With advancements in the 3D printing technology, many industrial sectors are transitioning from traditional production methods, such as cutting processing, and casting, to utilizing 3D printers for manufacturing. For instance, in the automotive industry, the production of vehicle upright knuckle parts typically involves casting followed by machining processes, such as turning and milling, to achieve dimensional accuracy. However, this approach is associated with high processing costs and longer lead times. This study focuses on the production of vehicle upright knuckle parts using a selective laser melting (SLM)-type 3D printer, with SUS 630 as the material. To evaluate the feasibility of utilizing this method in industrial vehicles, this study conducts static and modal analyses, along with topology optimization. Additionally, experimental test drives are performed with the parts installed in KSAE BAJA vehicles, and modal frequency experiments are conducted. The objective of these analyses and experiments is to assess the performance, reliability, and applicability of utilizing SLM-based 3D printing for manufacturing vehicle upright knuckle parts by optimizing the design through topology optimization and evaluating the results through experiments and analysis.

M.2 NVMe SSD (Non-Volatile Memory express Solid-State Drive), which have higher computational speed and reliability than conventional devices, have come to be widely used. Recent studies have reported that M.2 NVMe SSD are beginning to have thermal issues due to the increasing heat generation occurring with the high chip density and high-performance operation in a limited space. Thermal issues in the controller and memory units of M.2 NVMe SSD lead to increased failure rates and decreased data retention times. In this study, we propose a compact and optimized thermal solution for commercial M.2 NVMe SSD installed between the mainboard and GPU (Graphic Processing Unit). A thermal and fluid dynamics simulation of an M.2 NVMe SSD, including the heatsink, was performed, and the Genetic Algorithm method was used to optimize the heatsink size.

As the size of semiconductor devices gradually decreases, it is important to measure and analyze semiconductor devices, to improve the image quality of semiconductors. We use VDSR, one of the Super-Resolution methods to improve the quality of semiconductor devices’ SEM images. VDSR is also a convolutional neural network that can be optimized with various parameters. In this study, a VDSR model for semiconductor devices’ SEM images was optimized using parameters such as depth of layers and amount of training data. Meanwhile, the quantitative evaluation and the qualitative evaluation did not match at the low scale factor. To solve this problem, we proposed an MTF measurement method using the slanted edge for better quantitative evaluation. This method was verified by comparing the results with the PSNR and SSIM index results, which are known as quality indicators. Based on the results, it was confirmed that using the MTF value could be a better approach for the evaluation of SEM images of the semiconductor device than using PSNR and SSIM.