In this study, we have developed a movable defect detection system based on a vision module with machine-learning algorithm for distinguishing product quality. Machine-learning model determined the results in good or no good through images acquired from the vision module consisting of a camera, processor unit, and lighting. To ensure versatility for use in a variety of settings, we have integrated a robot arm and cart for the movable defect detection system, and the robot arm that adjusts the focus length is made to be able to rotate in all directions. The type of defect was divided into eccentricity defect and printing defect. As a result, it was confirmed that classification accuracy showed 0.9901 in our developed device.

We have demonstrated the feasibility of using electrospinning method to fabricate long and continuous composite nanofiber sheets of polyacrylonitrile (PAN) incorporated with zinc oxide (ZnO). Such PAN/ZnO composite nanofiber sheets represent an important step toward utilizing carbon nanofibers (CNFs) as materials to achieve remarkably enhanced physico-chemical properties. In an attempt to derive these advantages, we have used a variety of techniques such as field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM) and high resolution X-ray diffraction (HR-XRD) to obtain quantitative data on the materials. The CNFs produced are in the diameter range of 100 to 350 nm after carbonization at 1000℃. Electrical conductivity of the random CNFs was increased by increasing the concentration of ZnO. A dramatic improvement in porosity and specific surface area of the CNFs was a clear evidence of the novelty of the method used. This study indicated that the optimal ZnO concentration of 3 wt% is enough to produce CNFs having enhanced electrical and physico-chemical properties.