Byeong-Ju Jin

A Study on HAZ Softening Characteristics of Fiber Laser Weldment for High-Strength Steel

Laser welding sector in the automotive industry has been widely recognized as one of the most important bonding processes, such as parts welding. Efforts to improve productivity and weld quality have been progressing steadily. In addition, laser welding is suitable for welding process that can produce high-quality welds suitable for flexible production and small quantity batch productions. In order to ensure the rigidity of the material, high strength material are applied to more than 1 GPa class body parts and automotive bumper beams. However, not only the situation is that the trend of domestic research, but also development is based on product molding considering freedom of shape where reinforcement is applied to meet the safety regulations and high-speed crash performance, despite the use of high strength materials. The tendency for heat-affected zone (HAZ) softening phenomenon common in areas of laser welded high tensile steel welding confirmed the occurrence of weld softening effect according to the process parameters. Based on this, range of process parameters could be selected for ensuring weld quality.

Control of the weld quality using weldingparameters in a robotic welding process

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process.

A Study on Development of the Optimization Algorithms to Find the Seam Tracking

Abstract The Gas Metal Arc(GMA) welding, called Metal Inert Gas(MIG) welding, has been an important componentin manufacturing industries. A key technology for robotic welding processes is seam tracking system, whichis critical to improve the welding quality and welding capacities. The objectives of this study were to developthe intelligent and cost-effective algorithms for image processing in GMA welding which based on the laservision sensor. Welding images were captured from the CCD camera and then processed by the proposed algorithm to track the weld joint location. The proposed algorithms that commonly used at the present stage were verifiedand compared to obtain the optimal one for each step in image processing. Finally, validity of the proposed algorithms was examined by using weld seam images obtained with different welding environments for image processing. The results proved that the proposed algorithm was quite excellent in getting rid of the variable noises to extract the feature points and centerline for seam tracking in GMA welding and could be employed for general industrial application. Key Words : GMA welding process, Seam tracking, Image process, Laser vision sensorISSN 2466-2232Online ISSN 2466-2100