• Journal of the Korea Institute of Building Construction
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• v.24 no.4
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• pp.447-458
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• 2024

This study investigates the effectiveness of Building Information Modeling(BIM) software, specifically SketchUp and Revit, in reducing errors during quantity take-off(QTO) for complex building elements. While 3D modeling offers advantages, existing software may not fully account for manufacturing discrepancies, such as variations in concrete cover thickness and reinforcing bar radius. To address this limitation, this research proposes a BIM-based QTO method for high-insulation wall panels with intricate details. The method utilizes a BIM family library, focusing on key parameters like concrete cover thickness and inner radius of shear reinforcement. A case study compared the cross-sectional details of a wall panel modeled in Revit with the actual manufactured specimen. The analysis revealed a 12% reduction in modeled concrete cover thickness and a 1.27 times larger modeled inner radius of the shear bar compared to the real-world values. The proposed method incorporates these manufacturing variations into the Revit model of the high-insulation wall panel. Software like Navisworks facilitates the identification and correction of any material interferences arising from these adjustments. Furthermore, the method employs a unit wall concept(1m²) to account for the volume of various materials, including insulation and splice sleeves at joints. This allows for the identification of a similar existing family within the BIM library(e.g., "Double RC wall with embedded insulation") that reflects the actual material quantities used in the wall panel. By incorporating these manufacturing-induced variations, the proposed method offers a more accurate QTO process for complex high-insulation wall panels. The "Double RC wall with embedded insulation" family within the Revit program serves as a valuable tool for material quantity estimation in such scenarios.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.5
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• pp.314-323
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• 2013

Production simulation technology is beneficial to solve the complicated and fluctuated problems in a shipyard. It takes too much time and effort to build simulation models in the field, though. This research proposes a feasible method to reduce the difficulties related to simulation modeling for the factory or shop capacity analysis. In addition, a proposed neutral data format for production information is efficient to manage information acquisition for simulation modeling automation. A panel block shop model is contributed to comparison between the conventional technique and the automated one. The automation technique is highly recommended to run a rapid simulation in the shipyard problem.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.3 s.147
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• pp.392-398
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• 2006

A prototype of MES(Manufacturing Execution System) applied to panel assembly shop is implemented by using PDA(Personal Digital Assistant) and wireless database web server. The system is developed based on the Dot Net framework. The prototype can exchange the manufacturing execution data between production managers in the control room and workers in the factory through wireless internet communication. Manufacturing model of the panel shop is designed by using IDEF0 and UML method to understand the characteristics of the information and the data entities from the PPR-S view. Several issues in the shop were revealed from the manufacturing model analysis. The most typical problem was the lack of information sharing between the managing workers and the assembly workers. The problem prevents the workers and labors from sharing the process information and continuous workpiece flow In interactive way. To increase the information and data flow, a wireless internet based system is implemented and PDAs are linked together to exchange the process planning data and in-process data between the workers. It is anticipated that PDA and the implemented system can enable the process control at each process stages to obtain the well-organized operation.

• Journal of Welding and Joining
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• v.14 no.1
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• pp.30-37
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• 1996

조선에서 추진되어온 용접로봇의 적용은 주로 대조립 용접공정의 자동화로서, 갠트리(Gantry)에 용접토치를 장착한 로봇을 설치하여 구성하였다. 이러한 시스템에 서의 용접은 로봇을 용접부위까지 이송시킨 후 로봇의 구동으로 용접을 수행하거나, 또는 로봇과 캔트리의 동시 구동으로 용접을 수행하기도 한다. 또한 이 공정은 복잡 한 용접구조물을 OLP(Off-Line Programming)를 이용하여 교시하므로서 효과적인 자동 화 시스템의 구성이 가능할 수 있었다. 소조립 공정은 대조립공정과 비교하면 더 간단 한 부재의 용접이라 할 수 있으나 공정과 공장의 생산방법에 따라 자동화의 어려움은 따른다. 적용되는 매니프레이타는 소조립 공정의 특성에 맞게 그 형태가 설계되어야 하고 이를 운용하는 시스템은 소조립 생산방법에 맞게 통합, 개발되는 Task-Based System"이 되어야 한다. 특히 소조립 공정은 대조립 공정과 달리 여러가지 용접 판넬 을 동시에 이송시킨 후 용접함으로서 OLP의 직접 적용을 어렵게 하는 요인이 있어 이것을 해결하는 것이 생산성을 증가시키는데 적지 않은 영향을 미친다 하겠다. 이 글에서는 소조립 용접 자동화를 구성하기 위해 필요한 젓으로서 소조립 용접 공정을 소개하고, 공정의 특성에 맞도록 설계된 매니퓰레이타 시스템과, OLP, 판넬인식, 자동 교시 모들로 이루어지는 작업인식 시스템에 관해 기술한다.기술한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12A
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• pp.1897-1903
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• 2000

산업용 칼라 액정 패널 비젼 시스템은 공장 자동화 시스템 및 고속도로 등의 모니터링을 위해 필요한 시스템이다. 본 논문에서는 산업용 액정 패널 비젼에서 입력신호인 NTSC, SECAM, PAL 및 컴퓨터의 RGB 신호를 받아 이를 그래픽 처리하여 LCD 패널에 디스플레이 하여 대형 스크린에 투사하기 위한 LCD 드라이버 인터페이스를 카드를 개발하였다. 개발된 인터페이스 카드는 XGA(1024X768)급의 성능을 가진다. 카드의 성능을 테스트하기 위해 적합성 시험을 하였으며, 테스트 결과 만족할 만한 결과를 얻을 수 있었다.

• Journal of Intelligence and Information Systems
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• v.2 no.2
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• pp.29-42
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• 1996

본 논문은 조선업에서 평블럭을 생산하는 패널블럭조립공장의 일정계획 문제를 해결하기 위한 유전알고리즘의 적용 방안을 제시하고 있다. 패널불럭조립공장의 일정계획은 작업장별 평준화와 각 작업장내에서의 일자별 부하 평준화라는 두가지 목표를 가지고 있다. 이러한 목표를 달성하기 위해 본 논문에서는 유전알고리즘을 계층적으로 나누어 적용하였다. 상위단계 유전알고리즘은 작업장별 부하 평준화를 담당하며 하위단계 유전알고리즘은 상위단계 유전알고리즘의 결과를 바탕으로 각 작업장내에서 일자별 부하 평준화의 최적화를 담당한다. 실험 결과, 유전알고리즘에 의한 일정계획이 수작업에 의한 방법보다 처리시간이 짧게 소요되고 부하 평준화의 측면에서 더 좋은 해를 얻을 수 있음을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.33-40
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• 2022

In this paper, we propose a system that analyzes drone photographic images of panel-type factory roofs and conducts abnormal detection of bolts. Currently, inspectors directly climb onto the roof to carry out the inspection. However, safety accidents caused by working conditions at high places are continuously occurring, and new alternatives are needed. In response, the results of drone photography, which has recently emerged as an alternative to the dangerous environment inspection plan, will be easily inspected by finding the location of abnormal bolts using deep learning. The system proposed in this study proceeds with scanning the captured drone image using a sample image for the situation where the bolt cap is released. Furthermore, the scanned position is discriminated by using AI, and the presence/absence of the bolt abnormality is accurately discriminated. The AI used in this study showed 99% accuracy in test results based on VGGNet.