• 산업융합연구
• /
• 제21권1호
• /
• pp.103-110
• /
• 2023

본 연구는 우리나라 중소기업의 인더스트리 4.0의 성숙도, 클러스터링의 지표 요인, 주요 요인이 기업의 자체 평가에 미치는 영향을 알아보기 위해 클러스터링 분석을 수행하였다. 80개의 국내 중소기업을 4가지 범주로 분류하면 프로세스, 기술 및 조직 간에 유의미한 양의 상관관계가 있음을 발견하였다. 또한, 성숙도 모델에 따라 테스트된 80개 기업의 대다수는 아직 미성숙하거나 부분적으로 성숙한 것으로 보이며 인더스트리 4.0과 관련된 혁신 전략의 많은 개선과 재평가가 필요한 것으로 나타났다. 마지막으로, 싱가포르 스마트 산업 준비 지수는 국내 중소기업에서 자체 평가를 수행하는 데 적합하다는 결론을 도출하였다. 이러한 결론은 실무자와 연구자에게 유용한 성숙도 및 그룹화 지침으로 제공될 수 있다.

• Journal of Information Technology Applications and Management
• /
• 제27권1호
• /
• pp.75-95
• /
• 2020

Smart Factory is the decisive factor of the Fourth Industrial Revolution and is a key field for national competitiveness. Until now, most smart factory research has focused on policy and technology. In order to spread more technology, it is necessary to study what factors influence the adoption of smart factory technology in the enterprise. Nevertheless, little research has been done. In this study, based on the UTAUT (Unified Theory of Acceptance and Use of Technology), which has been proved through many years of research, I have studied the factors that influence the acceptance of smart factory technology. As a result of research, performance expectancy, social influence, and facilitating conditions of UTAUT model had a positive(+) effect on behavior intention. Their relationship of influence was in the order of performance expectancy (β = .459)> facilitating conditions (β = .212)> social influence (β = .210). However, it was found that the effort expectancy did not affect the behavior intention, and the impact of the newly perceived risk on the behavior intention to use was not confirmed. The main reason is that the acceptance of smart factory technology is not a matter of personal interest but a matter of organizational choice. Trust, on the other hand, was found to be partially mediated between performance expectancy, facilitating conditions, social influence and behavior intention. For many years, many researchers have validated the UTAUT, which has been validated through various empirical studies. It is academically meaningful to begin the study of factors affecting the acceptance of smart factory technology in terms of the UTAUT. In practice, it is necessary to provide SME employees with more information related to the introduction of smart factories, to provide advanced services related to the establishment of smart factories, and to establish a standardized model for each industry.

• 한국인터넷방송통신학회논문지
• /
• 제21권1호
• /
• pp.131-139
• /
• 2021

스마트팩토리는 4차 산업혁명에서 가장 대두되는 분야라고 말할 수 있다. 스마트팩토리에 필요한 공정이나 알고리즘을 개발하는 데에는 필수적으로 스마트팩토리에서 나오는 데이터값들이 필요하지만 그러한 데이터를 얻기 위해서는 현실적으로 많은 어려움이 있다. 따라서 본 연구에서는 스마트팩토리에 관한 연구를 돕기 위해 스마트팩토리 속 여러 공정의 데이터들을 보다 현실적으로 시뮬레이션할 수 있는 데이터 생성기를 개발하였다. 추가로, 설정 프리셋과 직관적인 UI 구성 등, 데이터 생성자의 편의를 위한 기능들도 개발하였다. 이 데이터 생성기를 이용함으로써, 스마트팩토리 환경에 필요한 여러 시스템을 만들 때 현실적인 데이터를 손쉽고 간단하게 생성하여 스마트팩토리 환경을 시뮬레이션하는 데 많은 도움을 받을 수 있을 것이다.

• 한국정보시스템학회지:정보시스템연구
• /
• 제31권1호
• /
• pp.197-218
• /
• 2022

Purpose This study examines the roles of firm-level smart factory implementation in the relationship between organizational competence and organizational performance in the context of Korean small and medium Enterprises (SMEs). To achieve this goal, this study presents and empirically tests a research model with evaluation data conducted by industrial experts on how organizational competence can be exploited to positively influence organizational performance through smart factory implementation. Design/methodology/approach Organizational competence are based on the research construct developed by Odważny et al.(2018). Research constructs on smart factory are based on the measurement model developed by Korea Technology and Information Promotion Agency for Korea small and medium Enterprises (TIPA) (2020) and organizational performance are based on the performance construct developed by Kwon(2019). To complete the investigation, we collected 31 firm data conducted by industrial experts in Korea from Dec 2018 to Dec 2020. Most of firm was implemented officially by government budget granted for smart factory of Korea SMEs. To test our hypotheses, partial least squares (PLS) method was employed. Findings The findings indicate that organizational competence is antecedent to influence smart factory implementation, while smart factory implementation has significant relationship with organizational performance. This study provides a better understanding of the connection between organizational competence and organizational performance through smart factory implementation. So companies should focus on enhancing organizational competence and implementing smart factory to obtain sustainable competitiveness.

• 한국CDE학회논문집
• /
• 제19권2호
• /
• pp.138-147
• /
• 2014

In manufacturing companies, different types of production have been developed based on diverse production strategies and differentiated technologies. The production systems have become smart, factories are filled with unmanned manufacturing lines, and sustainable manufacturing technologies are under development. Nowadays, the digital manufacturing technology is being adopted and used in manufacturing industries. When this technology is applied, a lot of efforts, time and cost are required and training professionals in-house is limited. In this paper, we introduce e-FEED system (electronic based Front End Engineering and Design) that is the integrated design and analysis system for optimized manufacturing line development on virtual environment. This system provides the functions that can be designed easily using library and template based on standardized modules and analyzed automatically the logistic and capacity simulation by one-click and verified the result using visual reports. Also, we can review the factory layout using automatically created 3D virtual factory and increase the knowledge reuse by e-FEED system.

• 스마트미디어저널
• /
• 제9권3호
• /
• pp.31-40
• /
• 2020

현재 제조 분야 빅데이터 분석을 위하여 black-box 기반 기계 학습 알고리즘을 활용하고 있다. 해당 알고리즘은 높은 분석 정합성 가지는 장점이 있지만, 분석 결과에 대한 해석이 어렵다는 단점이 있다. 그러나 제조업에서는 분석 알고리즘은 제조 공정 원리 기반 해석을 통하여 결과의 근거 및 도출 타당성에 대한 검증이 중요하다. 이러한 기계 학습 알고리즘의 결과 설명력 한계를 극복하기 위하여 유전 프로그래밍을 활용한 제조 빅데이터 분석 방법을 제안한다. 본 알고리즘은 생물학적 진화유전 프로그래밍 알고리즘은 생물학적 진화를 모방한 진화 연산 (선택, 교배, 돌연변이) 반복하면서 최적의 해를 찾아간다. 그리고 해는 수학적 기호를 활용하여 변수 간의 관계로 나타나며, 가장 높은 설명력을 가지는 해가 최종적으로 선택된다. 이를 통하여 입력 및 출력 변수 관계 수식화를 통한 결과를 도출하므로 직관적인 제조 매카니즘에 대한 해석이 가능하며 또한 수식으로 나타낸 변수간의 관계 기반으로 기존 해석이 불가한 제조 원리 도출도 가능하다. 제안 기법은 대표적인 기계 학습 알고리즘과 성능을 비교 분석 결과 동등 또는 우수한 성능을 보였다. 향후 해당 기법을 통하여 다양한 제조 분야 활용 가능성을 검증하였다.

• 제어로봇시스템학회논문지
• /
• 제15권11호
• /
• pp.1122-1129
• /
• 2009

Recently taping robot with smart recognition function have been studied in the coil manufacturing field. Due to the difficulty of 3D surface processing from the complicated working environment, it is not easy to accomplish smart tape attachment motion with non-contact sensor. To solve these problems the applicable surface recognition algorithm and a flexible sensing device has been recommended. In this research, the fusion method between 1D displacement and 3D laser scanner is applied for robust tape attachment about cold rolled coil. With these sensors we develop a two-step exploration and the smart algorithm for the awareness of non-aligned coil's information. In the proposed robot system for tape attachment, the problem is reduced to coil's radius searching with laser displacement sensor at first, and then position and orientation detection with 3D laser scanner. To get the movement at the robot's base frame, the hand-eye compensation between robot's end effector and sensing device should be also carried out respectively. In this paper, we examine the auto-coordinate transformation method in the calibration step for the real environment usage. From the experimental results, it was shown that the taping motion of robot had a robust under the non-aligned cold rolled coil.

• 한국정밀공학회지
• /
• 제28권4호
• /
• pp.436-445
• /
• 2011

As automation systems become intelligent and autonomous for productibility, industrial networks (fieldbuses) and network-based devices are essential components of intelligent manufacturing systems. However, there are obstacles for the wide acceptance of the network-based devices such as smart sensor and network-based actuator. First, there exist numerous fieldbus protocols that a network-based device should be able to support. Second, the whole network-based device has to be replaced when only the sensor of the module fails. In order to overcome these obstacles, a smart sensor/actuator is implemented as two units; one responsible for network communication and the other for sensor/actuator operations using IEEE 1451.0 standard. This paper presents a structure of the 1451.0-based smart sensor with multiple connectivity function designed by CANopen.

• 한국염색가공학회지
• /
• 제23권1호
• /
• pp.43-50
• /
• 2011

Electrically conductive yarn coated with silver particles are widely used to make smart wear but recent studies on smart fabrics are focused on measuring method of electrical characteristics and improving technologies of its electric properties. Also durability of conductive yarn with environmental change was also important work to make smart fabric. We compared resistance changes of silver coated conductive yarns under various physical and chemical conditions such as repeated strain, heat exposure and pH for basic informations on smart wear manufacturing process. And we deduct that repeated strain among the physical conditions was most effective factors on yarn resistance change and the low resistance change was observed with increasing the number of filaments in identical yarn fineness.

• 산업경영시스템학회지
• /
• 제43권2호
• /
• pp.110-119
• /
• 2020

In order to provide priorities of the factors affecting the introduction of Smart Factory, This study reconstructed the factors and calculated the priorities through AHP (Analytic Hierarchy Process). The first layer of the hierarchy have 4 factors; productivity increase, brand image improve, marketing improve, cost reduction. The second layer of the hierarchy have 3 factors belong to the first layer, so the total number of second layer is 12. We divided the characteristics of enterprises into type of manager and age. The C.R. (consistency ratio) values of the respondents were found to be less than 0.1 and were judged to be a 'reasonable test'. As a result, the weights of the higher layer and the lower layer were obtained respectively, and then the weights of the higher layer and the weights of the lower layer were multiplied to obtain the total weights. Unlike previous studies that only surveyed factors that companies consider when introducing smart factory, (1) weighing and prioritizing factors were achieved. There are differences in priorities, (2) smart factory can be studied with the type of manager and firm age. When establishing policies, it is a practical implication (3) to assess its strategy not only for government officials but also for executives.