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Chung-Che Chou,Gee‑Jin Yu,Kung‑Juin Wang,Wei‑Tze Chang,Chiun‑Lin Wu,Charlene Chin‑Jie Zhao,Chun‑Yao Yang,Ming‑Ti Chou 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.9
This paper presents a study to investigate the feasibility of using robotic welding technologies to weld the continuity plate and the column flange plate during manufacturing steel built-up box columns in buildings. Specimens designed to emulate the key components in the steel beam-to-column moment connections for Special moment frame were fabricated and welded using a proposed automated procedure that performs multi-layer, multi-pass welding for thick steel plates. Effects of controlling parameters on robotic welding results that includes the manner of bead stacking, wire feed speed, travel speed, working angle, arc voltage, the path of the welding pass, and the methods to start and finish welding passes were investigated. The quality of the welded products was assessed by visual inspection and ultrasonic testing (UT). Further mechanical tests including tensile tests, bending tests, and cyclic loading tests were carried out on selected welded products that passed the UT examination. The test results indicated that the robotic welds showed no visible damage or cracks, met requirements specified in the AWS specification, and exhibited satisfactory strength and ductility.
Grey Neural Network-Based Forecasting System for Vision-Guided Robot Trajectory Tracking
Shih-Hung Yang,Chung-Hsien Chou,Chen-Fang Chung,Wen-Pang Pai,Tse-Han Liu,Yung-Sheng Chang,Jung-Che Li,Huan-Chan Ting,Yon-Ping Chen 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
This paper presents a grey neural network-based forecasting system (GNNFS) in solving the prediction problem. GNNFS adopts a grey model to predict the signal and a neural network (NN) to forecast the prediction error of the grey model. A sequential batch learning (SBL) is developed to adjust the weights of the NN. The proposed GNNFS is applied to a binocular robot, called an Eye-Robot, for human-robot interaction which involved predicting the trajectory of a participant’s hand and tracking the hand. By applying the SBL, the GNNFS can gradually learn to predict the trajectory of the hand and track it well. The experimental results show that the GNNFS can carry out the SBL in real-time for vision-guided robot trajectory tracking.