Rule-based continuous line classification using shape and positional relationships between objects in piping and instrumentation diagram

Han, Seung-Tae; Moon, Yoochan; Lee, Hyunsik; Mun, Duhwan

doi:10.1016/j.eswa.2024.123366

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.123366

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Rule-based continuous line classification using shape and positional relationships between objects in piping and instrumentation diagram

Seung-Tae Han,

Yoochan Moon,

Hyunsik Lee

et al.

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Symbol Detection in Mechanical Engineering Sketches: Experimental Study on Principle Sketches with Synthetic Data Generation and Deep Learning

Bickel,

Goetz,

Wartzack

2024

Applied Sciences

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Digital transformation is omnipresent in our daily lives and its impact is noticeable through new technologies, like smart devices, AI-Chatbots or the changing work environment. This digitalization also takes place in product development, with the integration of many technologies, such as Industry 4.0, digital twins or data-driven methods, to improve the quality of new products and to save time and costs during the development process. Therefore, the use of data-driven methods reusing existing data has great potential. However, data from product design are very diverse and strongly depend on the respective development phase. One of the first few product representations are sketches and drawings, which represent the product in a simplified and condensed way. But, to reuse the data, the existing sketches must be found with an automated approach, allowing the contained information to be utilized. One approach to solve this problem is presented in this paper, with the detection of principle sketches in the early phase of the development process. The aim is to recognize the symbols in these sketches automatically with object detection models. Therefore, existing approaches were analyzed and a new procedure developed, which uses synthetic training data generation. In the next step, a total of six different data generation types were analyzed and tested using six different one- and two-stage detection models. The entire procedure was then evaluated on two unknown test datasets, one focusing on different gearbox variants and a second dataset derived from CAD assemblies. In the last sections the findings are discussed and a procedure with high detection accuracy is determined.

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Symbol Detection in Mechanical Engineering Sketches: Experimental Study on Principle Sketches with Synthetic Data Generation and Deep Learning

Bickel,

Goetz,

Wartzack

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Rule-based continuous line classification using shape and positional relationships between objects in piping and instrumentation diagram

Cited by 1 publication

References 29 publications

Symbol Detection in Mechanical Engineering Sketches: Experimental Study on Principle Sketches with Synthetic Data Generation and Deep Learning

Symbol Detection in Mechanical Engineering Sketches: Experimental Study on Principle Sketches with Synthetic Data Generation and Deep Learning

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