In this study, we examined the assembly and components of a 40-feet container chassis based on its 3D shape. Utilizing the finite element method, we conducted structural analysis considering the total weight, including the 40-ton weight specified in automotive regulations, along with a safety margin of 1.5 under extreme load conditions. And also fixed and junction conditions were applied to the chassis system. Subsequently, we presented the maximum stress results derived from the structural analysis of both the overall chassis system and its individual components. Finally, we evaluated the structural stability of the 40-feet container chassis by comparing and reviewing the maximum stress with the yield strength of the material used for each component.

In the development of eco-friendly vehicles such as electric vehicles, weight reduction has become a very important design target. Seat weight reduction is very important in vehicle weight reduction. In this study, the energy absorption characteristics of Almag material, an alloy of aluminum and magnesium, and mild steel SAFH440, SAFH590, SAFC780, and SAFH980 were analyzed to obtain a true stress versus true strain curve that was correlated with the test. By performing the seat frame structure analysis using the obtained analysis material property, it was possible to compare the deformation between lightweight material, Almag and mild steel materials. In addition, it was confirmed that the weight reduction effect was 25.8% when applying Almag, an equivalent lightweight material that gives the same maximum deformation as SAFH980, a high-strength mild steel.

본 논문에서는 충격파 형태의 폭발 하중을 받는 부재의 소성 범위를 고려한 SDOF 해석의 수정계수를 개발하였다. SDOF 해석의 수 정계수는 MDOF 해석 결과 값을 비교하여 도출하였다. SDOF 해석에 영향을 미치는 매개변수로 부재의 경계조건, 폭발 하중 지속시 간과 고유주기 비를 선정하였다. 수정계수는 탄성 하중-질량 변환 계수를 기준으로 산정하였다. 수정계수 곡선은 상한, 하한 매개변수 경계 사이에 있도록 타원 방정식을 이용하여 도출하였다. 서로 다른 단면과 경계조건을 가지는 예제에 수정계수를 적용한 결과 SDOF 해석의 오차율이 15%에서 3%로 감소하였다. 본 연구의 결과는 수정계수를 적용하여 SDOF 해석의 정확도를 높임에 따라 폭발 해석 에 널리 활용될 수 있다.

Due to the complexity of urban area, the city vehicle routing problem has been a difficult problem. The problem has involved factors such as parking availability, road conditions, and traffic congestion, all of which increase transportation costs and delivery times. To resolve this problem, one effective solution can be the use of parcel lockers located near customer sites, where products are stored for customers to pick up. When a vehicle delivers products to a designated parcel locker, customers in the vicinity must pick up their products from that locker. Recently, identifying optimal locations for these parcel lockers has become an important research issue. This paper addresses the parcel locker location problem within the context of urban traffic congestion. By considering dynamic environmental factors, we propose a Markov decision process model to tackle the city vehicle routing problem. To ensure more real situations, we have used optimal paths for distances between two nodes. Numerical results demonstrate the viability of our model and solution strategy.

This paper presents a path planning optimization model for the engineering units to install obstacles in the shortest time during wartime. In a rapidly changing battlefield environment, engineering units operate various engineering obstacles to fix, bypass, and delay enemy maneuvers, and the success of the operation lies in efficiently planning the obstacle installation path in the shortest time. Existing studies have not reflected the existence of obstacle material storage that should be visited precedence before installing obstacles, and there is a problem that does not fit the reality of the operation in which the installation is continuously carried out on a regional basis. By presenting a Mixed Integrer Programming optimization model reflecting various constraints suitable for the battlefield environment, this study attempted to promote the efficient mission performance of the engineering unit during wartime.

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

The adhesive design of a fast steering mirror transmitting a high power laser is one of the important design elements that affect optical aberration of the mirror surface. In this paper, we designed the adhesive part to avoid the high power laser beam of the FSM system. Stiffness and wavefront error are trade-off relationships and an optical design was derived to maintain the wavefront error of the mirror surface at high temperatures while satisfying the bandwidth of the FSM system. For the optimal design of the mirror bonding position, structural analysis was conducted using ANSYS and wavefront error analysis was performed using Zernike polynomial code. Through those analysis, FSM most effective at an angle 60 degrees and a distance of 46mm.

유체-구조물-지반 상호작용을 고려한 액체저장탱크의 유한요소 모형을 제시하고, 비선형 지진응답 해석기법을 정식화한다. 탱크 구조물은 기하 및 재료 비선형 거동을 고려할 수 있는 쉘 요소로 모델링한다. 유체의 거동은 acoustic 요소로 구현하고, interface 요소 를 사용하여 구조물과 결합한다. 지반-구조물 상호작용을 고려하기 위해 지반의 근역과 원역을 각각 solid 요소와 perfectly matched discrete layer로 모델링한다. 예제 20만 kl급 액체저장탱크의 지진취약도 해석에 적용하여, 유연한 지반에 구조물이 놓인 경우 부지에 서의 암반노두운동의 증폭 및 필터링으로 인해 지진취약도의 중앙값과 대수 표준편차가 감소하는 것을 관찰할 수 있다.