CADConverter-For Converting Complex CAD files into HDF5 Format
Date
2023-08-29
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this paper, we offer a very effective approach for extracting data from Computer-
Aided Design And Manufacturing (CAD/CAM) step files and converted them to the self-descriptive and open-source HDF5 format. This format provides for the seamless integration
of data and metadata inside a single file, making it ideal for dealing with complex data. The
difficulties associated with converting CAD files between applications and the sophisticated
data organisation inside multiple CAD file formats have motivated our research. The International standard ISO 10303 or STEP (’STandard for the Exchange of Product Model
data’) addresses the format complexity and CAD data conversion problem between different
computer-aided design (CAD) systems. We reviewed current CAD datasets like ABC
and Fusion360 for geometrical data processing and created an algorithm that converts CAD
models to open-source a human-readable format to feed deep learning algorithms directly
and hence eliminating the requirement for third-party software for file conversions will be
boosting the efficiency of CAD data administration and processing.
To do this, we employ a method that combines data pre-processing as well as the development of an algorithm that converts around one million CAD models to the HDF5 format and uses the derived data for a number of applications, including machine learning and data analysis. The findings of the study lead to a better understanding of CAD data processing procedures and establish the framework for future research and development and integrate the converted dataset with PyTorch and TensorFlow to address current CAD system constraints,such as the limitations of standard neutral 3D CAD file formats that are difficult to
understand. It opens the way for more efficient and simplified procedures in CAD-intensive
sectors.
Description
Keywords
Cad, CadConverter, CadConversion, Step files, HDF5 format, Open source cad files, Deep geometric learning, Cad data sampling, Data extraction