고려대학교 · 공학
Duhwan Mun 교수의 연구실은 공정설비 및 제조업계에서 핵심적인 설계도면인 P&ID(배관 및 인스트루멘테이션 다이어그램)의 디지털 전환을 주요 연구 주제로 삼고 있습니다. 이미지 형식의 P&ID를 자동으로 인식하고 디지털 P&ID로 변환하는 데 기반한 딥러닝 기반의 객체 인식, 토폴로지 재구성, 3D CAD 모델 복원 기술을 개발하고 있습니다. 특히, 다양한 CAD 시스템 간의 데이터 교환과 공유를 위한 파라미터 기반 모델 변환 및 지속적 이름 문제 해결 기술에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
A piping and instrumentation diagram (P&ID) is a key drawing widely used in the energy industry. In a digital P&ID, all included objects are classified and made amenable to computerized data management. However, despite being widespread, a large number of P&IDs in the image format still in use throughout the process (plant design, procurement, construction, and commissioning) are hampered by difficulties associated with contractual relationships and software systems. In this study, w
ABSTRACT Three-dimensional (3D) computer-aided design (CAD) model reconstruction techniques are used for numerous purposes across various industries, including free-viewpoint video reconstruction, robotic mapping, tomographic reconstruction, 3D object recognition, and reverse engineering. With the development of deep learning techniques, researchers are investigating the reconstruction of 3D CAD models using learning-based methods. Therefore, we proposed a method to effectively reconstruct 3D CA
Abstract This study proposes an end-to-end digitization method for converting piping and instrumentation diagrams (P&IDs) in the image format to digital P&IDs. Automating this process is an important concern in the process plant industry because presently image P&IDs are manually converted into digital P&IDs. The proposed method comprises object recognition within the P&ID images, topology reconstruction of recognized objects, and digital P&ID generation. A data set compr
As modular production becomes increasingly widespread in globalized manufacturing industries, many components constituting a final product are being developed and produced by collaborating part suppliers who have the ability to design their own parts by themselves without aid from the original equipment manufacturer (OEM). In this collaborative product development, the important aspect to expedite engineering changes is that engineering change information should be represented precisely in a des
As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows
