Ill-Soo Kim

The Magnetic Pulse Welding (MPW) process uses only electromagnetic force to create a solid-state metallurgical bond between a working coil and outer workpiece. The electromagnetic force drives the outer tube to collide with the inner rod, resulting in successful bonding. However, due to the dissimilarity of the MPW joint, only a portion of the interface forms a metallurgical bond, which affects the quality of the joint. Therefore, the purpose of this study is to analyze the effects of process parameters on joint quality through experimental work using RSM. Furthermore, an optimization algorithm is utilized to optimize the process parameters used in magnetic pulse welding. A1070 aluminum and S45C carbon steel were used as the materials, while peak current, gap between working coil and outer tube, and frequency were chosen as the process parameters for MPW. The welding conditions are determined through experimental design. After welding, the maximum load and weld length are measured to analyze the effect of the process parameters, and a prediction model is developed. Specifically, to achieve a high-quality joint, the process parameters are optimized using the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA). The results reveal that the peak current is a significant parameter, and the developed prediction model exhibits high accuracy. Furthermore, the ICA algorithm proves very effective in determining the process parameters for achieving a high-quality Al/Steel MPW joint.

An electrolytic copper foil uses titanium drums, which exhibit excellent corrosion resistance and can produce thin electrodeposited copper foils. The appropriate technology for producing thin copper foils is required as electronic products become lighter, thinner, shorter, and smaller. Thus, the importance of titanium drum manufacturing technology, which affects the specifications of electrolytic copper foils, is emphasized. In this study, thermo-elastic-plastic analysis is performed to select an appropriate range of welding process parameters for titanium drums. In addition, using a range of welding process variables derived from analysis, GBO and GPS algorithms, which are optimization algorithms, are developed to derive objective and restriction functions as well as to select the optimal algorithm.

As lightweight automotive structures improve fuel efficiency and reduce carbon dioxide emission, they have garnered extensive attention. Vehicle mass reduction, which is a key problem for next generation eco-friendly vehicles, can significantly increase mileage. Hence, industries have committed to replace conventional materials with lightweight materials, such as advanced high strength steel. Additionally, automotive industries are hindered by challenges in the field of joining technology. A novel welding technology called resistance element welding (REW), which is an appropriate thermal-based joining method, was developed recently for joining hybrid materials with other structural steel grades. In this study, the state of the art joining and the process characteristics for dissimilar metal joints have been presented because related studies show limited investigation in this area. Following by the state of them, the principal and welding quality of REW, experimentally and numerically, are reported to give comprehensive information on the current practices and research interest related to technologies. Finally, extensive work was concentrated on portional joining optimization techniques to improve different materials.

The importance of the titanium drum manufacturing technology, which influences the specification of electrolytic copper foils, is being greatly emphasized, and an immediate research is required. The challenges in the current titanium drum production process are that the welding beads generated by the welding of the heat affected zone during the welding process may cause stains or discoloration during the generation of electrolytic copper foils, and hot spot defects may occur in the current density operation range. Consequently, the quality of the copper foil is significantly deteriorated. Therefore, it predicts the strain, stress, and safety rate by the heat transfer analysis of titanium drums for the electrolytic copper foil and optimizes welding process variables according to the case-by-case strain, stress, and safety rate by considering the inhibition of the residual stress.

Social interest in the 4th industry, intelligent factories, and smart manufacturing is continually growing along with the core technologies like big data and artificial intelligence, which can generate meaningful information by collecting and accumulating sensor data. Demand for industrial automation equipment is increasing worldwide due to the efforts needed to modernize manufacturing facilities, reduce automation and cycle time, and improve quality. Currently, the majority of research is focused on the development of automation facilities and improving productivity. The research on the contents of real-time data considering the characteristics of the cutting machine plasma machine is insufficient. In this study, based on the current data measured according to cutting current and cutting speed, a reference value for cutting quality is presented and the optimal process parameter has been selected. A model for predicting cutting quality by introducing the Mahalanobis Distance Method is presented. An attempt has been made to derive selection and optimal cutting process variables. Based on the predictive model, threshold values were specified and used in real-time data to consider the correlations between multivariate variables and evaluate the degree of scattering around the average of specific values of each variable. Also, process parameters suitable for surface roughness were calculated.