Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomic...Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes.However,if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool,traditional rotary-turning machining methods are not applicable,and advanced computerized numerical control(CNC)machining methods,such as the slow-tool-servo method,must be im-plemented.This article proposes a non-conventional offset(NCO)fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts.This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount.NCO fab-rication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic sur-faces.In addition,corresponding solutions for other types of aspherical surfaces are proposed using the NCO method.The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared.A specific measurement scheme for the NCO method is presented,and the experimental results indicate that the PV and RMS form errors are 0.658μm and 60 nm,respectively.This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
The surfaces of brittle materials are susceptible to defects such as scratches,cracks,and chipping during con-ventional grinding processes,which significantly compromise surface quality and service performance.A flexi...The surfaces of brittle materials are susceptible to defects such as scratches,cracks,and chipping during con-ventional grinding processes,which significantly compromise surface quality and service performance.A flexible ball-end body-armor-like abrasive tool(BAAT)can effectively remove micro-convex peaks from the surfaces of brittle materials by employing a high tangential grinding force and a low normal grinding force,thereby achieving nano-level surface roughness and ultra-smooth mirror finishes.However,the surface contact me-chanism,pressure distribution pattern,and grinding force behavior between BAAT and workpiece remain in-adequately understood.This study examines the mechanism of liquid film formation and the distribution pattern of elastohydrodynamic pressure in high-shear and low-pressure grinding areas,drawing on the theories of elastohydrodynamic lubrication,non-Newtonian fluid dynamics,and material mechanics.A high-shear low-pressure grinding force model,which incorporates elastohydrodynamic liquid film thickness and abrasive grain size,was developed.The effects of the main grinding parameters(normal load,spindle rotational speed,and abrasive grain size)on the tangential grinding force were investigated through the processing of lithium niobate crystals using an intelligent precision-grinding system.The experimental results indicated that the relative error between the predicted and experimental values was 10.74%,thereby confirming the accuracy of the grinding force model.This study advances the understanding of elastohydrodynamic lubrication mechanisms in abrasive machining and provides a crucial theoretical foundation for the application of flexible ball-end BAAT.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
行道树是城市森林的重要组成部分,在冠层截留降雨方面起着十分重要的作用,研究城市行道树冠层截留雨水的能力和价值有利于对其进行筛选。通过i-Tree tools对湛江霞山区、开发区、赤坎区3个中心主城区的行道树抽样调查,进行定量的冠层截...行道树是城市森林的重要组成部分,在冠层截留降雨方面起着十分重要的作用,研究城市行道树冠层截留雨水的能力和价值有利于对其进行筛选。通过i-Tree tools对湛江霞山区、开发区、赤坎区3个中心主城区的行道树抽样调查,进行定量的冠层截留雨水生态效益分析评估。湛江市区种植有行道树种67种,分别隶属于52个属,其中细叶榕、小叶榄仁、大王椰子、非洲楝、椰子数量最多,除细叶榕之外,其他树种的比例均<10%;行道树胸径范围以7.6~15.2、15.2~30.5、30.5~45.7 cm这3个径级为主,其中胸径15.2~30.5 cm的占42.7%;湛江市行道树叶面积总计8600854 m 2,树冠覆盖面积达2905896 m 2;单株叶面积最大的为黄葛树,其次为非洲楝;湛江市行道树年冠层截留量达689041.96 m 2,获得的经济效益为350.2万元,平均单株年冠层截留降雨量获得的效益为43.4元,其中单株效益最高的是黄葛树,其次是非洲楝,再是细叶榕。较大的叶面积及冠幅对于增强冠层截留雨水能力起到很大作用,在暴雨成灾的南方,可以选择黄葛树、非洲楝和细叶榕这些冠层截留率大的行道树。展开更多
基金Supported by National Key R&D Program of China(Grant No.2023YFE0203800)the National Natural Science Foundation of China(Grant No.52105482).
文摘Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes.However,if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool,traditional rotary-turning machining methods are not applicable,and advanced computerized numerical control(CNC)machining methods,such as the slow-tool-servo method,must be im-plemented.This article proposes a non-conventional offset(NCO)fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts.This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount.NCO fab-rication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic sur-faces.In addition,corresponding solutions for other types of aspherical surfaces are proposed using the NCO method.The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared.A specific measurement scheme for the NCO method is presented,and the experimental results indicate that the PV and RMS form errors are 0.658μm and 60 nm,respectively.This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金Supported by National Natural Science Foundation of China(Grant Nos.52575516,51875329)Taishan Scholar Special Foundation of Shandong Province(Grant Nos.tstp20240826,tsqn201812064)+2 种基金Shandong Provincial Natural Science Foundation(Grant No.ZR2023ME112)Key Research and Development Project of the Ningxia Hui Autonomous Region(Grant No.2024BEE02019)Innovation Capacity Improvement Programme for High-tech SMEs of Shandong Province(Grant Nos.2022TSGC1333,2022TSGC1261).
文摘The surfaces of brittle materials are susceptible to defects such as scratches,cracks,and chipping during con-ventional grinding processes,which significantly compromise surface quality and service performance.A flexible ball-end body-armor-like abrasive tool(BAAT)can effectively remove micro-convex peaks from the surfaces of brittle materials by employing a high tangential grinding force and a low normal grinding force,thereby achieving nano-level surface roughness and ultra-smooth mirror finishes.However,the surface contact me-chanism,pressure distribution pattern,and grinding force behavior between BAAT and workpiece remain in-adequately understood.This study examines the mechanism of liquid film formation and the distribution pattern of elastohydrodynamic pressure in high-shear and low-pressure grinding areas,drawing on the theories of elastohydrodynamic lubrication,non-Newtonian fluid dynamics,and material mechanics.A high-shear low-pressure grinding force model,which incorporates elastohydrodynamic liquid film thickness and abrasive grain size,was developed.The effects of the main grinding parameters(normal load,spindle rotational speed,and abrasive grain size)on the tangential grinding force were investigated through the processing of lithium niobate crystals using an intelligent precision-grinding system.The experimental results indicated that the relative error between the predicted and experimental values was 10.74%,thereby confirming the accuracy of the grinding force model.This study advances the understanding of elastohydrodynamic lubrication mechanisms in abrasive machining and provides a crucial theoretical foundation for the application of flexible ball-end BAAT.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
文摘行道树是城市森林的重要组成部分,在冠层截留降雨方面起着十分重要的作用,研究城市行道树冠层截留雨水的能力和价值有利于对其进行筛选。通过i-Tree tools对湛江霞山区、开发区、赤坎区3个中心主城区的行道树抽样调查,进行定量的冠层截留雨水生态效益分析评估。湛江市区种植有行道树种67种,分别隶属于52个属,其中细叶榕、小叶榄仁、大王椰子、非洲楝、椰子数量最多,除细叶榕之外,其他树种的比例均<10%;行道树胸径范围以7.6~15.2、15.2~30.5、30.5~45.7 cm这3个径级为主,其中胸径15.2~30.5 cm的占42.7%;湛江市行道树叶面积总计8600854 m 2,树冠覆盖面积达2905896 m 2;单株叶面积最大的为黄葛树,其次为非洲楝;湛江市行道树年冠层截留量达689041.96 m 2,获得的经济效益为350.2万元,平均单株年冠层截留降雨量获得的效益为43.4元,其中单株效益最高的是黄葛树,其次是非洲楝,再是细叶榕。较大的叶面积及冠幅对于增强冠层截留雨水能力起到很大作用,在暴雨成灾的南方,可以选择黄葛树、非洲楝和细叶榕这些冠层截留率大的行道树。
