Dr Gokula Vasantha G.Vasantha@napier.ac.uk
Associate Professor
Dr Gokula Vasantha G.Vasantha@napier.ac.uk
Associate Professor
David Purves
John Quigley
Jonathan Corney
Andrew Sherlock
Geevin Randika
This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a `predictive CAD system' because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components for assemblies using 3D solid models. In contrast,
this research work focuses on feature-based predictive CAD system using B-rep models. This paper investigates the performance of predictive models that could enable the creation of such an intelligent CAD system by assessing three different methods to support inference: sequential, machine learning or probabilistic methods using N-Grams, Neural Networks (NN) and Bayesian Networks (BN) as representative of these methods.
After defining the functional properties that characterise a predictive design system a generic development methodology is presented. The methodology is used to carry out a systematic assessment of the relative performance of three methods each used to predict the diameter value of the next hole and boss feature type being added during the design of a hydraulic valve body.
Evaluating predictive performance; providing five recommendations (k=5) for hole or boss features as a new design was developed, recall@k increased from around 30% to 50% and precision@ k from around 50% to 70% as one to three features were added. The results indicate that the Bayesian and Neural Network models perform better than those using N-Grams. The practical impact of this contribution is assessed using a prototype (implemented as an extension to a commercial CAD system) by engineers whose comments defined an agenda for ongoing research in this area.
Vasantha, G., Purves, D., Quigley, J., Corney, J., Sherlock, A., & Randika, G. (2022). Assessment of Predictive Probability Models for Effective Mechanical Design Feature Reuse. Artificial intelligence for engineering design, analysis and manufacturing : AI EDAM, 36, Article e17. https://doi.org/10.1017/S0890060422000014
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 4, 2022 |
Online Publication Date | May 6, 2022 |
Publication Date | 2022 |
Deposit Date | Jan 5, 2022 |
Publicly Available Date | May 6, 2022 |
Print ISSN | 0890-0604 |
Electronic ISSN | 1469-1760 |
Publisher | Cambridge University Press |
Peer Reviewed | Peer Reviewed |
Volume | 36 |
Article Number | e17 |
DOI | https://doi.org/10.1017/S0890060422000014 |
Keywords | Data mining, feature recognition, design re-use, sequence modelling, predictive CAD |
Public URL | http://researchrepository.napier.ac.uk/Output/2689026 |
Assessment Of Predictive Probability Models For Effective Mechanical Design Feature Reuse
(893 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
A scheduling decision-making framework using machine learning algorithm for energy efficient integrated factory
(2025)
Presentation / Conference Contribution
Analysing Spatio-Temporal Worker Movement Patterns: Implications for Safety and Productivity in Smart Factories
(2025)
Presentation / Conference Contribution
Safer and Efficient Factory by Predicting Worker Trajectories using Spatio-Temporal Graph Attention Networks
(2024)
Presentation / Conference Contribution
Safer and Efficient Assemblies: Harnessing Real Time Worker Movements with Digital Twins
(2024)
Presentation / Conference Contribution
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search