Authors :
Amulya Tiwari; Tanishk Verma; Dr. A.K Madan
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3NkhT9d
DOI :
https://doi.org/10.5281/zenodo.6609306
Abstract :
Engineering expertise and the digitization of
all business operations are the important factors in
today's industrial firms' success. The engineer's design
process and expertise are highly personal, and rulebased procedure explanations are almost always
difficult and/or time-consuming. As a result, passing on
existing information to new engineers, particularly
training effort, is very challenging. Another issue is the
lack of an overview of the company's existing
components. Multiple designs and engineers spend their
valuable time as a result of this. The goal of this
approach is to use machine learning techniques to
extract information from current CAD models and
codify it. Furthermore, proper categorization and
similarity analysis should reveal existing components
rapidly.For this, an AI-based support system will be
developed. Engineers merely need to adjust the
parameters of the proposed components based on the
application. The assistant should then be able to
recommend an appropriate next design step based on
the existing CAD data and design history. As a result,
current CAD models' implicit empirical knowledge
supports production-friendly design and the avoidance
of design errors.
Engineering expertise and the digitization of
all business operations are the important factors in
today's industrial firms' success. The engineer's design
process and expertise are highly personal, and rulebased procedure explanations are almost always
difficult and/or time-consuming. As a result, passing on
existing information to new engineers, particularly
training effort, is very challenging. Another issue is the
lack of an overview of the company's existing
components. Multiple designs and engineers spend their
valuable time as a result of this. The goal of this
approach is to use machine learning techniques to
extract information from current CAD models and
codify it. Furthermore, proper categorization and
similarity analysis should reveal existing components
rapidly.For this, an AI-based support system will be
developed. Engineers merely need to adjust the
parameters of the proposed components based on the
application. The assistant should then be able to
recommend an appropriate next design step based on
the existing CAD data and design history. As a result,
current CAD models' implicit empirical knowledge
supports production-friendly design and the avoidance
of design errors.
