towards building an ai-integrated computer- aided design ...molla hafizur rahman, zhenghui sha (pi)...
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Towards Building an AI-Integrated Computer-
Aided Design Platform for Design Research Molla Hafizur Rahman, Zhenghui Sha (PI)
Department of Mechanical Engineering, University of Arkansas, FayettevilleIMECE Track 16-1
NSF Research Poster CompetitionIMECE 2019-13335
SIDI LAB
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Project Objectives and Goals
• To develop a fine-grained data-driven research platform to support studies in engineering system design.
• To identify beneficial design patterns and aid designer grouping based on their sequential design behaviors.
• To computationally model and predict human sequential decisions using deep-learning methods.
• The overall goal of this project is to build an AI-integrated computer-aided design (CAD) tool for data-driven design research.
Future Work
• To develop a bi-level framework using embedding
technique to predict human design actions based on
the current model which predicts design processes.
• To integrate system thinking factors into the
predictive models by extracting human psychological
factors and cognitive skill.
• To implement reinforcement learning, such as
Markov Decision Process (MDP), to further advance
the artificial intelligence in our platform.
Conclusions and Contributions
References
[1] P. Berkhin, "A survey of clustering data mining techniques,"
in Grouping multidimensional data: Springer, 2006, pp. 25-71.
[2] Gero, J. S., 1990. “Design prototypes: a knowledge
representation schema for design”.AI magazine,11(4), p. 26.
[3] Goodfellow, I., Y. Bengio, and A. Courville, Deep learning.
2016: MIT press
Publications
Experimental Setup
• We developed an open-source research platform
that facilitates fine-grained data-driven design
thinking studies based on Energy3D.
• We developed an approach based on Markov chain
model and clustering algorithms to identify design
patterns and designers of similar behaviors.
• We developed a deep-learning based approach that
achieves higher prediction accuracy than the
traditional sequential models, e.g., hidden Markov
model and Markov chain model in design field.
• We improved our deep-learning models by
integrating both human attributes and human design
actions which shows promising prediction.
• In summary, this project lays a stepping stone
towards building an AI-integrated computer-aided
design platform for design research.
Background
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60.88
63.38
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Energy-plus home dataset Parking lot dataset
…..
Function-Behavior-Structure Based Design Process Model
Add wall Add wall Edit window Remove window Add roof Energy annual analysis
Formulation Formulation Synthesis Reformulation 1 ….. Formulation Analysis
(b) Recurrent neural network [6]
Acknowledgments
We gratefully acknowledge the financial support from the
National Science Foundation through grant # 1842588.
[1] Rahman, M.H., et al. Automatic Clustering of Sequential
Design Behaviors. in ASME 2018 International Design
Engineering Technical Conferences and Computers and
Information in Engineering Conference.
[2] Rahman, M.H., Schimpf, C., Xie, C. and Sha, Z., 2019. A
Computer-Aided Design Based Research Platform for Design
Thinking Studies. Journal of Mechanical Design, 141(12).
[3] Rahman M.H. et al. “A Deep learning-based Approach to
predict sequential design decision”, in ASME 2019
International Design Engineering Technical Conferences and
Computers and Information in Engineering Conference. (Robert E. Fulton Best Paper Award)
Figure 2: Computational models used in this study
• We transformed Energy3D, a solar system design software, to a
research platform for data-driven design thinking studies [1].
• Energy3D has various unique features for system design research.
Figure 1: Build an AI-Integrated Design Tool
• Function-Behavior-Structure (FBS) based design process model is
used to facilitate the abstraction and modeling of design thinking and
help reduce the dimensionality in modeling.
• Unsupervised cluster algorithms are used to identify designers of
similar sequential behavior [4].
• Deep-learning models, particularly the recurrent neural network and
its variant long short-term memory unit (LSTM) and gated recurrent
unit (GRU), are adopted to predict future design decisions.
Markov chain
Clustering methods
Final clusters
One-hot vector
Deep learning model
Predicted actions
K-means
Hierarchical
agglomerative
Network-based
LSTM
RNN
GRU
Data and Results
Improvised deep learning models for better prediction accuracy
Direct model Indirect model
We conducted two design experiments: Energy-Plus Home Design and Solarized Parking Lot Design
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MM LSTM HMM FFN RANDOM REPMODEL
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Training accuracy Testing accuracy
Figure 4 : a) Comparison between deep-learning modes and traditionally used sequential models [3]
b) Comparison between improved deep-learning models and baseline models
(a) (b)
2 design cases
~100 students
~500 design actions
4-fold cross
validation
Fine-grained Design Sequence
Synthesis Synthesis
Input design sequence
Predicted design sequence
(a) Function-Behavior-Structure model [5]
Figure 3: Clusters of designers of similar sequential behavior [2]
Energy3D