop plants were initially estimated. To obtain the accurate locations of crops, median filtering was conducted locally in the bounding boxes of the crops close to the bottom of the images. For the lateral guidance of the robot, a novel method of calculating lateral offset was proposed based on a simplified match between a template and the detected crops. Field experiments were conducted under three different illumination conditions. The results showed that the accurate identification rates on lettuce, cauliflower and maize were all above 95%. The positional error as within ±15 mm, and the average processing time for a 640×480 image was 31 ms. The method was adequate to meet the technical requirement of the weeding robot, and laid a foundation for robotic weeding in commercial production system.