Korea University · 工学
Professor Duhwan Mun's research lab specializes in digital transformation and intelligent design automation within the engineering and manufacturing domains. The lab focuses on advancing automated conversion of engineering drawings—particularly image-format piping and instrumentation diagrams (P&IDs)—into structured digital formats using deep learning and computer vision. Key research directions include 3D CAD model reconstruction from 2D data, topology-aware recognition of engineering symbols and lines, and seamless parametric CAD model exchange across heterogeneous design systems. The lab also addresses challenges in collaborative product development, such as consistent engineering change management and persistent feature referencing in distributed design environments.
Figures are computed from collected data and may differ slightly.
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
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