This study proposed a conditional generative adversarial network (cGAN) model for predicting steel plate deformation based on heating line positions in a line heating process. A database was constructed by performing finite element analysis (FEA) to establish relationships between heating line positions and deformation shapes. Deformation shapes were converted into color map images. Heating line positions were used as conditional labels for training and validating the proposed model.
During the training process, generator and discriminator loss values, along with MSE and R² metrics, converged stably, demonstrating that generated images closely resembled the actual data. Validation results showed that predicted deformation magnitudes had an average relative error of approximately 3% and a maximum error of less than 7%. These findings confirm that the proposed model can effectively predict steel plate deformation shapes based on heating line positions in the line heating process, making it a reliable predictive tool for this application.

When a narrow gap was formed under appropriate welding conditions in the steel pipe manufacturing process using highfrequency resistance welding, temperature distribution was analyzed to predict the length of the gap. Assuming the length of the gap from the apex point to the welding point at an applied voltage, and calculating the temperature distribution around the gap, the length of the gap with an appropriate fusion width at the welding point could be estimated. Along with this, the current density and magnetic flux density distributions that appeared in the narrow gap were obtained according to the change in the applied voltage, and the distribution shape and size of the electromagnetic force acting on the gap were also predicted. The current density, magnetic flux density, and electromagnetic force gradually increased along the narrow gap, showing the maximum value at the welding point. In the temperature distribution in the narrow gap, the surface of the front end began to melt at an appropriate applied voltage, and the melting width was the largest at the welding point. As the applied voltage increased, the narrow gap became longer, and the appropriate gap length appeared in proportion to the applied voltage.

The high-frequency electric resistance welding (HF-ERW) process is widely used in the steel pipes production because it can weld at a high speed, has excellent weldability, and attains clean and precise shapes. However, for process improvement, analytic studies on electromagnetic field and temperature distributions, and selection of appropriate process variables are required. In this study, finite element analysis models that can analyze the electromagnetic field distribution and temperature distribution in the HF-ERW of a steel pipe were proposed, in consideration of the characteristics of the process, including electromagnetic phenomena localized to the workpiece surface and fast welding speed. By applying the proposed analysis models, changes in current density, magnetic flux density, generated heat density, and fused width in the pipe could be predicted according to changes in process variables such as the V angle of the strip, the electrode position, and the source voltage. Through comparison with the analysis and the limited-case experiment, the analysis result predicted the actual fused width fairly well, and the validity of the proposed model could be verified.

In order to develop an automatic system for welding thin steel plates with curvature such as the corrugated membranes of an LNG storage tank, a rotating mechanism should be firstly designed for the torch to easily follow the weld seam with a constant distance and angle. In this study, a torch rotating mechanism consisting of three circular links, two square-type links and a torch link was proposed for automation of the welding process. A weld-seam tracking system with two axis slides and the proposed rotating mechanism was successfully simulated with a dynamic simulation software. A prototype tracking system was manufactured and a tracking test with the system was then carried out. The test results with tracking system showed that the rotating mechanism could be implemented and it was feasible to be used in automatic tracking of weld seam with curvature.

An inductive sensor system is proposed to detect the gear location and angular position of a geared shaft for automatic feeding of the shaft into the proper cutting position of the other end. The system consists of two set of coils, bridge circuit, signal condition circuit, and microprocessor. The coil sensors of the system measure changes of inductance along with the surface position of a geared shaft. The inductance changes are transformed to voltages by the bridge circuit, which are then conditioned and processed for the recognition of the gear. In order to incorporate with the experimental results with the sensor system, a finite element method (FEM) simulation for the magnetic field between the sensor and the shaft was carried out. The predicted results and the experiments revealed that the sensor system was appropriate for sensing the position of gear and the angular position of gear tooth of a geared shaft.

Heat treatment with carburized quenching process is widely used for automotive helical gear to improve its surface properties of hardness and strength. However, the gear can be deformed with the process over the allowable tolerance, which possibly makes noise, vibration and heat problems in operation. In this study, deformation of helical gear during heat treatment of carburized quenching was analyzed with a numerical method, incorporating coupled calculations of thermal conduction, carbon diffusion, phase transformation and thermal stresses. With the analysis, the effect of coolant temperature in quenching on the deformation was investigated. The result of the analysis revealed that the higher the coolant temperature became, the more change of helix angle and the more compressive stresses in the surface generated, because of delayed generation of martensite in the part.

A robust motion controller based on self-organizing fuzzy control (SOFC) and feed-back tracking control technique is proposed for a two-wheel driven mobile robot. The feed-back control technique of the controller guarantees the robot follows a desired trajectory. The SOFC technique of the controller deals with unmodelled dynamics of the vehicle and uncertainties. The computer simulations are carried out to verify the tracking ability of the proposed controller with various driving situations. The results of the simulations reveal the effectiveness and stability of the proposed controller to compensate the unmodelled dynamics and uncertainties.

Transplanting accuracy of a rice transplanter mainly depends on the trajectory of the hoe for picking, conveying and transplanting of seedlings as well as the return motion. The trajectory can be decided and prescribed to be suitable in treating seedlings for a prevailing soil condition. For the purpose of the transplanting accuracy, the design of a transplanting mechanism would be carried out using a planetary-gear-train system instead of the four bar linkage system. In this study, a design method of transplanting mechanism is theoretically proposed by synthesizing a noncircular planetary-gear-train system for the tool (hoe) to trace a prescribed trajectory.
The method utilizes an optimization approach to decide the lengths of an arm and a tool, the inverse kinematics to figure out the configuration angles of the two links, the roll contact condition in transmitting motion between the gears, and a linearization approach to obtain the shapes of the gears. Based on the proposed method, the shapes of the gears and the lengths of the tools of the planetary-gear-train system are determined for three prescribed trajectories. A kinematical simulation with a commercialized package program is also carried out to confirm that the gear-train system synthesized with the proposed method is able to trace the prescribed trajectory.