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
论述了基于Creo三维平台的零件快速变型设计系统的二次开发,并针对此系统开发中的关键技术和系统功能进行了详细的介绍。在Visual Studio 2010环境下,利用Creo软件本身提供的二次开发工具Creo/TOOLKIT,结合MFC技术设计编写了具有通用性...论述了基于Creo三维平台的零件快速变型设计系统的二次开发,并针对此系统开发中的关键技术和系统功能进行了详细的介绍。在Visual Studio 2010环境下,利用Creo软件本身提供的二次开发工具Creo/TOOLKIT,结合MFC技术设计编写了具有通用性的零件快速变型设计系统。以带颈法兰的变型设计为例,介绍了系统的具体功能。工程应用表明,该系统能够对零件进行快速变型设计,同时也为设计人员快速设计新的零件产品及实现产品的系列化提供了新方法,减小了设计人员的工作量,提高了设计效率。展开更多
提高设计效率的角度出发,对如何在Creo平台下实现零件的参数化变型设计进行了探究,并以一种新的方法实现了零件的参数化变型设计和零件的系列化。结合Visual Studio 2010集成开发环境和Creo软件二次开发工具Creo/TOOLKIT,利用MFC技术实...提高设计效率的角度出发,对如何在Creo平台下实现零件的参数化变型设计进行了探究,并以一种新的方法实现了零件的参数化变型设计和零件的系列化。结合Visual Studio 2010集成开发环境和Creo软件二次开发工具Creo/TOOLKIT,利用MFC技术实现了零件的参数化变型设计。以阶梯轴的参数化变型设计为例,对该方法进行了验证,结果表明该方法避免了设计者的重复劳动,提高了零件的变型设计效率,更好的实现零件产品的系列化。展开更多
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.
文摘论述了基于Creo三维平台的零件快速变型设计系统的二次开发,并针对此系统开发中的关键技术和系统功能进行了详细的介绍。在Visual Studio 2010环境下,利用Creo软件本身提供的二次开发工具Creo/TOOLKIT,结合MFC技术设计编写了具有通用性的零件快速变型设计系统。以带颈法兰的变型设计为例,介绍了系统的具体功能。工程应用表明,该系统能够对零件进行快速变型设计,同时也为设计人员快速设计新的零件产品及实现产品的系列化提供了新方法,减小了设计人员的工作量,提高了设计效率。
文摘提高设计效率的角度出发,对如何在Creo平台下实现零件的参数化变型设计进行了探究,并以一种新的方法实现了零件的参数化变型设计和零件的系列化。结合Visual Studio 2010集成开发环境和Creo软件二次开发工具Creo/TOOLKIT,利用MFC技术实现了零件的参数化变型设计。以阶梯轴的参数化变型设计为例,对该方法进行了验证,结果表明该方法避免了设计者的重复劳动,提高了零件的变型设计效率,更好的实现零件产品的系列化。
