A computational analysis was performed to study the thermal characteristics within the injection molding process of polygon mirrors in LiDAR systems. Such polygon mirrors are significantly influenced by the geometric shape of the injection mold as well as temperature and operating conditions. The analysis included the temperature distribution, heat flux, and variations in heat transfer rate of the polygon mirror from initial conditions. From the beginning of the injection process, temperature of the polygon mirror changes rapidly, leading to conductive heat transfer to the mold. There are large variations in the mirror temperature change depending on local position, and surface heat flux are affected by internal cooling path. These results are expected to be used as thermal design data for various polygon mirror processes.

In this study, we report the microstructural evolution and shear strength of an Sn-Sb alloy, used for die attach process as a solder layer of backside metal (BSM). The Sb content in the binary system was less than 1 at%. A chip with the Sn-Sb BSM was attached to a Ag plated Cu lead frame. The microstructure evolution was investigated after die bonding at 330 °C, die bonding and isothermal heat treatment at 330 °C for 5 min and wire bonding at 260 °C, respectively. At the interface between the chip and lead frame, Ni3Sn4 and Ag3Sn intermetallic compounds (IMCs) layers and pure Sn regions were confirmed after die bonding. When the isothermal heat treatment is conducted, pure Sn regions disappear at the interface because the Sn is consumed to form Ni3Sn4 and Ag3Sn IMCs. After the wire bonding process, the interface is composed of Ni3Sn4, Ag3Sn and (Ag,Cu)3Sn IMCs. The Sn-Sb BSM had a high maximum shear strength of 78.2 MPa, which is higher than the required specification of 6.2 MPa. In addition, it showed good wetting flow.

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.

Stainless steel is used in many industrial fields due to its excellent properties such as workability, strength, ductility, and corrosion resistance, and various properties required in the manufacturing field depending on the constituent components. pump impellers used in seawater and underwater require high corrosion resistance and high rigidity to prevent corrosion and damage, so they are a representative part group to which Stainless materials are applied. Through the introduction of the CMT(Cold Metal Transfer) process, a manufacturing method through WAAM(Wire Arc Additive Manufacturing) technology, which has advantages of lower production cost and excellent fatigue strength compared to the existing casting method, is being proposed. Recently, prior research on the WAAM process has been conducted on various materials, but most of the research results published so far are focused on the DED(Direct Energy Deposition) process, and a good WAAM shape design study using austenitic stainless steel is lacking. in this study, using the CMT process, the relationship between the change in bead shape and process parameters was confirmed in the BoP(Bead on Plate) welding experiment using wire made of austenitic stainless steel STS-308.

The purpose of this study is to use the hybrid steam-solvent process, because it is created in the form of water, bitumen, and water/bitumen emulsion by hot steam, so effective separation is required. Methods for separating the emulsion include a chemical separation method by adding a chemical, a separation method using an electrostatic property, a separation method using a membrane, a separation method using a microwave, and the like. Among them, the most used method is the separation method using a chemical, and it is reported that the separation efficiency of the emulsion is the best. In this study, a method for efficiently separating bitumen emulsions using a chemical separation method adding an emulsifier was investigated. In particular, technological trends in oil sand oil treatment technology were analyzed based on patent analysis.

The Korean Air-Force aircraft maintenance depot paints the exterior of various aircraft, including high-tech fighters. Aircraft exterior painting is a maintenance process for long-term life management by preventing damage to the aircraft surface due to corrosion. The de-painting process is essential to ensure the quality of aircraft exterior paints. However, because the Korean Air-Force’s de-painting process is currently done with sanding or Plastic Media Blasting (PMB) method, it is exposed to harmful dust and harmful compounds and consumes a lot of manpower. This study compares the de-painting process currently applied by the ROK Air-Force and the more improved process of the US Air Force, and performs economic analysis for the introduction of advanced equipment. It aims to provide information that can determine the optimal time to introduce new facilities through Cost-Volume-Profit (CVP) analysis. As a result of the analysis, it was confirmed that the sanding method had the most economical efficiency up to 2 units per year, the PMB method from 3 to 21 units, and the laser method from 22 units or more. In addition, in a situation where the amount of de-painting work is expected to increase significantly due to the increase in fighters in future, BEP analysis was conducted on the expansion of the existing PMB method and the introduction of a new laser method. As a result of the analysis, it was confirmed that it is more economical to introduce the laser method when the amount of work exceeds the PMB work capacity(18 units per year). The paper would helpful to improve the productivity and quality of the Korean Air Force Aircraft maintenance depot through timely changes of facilities in the workplace in preparation for expansion.

In this study, acoustic and viscosity data are collected in real time during the ball milling process and analyzed for correlation. After fast Fourier transformation (FFT) of the acoustic data, changes in the signals are observed as a function of the milling time. To analyze this quantitatively, the frequency band is divided into 1 kHz ranges to obtain an integral value. The integrated values in the 2–3 kHz range of the frequency band decrease linearly, confirming that they have a high correlation with changes in viscosity. The experiment is repeated four times to ensure the reproducibility of the data. The results of this study show that it is possible to estimate changes in slurry properties, such as viscosity and particle size, during the ball milling process using an acoustic signal.

본 연구는 반도체 제품과 설비기술을 대상으로 제품-공정기술 공진화를 교차영향분석(cross impact analysis)을 통해 구체화하고, 공진화 관계를 기술 인텔리전스의 대표적 도구 중 하나인 기술레이더에 통합하는 방법을 제시한다. 교차영향분석을 통해 공정기술 발전과 제품특성 개선이 반복되는 공진화 경로와 축이 되는 세부기술들을 파악했다. 또한 공진화 관계를 기술레이더의 기술가치평가 프로세스에 반영 해 가치평가와 연구개발 포트폴리오의 신뢰성을 제고했다. 학술적 측면에서 기술간 공진화를 세부기술 단위에서 구체화했으며, 기술 공진화 이론과 기술 인텔리전스의 접점을 제시했다는 의미가 있다. 실무적 측면에서는 반도체 관통전극-하이브리드 패키지 제품과 주요 후공정 기술간 공진화 및 기술레이더 분석 실례를 제시하고, 이를 통해 기술간 공진화 관계를 기존 기술전략 및 기획도구에 반영 해 기업의 미래준비역량과 전략기획의 신뢰성을 제고하는 방법을 구체화했다는 점에서 가치가 있다.