A method of 3-D measuring fixture automatic assembly for auto-body part is presented. Locating constraint mapping technique and assembly rule-based reasoning are applied. Calculating algorithm of the position and pose...A method of 3-D measuring fixture automatic assembly for auto-body part is presented. Locating constraint mapping technique and assembly rule-based reasoning are applied. Calculating algorithm of the position and pose for the part model, fixture configuration and fixture elements in virtual auto-body assembly space are given. Transforming fixture element from itself coordinate system space to assembly space with homogeneous transformation matrix is realized. Based on the second development technique of unigraphics（UG）, the automated assembly is implemented with application program interface （API） function. Lastly the automated assembly of measuring fixture for rear longeron as a case is implemented.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
Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material use.This transition from traditional construction meth...Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material use.This transition from traditional construction methods not only accelerates building processes but also enhances working efficiencies globally.Despite its widespread adoption,the performance of industrialized building manufacturing(IBM)can still be optimized,particularly in enhancing time efficiency and reducing costs.This paper explores the integration of Artificial Intelligence(AI)and robotics at IBM to improve efficiency,cost-effectiveness,and material use in off-site assembly.Through a narrative literature review,this study systematically categorizes AI-based Robots(AIRs)applications into four critical stages—Cognition,Communication,Control,and Collab-oration and Coordination,and then investigates their appli-cation in the factory assembly process for industrialized buildings,which is structured into distinct stages:compo-nent preparation,sub-assembly,main assembly,finishing tasks,and quality control.Each stage,from positioning components to the integration of larger modules and subsequent quality inspection,often involves robots or human-robot collaboration to enhance precision and effi-ciency.By examining research from 2014 to 2024,the review highlights the significant improvements AI-based robots have introduced to the construction sector,identifies existing challenges,and outlines future research directions.This comprehensive analysis aims to establish more effi-cient,precise,and tailored construction processes,paving the way for advanced IBM.展开更多
Automated chemical solid-phase synthesis is an automation platform for rapid and reliable synthesis of glycans.Since the seminal work of Automated Glycan Assembly(AGA)disclosed by Seeberger in 2001,AGA has evolved fro...Automated chemical solid-phase synthesis is an automation platform for rapid and reliable synthesis of glycans.Since the seminal work of Automated Glycan Assembly(AGA)disclosed by Seeberger in 2001,AGA has evolved from a proof-of-concept to a robust and reliable technology for streamlined production of various types of glycans.Through more than 20 years of unceasing efforts,the major breakthroughs in AGA including linkers,approved building blocks,and synthesizers have been acquired,and numerous influential achievements have been made in complex glycan synthesis.In addition,the HPLC-assisted automated synthesis emerges as a promising automation platform to access glycans.In this review,we highlight the key advances in the field of automated chemical solid-phase synthesis,especially in AGA.The synthesis of representative glycans based on AGA is also described.展开更多
文摘A method of 3-D measuring fixture automatic assembly for auto-body part is presented. Locating constraint mapping technique and assembly rule-based reasoning are applied. Calculating algorithm of the position and pose for the part model, fixture configuration and fixture elements in virtual auto-body assembly space are given. Transforming fixture element from itself coordinate system space to assembly space with homogeneous transformation matrix is realized. Based on the second development technique of unigraphics（UG）, the automated assembly is implemented with application program interface （API） function. Lastly the automated assembly of measuring fixture for rear longeron as a case is implemented.
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Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material use.This transition from traditional construction methods not only accelerates building processes but also enhances working efficiencies globally.Despite its widespread adoption,the performance of industrialized building manufacturing(IBM)can still be optimized,particularly in enhancing time efficiency and reducing costs.This paper explores the integration of Artificial Intelligence(AI)and robotics at IBM to improve efficiency,cost-effectiveness,and material use in off-site assembly.Through a narrative literature review,this study systematically categorizes AI-based Robots(AIRs)applications into four critical stages—Cognition,Communication,Control,and Collab-oration and Coordination,and then investigates their appli-cation in the factory assembly process for industrialized buildings,which is structured into distinct stages:compo-nent preparation,sub-assembly,main assembly,finishing tasks,and quality control.Each stage,from positioning components to the integration of larger modules and subsequent quality inspection,often involves robots or human-robot collaboration to enhance precision and effi-ciency.By examining research from 2014 to 2024,the review highlights the significant improvements AI-based robots have introduced to the construction sector,identifies existing challenges,and outlines future research directions.This comprehensive analysis aims to establish more effi-cient,precise,and tailored construction processes,paving the way for advanced IBM.
文摘Automated chemical solid-phase synthesis is an automation platform for rapid and reliable synthesis of glycans.Since the seminal work of Automated Glycan Assembly(AGA)disclosed by Seeberger in 2001,AGA has evolved from a proof-of-concept to a robust and reliable technology for streamlined production of various types of glycans.Through more than 20 years of unceasing efforts,the major breakthroughs in AGA including linkers,approved building blocks,and synthesizers have been acquired,and numerous influential achievements have been made in complex glycan synthesis.In addition,the HPLC-assisted automated synthesis emerges as a promising automation platform to access glycans.In this review,we highlight the key advances in the field of automated chemical solid-phase synthesis,especially in AGA.The synthesis of representative glycans based on AGA is also described.
