제올라이트, 특히 ZSM-5는 독특한 구조와 분자 체 특성으로 인해 산업적으로 매우 유용하며, 우수한 가스 분리 및 투과 증발 성능으로 높은 평가를 받고 있다. 그러나 ZSM-5 막의 제조 공정을 일관되게 재현하는 것은 여전히 도전 과제 로 남아 있다. 본 연구는 수열합성 조건(합성 시간: 24~72 h, 온도: 180~220°C)을 제어하고, 다양한 알루미나 지지체 비교하 며, 수열 처리 시 유기 구조유도체의 영향 분석을 통해 ZSM-5 막 제조의 신뢰성 향상을 목표로 하였다. 연구 결과, 합성 온 도 및 시간의 변화는 막 두께나 결정 크기에 큰 영향을 미치지 않았으나, 180°C에서 48 h 합성 조건에서 가장 우수한 가스 투과 성능이 나타났다. 다양한 알루미나 지지체 중에서는 N5 α-알루미나 모세관 지지체가 가장 높은 투과도를 나타내었다. 또한, 유기 구조유도체인 테트라프로필 암모늄 브로마이드(tetrapropylammonium bromide, TPABr)의 존재는 합성의 신뢰성에 상당한 영향을 미치는 것으로 확인되었다. 가스 투과 성능 평가 결과, 본 ZSM-5 막은 SF₆에 비해 N2 및 CO2에 대해 선택적 인 투과 특성을 보였으며, TPABr을 사용하여 합성한 막은 CO2/N2 선택도(α)가 약 4.6으로 나타났다.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

Small and medium-sized manufacturing enterprises(SMEs) have traditionally relied on skilled labor to support multi-variety, small-batch production. However, demographic changes such as low birth rates and aging populations have led to severe labor shortages, prompting increased interest in collaborative robots(cobots) as a viable alternative. Despite this necessity, many SMEs continue to face significant challenges in implementing such technologies due to technical, organizational, and environmental(TOE) constraints. While prior research has mainly focused on technology adoption from the perspective of user organizations, this study adopts a differentiated approach by analyzing adoption factors from the perspective of smart factory experts—specifically, evaluators/mentors and solution providers—who play a critical role in Korea’s policy-driven smart manufacturing environment. Using the Analytic Hierarchy Process(AHP), the study evaluates the relative importance and prioritization of adoption factors across three dimensions: technology, organization, and environment. Survey data collected from 20 smart factory experts indicate that top management support, relative advantage, and safety are key determinants in cobot adoption. Furthermore, the findings reveal that organizational readiness and technical effectiveness have greater influence on implementation decisions than external pressures such as partner pressure. This study provides new insights by incorporating expert perspectives into the adoption framework and offers practical policy and managerial implications to support cobots implementation in the SMEs.