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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Energy-plus home dataset Parking lot dataset

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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