In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.

The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.