A revolutionary approach to automate labelling of CAD files

Abstract This innovation represents a significant advancement in enhancing processing efficiency when dealing with engineering drawings. Traditionally, the review of customer drawings needs to be executed by experienced experts before manufacture. The identification and labelling of key features were reliant on human intervention for subsequent Design for Manufacturability (DFM) or G-code programming. However, the previous method not only consumed valuable time but also introduced the potential for human errors. In this context, we introduce a high-efficiency technology for recognizing engineering drawings, automatically labelling key features, and converting unstructured data into structured data. It is noteworthy that this technology distinguishes itself from traditional methods, such as R-CNN or similar neural network approaches and differs from pre-labeled engineering drawings reliant on experts manually inputting keywords for dimensions or layers. This work is a vital step towards the realization of intelligent manufacturing. For instance, our customers can now review engineering drawings online and receive immediate feedback. This online interaction is facilitated by processing structured data, a direct output of this presented technology, with other specific modules. Furthermore, the structured data seamlessly integrates into subsequent manufacturing procedures, including notifying the supply chain, generating 3D drawings, and CNC programming. In conclusion, this technology showcases its significance in advancing our vision of intelligent manufacturing at ZY Corp.

Authors

Institutions

Publication Details

Journal
Journal of Physics Conference Series
Published
2024-10-01
DOI
https://doi.org/10.1088/1742-6596/2878/1/012008
Primary Topic
Digital Image Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A revolutionary approach to automate labelling of CAD files

Teresa C. Chen, Ja‐Kyeong Lee, Tien‐Hsien Chang
Journal of Physics Conference Series
Digital Image Processing Techniques
article

A revolutionary approach to automate labelling of CAD files

Teresa C. Chen, Ja‐Kyeong Lee, Tien‐Hsien Chang
article en

Abstract

Abstract This innovation represents a significant advancement in enhancing processing efficiency when dealing with engineering drawings. Traditionally, the review of customer drawings needs to be executed by experienced experts before manufacture. The identification and labelling of key features were reliant on human intervention for subsequent Design for Manufacturability (DFM) or G-code programming. However, the previous method not only consumed valuable time but also introduced the potential for human errors. In this context, we introduce a high-efficiency technology for recognizing engineering drawings, automatically labelling key features, and converting unstructured data into structured data. It is noteworthy that this technology distinguishes itself from traditional methods, such as R-CNN or similar neural network approaches and differs from pre-labeled engineering drawings reliant on experts manually inputting keywords for dimensions or layers. This work is a vital step towards the realization of intelligent manufacturing. For instance, our customers can now review engineering drawings online and receive immediate feedback. This online interaction is facilitated by processing structured data, a direct output of this presented technology, with other specific modules. Furthermore, the structured data seamlessly integrates into subsequent manufacturing procedures, including notifying the supply chain, generating 3D drawings, and CNC programming. In conclusion, this technology showcases its significance in advancing our vision of intelligent manufacturing at ZY Corp.

Journal of Physics Conference SeriesVol. 2878(1)
Taichung City Government (TW)
Industry, innovation and infrastructure
Openalex Percentile: Top 80%
Digital Image Processing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A revolutionary approach to automate labelling of CAD files — Teresa C. Chen, Ja‐Kyeong Lee, et al. · Journal of Physics Conference Series (2024) | TGRS Research Map | TGRS