Papers by Author: Reenal Ritesh Chand

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Authors: Young Jae Jeong, Hak Hyoung Kim, Reenal Ritesh Chand, Hyun Ho Na, Ji Hye Lee, Ill Soo Kim
Abstract: Determination of the optimal welding parameters to achieve specific weldments on a new material is usually an expensive and time consuming. To determine the welding parameters using Artificial Intelligence (AI) technologies, one must consider many factors including productivity, thermal input, defect formation, and process robustness. Determination of the welding parameters for pipeline welding is based on a skilled welders long-term experience rather than on a theoretical and analytical technique. In this paper, a smart system develops which determines welding parameters and position for each weld pass in pipeline welding based on one database and FEM model, two BP neural network models and a C-NN model. The preliminary test of the system has indicated that the system could determine the welding parameters for pipeline welding quickly, from which good weldments can be produced without experienced welding personnel. Experiments using the predicted welding parameters from the developed system proved the feasibility of interface standards and intelligent control technology to increase productivity, improve quality, and reduce the cost of system integration.
Authors: Reenal Ritesh Chand, Ill Soo Kim, Ji Hye Lee, Jong Pyo Lee, Ji Yeon Shim, Young Su Kim
Abstract: In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.
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