본 연구는 다시마 양식을 위한 통합 자동화 시스템을 개발하고, 이를 통해 생산성, 비용 효율성, 환경적 지속 가능성을 모두 개선하는 데 중점을 두고 있다. 기존의 노동 집약적 수확 방식과 넓은 공간을 필요로 하는 수평 건조 방식은 비효율적이며, 환경적 부작 용을 초래했다. 이에 본 연구는 자동화된 수확 시스템, 해상-육상 연계 운송 시스템, 그리고 수직 건조 시스템을 통합적으로 개발하여 양 식업의 생산성을 극대화하고 자원 사용을 최적화하였다. 자동화된 수확 시스템은 작업 속도를 약 35% 향상시켰으며, 작업의 일관성을 유지하여 품질 오차율을 2% 이하로 줄이는 성과를 보였다. 해상-육상 연계 운송 시스템은 모듈형 컨테이너를 활용하여 운송 중 손상률 을 기존 15%에서 5%로 감소시켰고, 운송 시간을 평균 6시간에서 4시간으로 단축하였다. 또한, 수직 건조시스템은 고밀도 적재와 자연 대류 방식을 도입하여 건조 시간을 기존 48시간에서 28시간으로 40% 단축하였으며, 에너지 소비를 25% 감소시켰다. 이러한 시스템은 데이터 기반으로 설계 및 검증되었으며, 통합적으로 양식업의 경제성 향상과 환경적 부담 감소를 동시에 실현하였다. 본 연구의 결과는 다른 해조류 양식에도 적용 가능하며 지속 가능한 해양 자원 관리에 기여할 것으로 기대된다.

현재 기후변화 문제 해결을 위한 국제적 목표 및 국내 에너지 정책에 부합하는 LNG 발전의 효율성을 극대화하는 방안이 요구 되고 있다. 2050년 탄소중립 목표 달성을 위해 각국은 2030년까지 온실가스 감축 목표를 설정하였고, 국내에서도 석탄 발전 시설을 단계 적으로 폐쇄하고 LNG 발전 시설을 확대하려는 노력이 진행되고 있다. 이 연구에서는 LNG의 재기화 과정에서 발생하는 냉열을 회수하여 Carbon Capture and Storage, Ammonia-water Rankine Cycle / Kalina Cycle, Data Center Cooling, Direct Expansion 공정에 활용할 수 있는 시스템을 제안하였다. 연구 결과 제안된 시스템의 3E 분석 결과 Energy 효율 51.52%, Exergy 효율 42.74%, 환경적 측면에서 2,145.8 gCO2 / kgLNG의 탄 소 배출량을 보여 가장 우수한 성능을 확인하였다. 이를통해 본 연구에서 새롭게 제시한 시스템은 Energy, Exergy, 환경성 측면에서 강점을 가지며, 향후 기후변화 대응에 크게 기여할 것으로 판단된다.

건물관리업은 산업현장의 생산설비와 시설로 부터 빌딩, 공동주택까지 영역을 확장하고 있다. 최근 5년간 건물관리업에 종사하는 근로자 수는 지속적으로 증가해 왔으며 해당 산업에 종사하는 재해자 수도 함께 증가하고 있다. 특히 건물관리업에서 발생하는 재해의 약 88.6%가 50인 미만 소규모 사업장에서 발생하며, 사고성 재해가 반복된다는 점에서 소규모 사업장에서 중점적으로 관리해야 하는 위험작업에 대한 정보를 제공하여 집중 관리하도록 할 필요성이 높다. 본 연구는 일의 형태가 다양한 건물관리업의 공정과 단위작 업을 표준화하고, 표준화된 작업의 우선 순위와 중요도를 AHP 기법을 통해 분석한 후 일의 중요도와 업무상 가중치를 제시하여 사업장에서 중점적으로 관리해야 하는 작업을 선별하는데 도움을 주고자 한다. 본 연구에서 제시하는 건물관리업의 표준화된 공정과 단위작업을 기반으로 향후 유해위험요인의 표준평가 모델과 표준가중치 모델을 개발하여 사업장에 적용한다면 건물관리사업의 산업재해 리스크를 체계적으로 관리하는데 도움이 될 것으로 기대한다.

본 논문에서는 3D 프린팅 공정을 통해 제작된 단섬유 강화 복합소재 구조물의 기계적 거동을 효과적으로 예측하기 위한 AM 공정 연계 구조 해석 기법을 제안하였다. 복합소재 3D 프린터(Mark Two, Markforged)를 활용하여 다양한 노즐 경로를 갖는 인장 시편을 출력하였으며, 출력물에 대한 인장 시험을 진행하였다. 또한, 노즐 경로에 따른 부위별 이방 물성을 도출하기 위해 실험적 데이터를 기반으로 역공학 기법을 적용하였다. 제안된 AM 공정 연계 구조 해석 방안의 타당성을 검증하기 위해 실험 결과와의 비교/분석을 병 행하였으며, 부위별 이방 물성이 반영된 FE 모델을 바탕으로 AM 공정 연계 구조 해석을 수행함으로써 복합소재 3D 프린팅 출력물의 거동 양상을 정확하게 예측할 수 있음을 확인하였다.

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.