A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment

Abstract

Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits.

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing

“…To meet the real-time requirements of the material distribution scheduling system, an innovative "cloud-edge" computing architecture is adopted (as shown in the right half of Figure 2). This architecture effectively allocates computational needs between cloud and edge servers [37], balancing the optimization of computing resources and real-time performance. The cloud servers handle historical operational data, conducting large-scale data analysis and mining.…”

Towards an automatic classification of welding defect by convolutional neural network and robot classifier

“…We aim to develop a robot classifier in our project. The robot classifier [8], [9] will use the data set of differnts type of welding captured by the camera vision [10]. The robot will be propgarmed to put the scaned piece in the specified area depending on the detected type of defect.…”