With rapid growth of the global electric vehicle market, interest in the development of secondary batteries such as lithium batteries is also increasing. Core functional parts of secondary batteries are known to determine the performance of these batteries. Micro cracks, scratches, and markings that may occur during the manufacturing process must be checked in advance. As part of developing an automated inspection system based on machine vision, this study optimized the design of a linear feeder exposed to an environment with a specific operating frequency continuously to transfer parts at a constant supply speed. Resonance can occur when the natural frequency and the operating frequency of the linear feeder are within a similar range. It can negatively affect stable supply and the process of finding good or defective products during subsequent vision tests. In this study, vibration characteristics of the linear feeder were analyzed using mode analysis, frequency response analysis, and finite element analysis. An optimal design plan was derived based on this. After evaluating effects on vibration characteristics for structures in which vibrations or periodic loads such as mass and rails were continuously applied, the shape of the optimal linear feeder was presented using RSM.

Induction heating is a technology that uses heat generated by resistance when a high-frequency current is applied to a coil. An electric range using this is called an Induction Heating (IH) electric range. IH electric ranges are being widely applied in commercial products recently because they have higher thermal efficiency performances than other methods. The performance of a heating coil of an IH electric range greatly varies depending on the shape and number of coils. Thus, research on optimal coil shape and number according to product shape is required. Therefore, this study aimed to design an optimal heating coil at the set temperature of an electric range product. Target temperature was set to the temperature that a commercial stainless-steel container could withstand. The thickness of the coil copper wire, the number of windings, the applied voltage, and the frequency were set as design variables. A sensitivity analysis was performed to check the influence of each design variable on coil temperature. Based on this, optimal design was performed using the response surface method. Electromagnetic field-thermal analysis was performed with the designed coil and a very approximate result was obtained with a 0.07% error from the set target temperature.

Improving product quality is a crucial factor in determining the competitiveness and business efficiency of enterprises. This study investigates the influence of the cutting parameters, including the cutting speed, the depth of cut, and the feed rate on the surface roughness and the residual stress during the turning of AISI 304 austenitic stainless steel. Moreover, the work aims to determine optimal cutting parameters to satisfy both surface roughness and residual stress requirements. The mathematical model of the relationship between the machining parameters and the performance characteristics was formulated based on the response surface methodology (RSM) and the Box-Behnken design of the experiments. Pareto optimal solution applying natural-inspired algorithm (Bat Algorithm) is proposed to solve the bi-objective optimization problem to obtain the lowest surface roughness and minimal residual stress. The optimum cutting parameters selected by the manufacturing planners from the Pareto optimal fronts are calculated to comply with the production requirements.

The surface roughness and cutting forces are the important factors for the machine-part quality during the hard-turning process. The aim of this paper is to optimize hard-cutting conditions via implementation of response surface methodology (RSM). The experiments were conducted for the hard-turning process with the Box-Behnken design. The validation of the surface roughness and cutting forces was performed with the obtained 2nd order polynomial regression model. The results showed that the surface roughness was strongly dependent upon the RPM. The diminution of the cutting force was attributed to the low feed rate and the depth of cut. On the basis of the RSM, optimized cutting conditions of RPM, feed rate, and depth of cut are 3440, 0.0352 [mm/rev], and 0.03 [mm]. In this optimal cutting condition, the surface roughness can be around Ra= 0.202 μm.