Automated Extraction of Complexity Measures from Engineering Drawings

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s): not specified
• Date: 2024
• TU Wien Library:
• Open Access: yes
• First Supervisor: Eduard Gröller
• Pages: 117
• Keywords: engineering drawing, information extraction, computer vision, pattern recognition, deep learning, additive manufacturing, vision transformer

Abstract

An engineering drawing is a detailed representation of an object used to communicate complex information for the purposes of design, manufacturing, and maintenance.These line drawings typically consist of multiple 2D orthographic views of a 3D object, along with dimensioning information and metadata about specific properties.Over the past decades, engineering drawings have evolved from hand-drawn sketches to highly standardized documents created with the help of CAD software.The large variety of engineering drawings makes it difficult to automatically extract abstract information in a robust way.The emergence of additive manufacturing (AM) promises companies that they can produce spare parts on demand for maintenance, potentially increasing the operational time of their infrastructure.Evaluating the AM potential of spare parts is essential from both an economic and technical perspective.This analysis of economic and technical viability requires the interpretation of complexity measures that can be derived from the engineering drawing of a spare part.The external dimensions of an object are key complexity measures to facilitate an AM potential analysis.In this thesis, we propose a processing pipeline that automates the extraction of complexity measures from engineering drawings, focusing on the external dimensions of the depicted objects.An in-depth examination of engineering drawings from different eras forms the basis of our methodology.Our pipeline is designed to be adaptable and consists of interpretable stages for specific tasks.We segment important entities in the input drawing to detect candidate dimension lines that are subsequently filtered by a sequence of processing steps.The grid structure of the orthographic views is determined, which allows us to assign axis labels to each view.We run optical character recognition (OCR) on detected dimension numbers and use the results to optimize the ratio between the OCR values and the length of dimension lines in pixels, providing us with a solution that is resilient to errors in the OCR predictions.A prototypical implementation of our pipeline demonstrates its capabilities in handling a large variety of drawings.We conduct a basic quantitative and qualitative evaluation of our methodology.The results confirm the effectiveness of our approach in automatically extracting abstract information from real-world engineering drawings.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2024.115164

BibTeX

@mastersthesis{riegelnegg-2024-aeo,
  title =      "Automated Extraction of Complexity Measures from Engineering
               Drawings",
  author =     "Martin Riegelnegg",
  year =       "2024",
  abstract =   "An engineering drawing is a detailed representation of an
               object used to communicate complex information for the
               purposes of design, manufacturing, and maintenance.These
               line drawings typically consist of multiple 2D orthographic
               views of a 3D object, along with dimensioning information
               and metadata about specific properties.Over the past
               decades, engineering drawings have evolved from hand-drawn
               sketches to highly standardized documents created with the
               help of CAD software.The large variety of engineering
               drawings makes it difficult to automatically extract
               abstract information in a robust way.The emergence of
               additive manufacturing (AM) promises companies that they can
               produce spare parts on demand for maintenance, potentially
               increasing the operational time of their
               infrastructure.Evaluating the AM potential of spare parts is
               essential from both an economic and technical
               perspective.This analysis of economic and technical
               viability requires the interpretation of complexity measures
               that can be derived from the engineering drawing of a spare
               part.The external dimensions of an object are key complexity
               measures to facilitate an AM potential analysis.In this
               thesis, we propose a processing pipeline that automates the
               extraction of complexity measures from engineering drawings,
               focusing on the external dimensions of the depicted
               objects.An in-depth examination of engineering drawings from
               different eras forms the basis of our methodology.Our
               pipeline is designed to be adaptable and consists of
               interpretable stages for specific tasks.We segment important
               entities in the input drawing to detect candidate dimension
               lines that are subsequently filtered by a sequence of
               processing steps.The grid structure of the orthographic
               views is determined, which allows us to assign axis labels
               to each view.We run optical character recognition (OCR) on
               detected dimension numbers and use the results to optimize
               the ratio between the OCR values and the length of dimension
               lines in pixels, providing us with a solution that is
               resilient to errors in the OCR predictions.A prototypical
               implementation of our pipeline demonstrates its capabilities
               in handling a large variety of drawings.We conduct a basic
               quantitative and qualitative evaluation of our
               methodology.The results confirm the effectiveness of our
               approach in automatically extracting abstract information
               from real-world engineering drawings.",
  pages =      "117",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "engineering drawing, information extraction, computer
               vision, pattern recognition, deep learning, additive
               manufacturing, vision transformer",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/riegelnegg-2024-aeo/",
}