24 research outputs found
Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data
The product design process in manufacturing involves iterative design
modeling and analysis to achieve the target engineering performance, but such
an iterative process is time consuming and computationally expensive. Recently,
deep learning-based engineering performance prediction models have been
proposed to accelerate design optimization. However, they only guarantee
predictions on training data and may be inaccurate when applied to new domain
data. In particular, 3D design data have complex features, which means domains
with various distributions exist. Thus, the utilization of deep learning has
limitations due to the heavy data collection and training burdens. We propose a
bi-weighted unsupervised domain adaptation approach that considers the geometry
features and engineering performance of 3D design data. It is specialized for
deep learning-based engineering performance predictions. Domain-invariant
features can be extracted through an adversarial training strategy by using
hypothesis discrepancy, and a multi-output regression task can be performed
with the extracted features to predict the engineering performance. In
particular, we present a source instance weighting method suitable for 3D
design data to avoid negative transfers. The developed bi-weighting strategy
based on the geometry features and engineering performance of engineering
structures is incorporated into the training process. The proposed model is
tested on a wheel impact analysis problem to predict the magnitude of the
maximum von Mises stress and the corresponding location of 3D road wheels. This
mechanism can reduce the target risk for unlabeled target domains on the basis
of weighted multi-source domain knowledge and can efficiently replace
conventional finite element analysis
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
Studies on manufacturing cost prediction based on deep learning have begun in
recent years, but the cost prediction rationale cannot be explained because the
models are still used as a black box. This study aims to propose a
manufacturing cost prediction process for 3D computer-aided design (CAD) models
using explainable artificial intelligence. The proposed process can visualize
the machining features of the 3D CAD model that are influencing the increase in
manufacturing costs. The proposed process consists of (1) data collection and
pre-processing, (2) 3D deep learning architecture exploration, and (3)
visualization to explain the prediction results. The proposed deep learning
model shows high predictability of manufacturing cost for the computer
numerical control (CNC) machined parts. In particular, using 3D
gradient-weighted class activation mapping proves that the proposed model not
only can detect the CNC machining features but also can differentiate the
machining difficulty for the same feature. Using the proposed process, we can
provide a design guidance to engineering designers in reducing manufacturing
costs during the conceptual design phase. We can also provide real-time
quotations and redesign proposals to online manufacturing platform customers
Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97129/1/AIAA2012-5524.pd
Topology Optimization via Machine Learning and Deep Learning: A Review
Topology optimization (TO) is a method of deriving an optimal design that
satisfies a given load and boundary conditions within a design domain. This
method enables effective design without initial design, but has been limited in
use due to high computational costs. At the same time, machine learning (ML)
methodology including deep learning has made great progress in the 21st
century, and accordingly, many studies have been conducted to enable effective
and rapid optimization by applying ML to TO. Therefore, this study reviews and
analyzes previous research on ML-based TO (MLTO). Two different perspectives of
MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO
perspective addresses "why" to use ML for TO, while the ML perspective
addresses "how" to apply ML to TO. In addition, the limitations of current MLTO
research and future research directions are examined
Multidomain Demand Modeling in Design for Market Systems.
Consumers make choices based not only on functional product attributes (e.g., fuel economy) but also on non-functional attributes (e.g., vehicle form). Consequently, ignoring non-functional product attributes in demand modeling can lead to product designs less attractive to consumers. This dissertation focuses on two major non-functional product attributes: (i) aesthetic product form as a perceptual product attribute and (ii) services as external product attributes.
A limitation in conventional discrete choice analysis is that it handles functional and non-functional attributes within a single demand model. An aesthetic product form is generated by a potentially huge number of geometric variables; thus, it cannot be quantified simply and it is difficult to integrate with functional attributes. Similarly, when considering services, it is challenging to incorporate the relationship (or channel) between product and service attributes (or multiple providers) into a single demand model.
This dissertation proposes a multidomain demand modeling approach to integrate functional and non-functional attributes, whose values are decided by different design domains, into a single demand model. We employ consumer choice models from Marketing, systems design optimization from Engineering, machine learning algorithms and human-computer interaction from Computer Science, and location network models from Operations Research within a design optimization framework. This work addresses three demand models: (i) a demand model for engineering and industrial design, (ii) a demand model for engineering and service design, and (iii) a demand model for engineering and operations design. The benefits of this unified approach is demonstrated through three respective design applications including gasoline vehicle design, electric vehicle and charging station location design, and tablet and e-book service design.
The contribution of this research is in helping resolve trade-offs between conflicted design domain decisions, by integrating disparate attributes into a multidomain demand model. This work consequently extends the scope of Design for Market Systems from product design to business model design by considering external product attributes.PhDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110471/1/nwkang_1.pd
The Effect of Robo-taxi User Experience on User Acceptance: Field Test Data Analysis
With the advancement of self-driving technology, the commercialization of
Robo-taxi services is just a matter of time. However, there is some skepticism
regarding whether such taxi services will be successfully accepted by real
customers due to perceived safety-related concerns; therefore, studies focused
on user experience have become more crucial. Although many studies
statistically analyze user experience data obtained by surveying individuals'
perceptions of Robo-taxi or indirectly through simulators, there is a lack of
research that statistically analyzes data obtained directly from actual
Robo-taxi service experiences. Accordingly, based on the user experience data
obtained by implementing a Robo-taxi service in the downtown of Seoul and
Daejeon in South Korea, this study quantitatively analyzes the effect of user
experience on user acceptance through structural equation modeling and path
analysis. We also obtained balanced and highly valid insights by reanalyzing
meaningful causal relationships obtained through statistical models based on
in-depth interview results. Results revealed that the experience of the
traveling stage had the greatest effect on user acceptance, and the cutting
edge of the service and apprehension of technology were emotions that had a
great effect on user acceptance. Based on these findings, we suggest guidelines
for the design and marketing of future Robo-taxi services
Performance Comparison of Design Optimization and Deep Learning-based Inverse Design
Surrogate model-based optimization has been increasingly used in the field of
engineering design. It involves creating a surrogate model with objective
functions or constraints based on the data obtained from simulations or
real-world experiments, and then finding the optimal solution from the model
using numerical optimization methods. Recent advancements in deep
learning-based inverse design methods have made it possible to generate
real-time optimal solutions for engineering design problems, eliminating the
requirement for iterative optimization processes. Nevertheless, no
comprehensive study has yet closely examined the specific advantages and
disadvantages of this novel approach compared to the traditional design
optimization method. The objective of this paper is to compare the performance
of traditional design optimization methods with deep learning-based inverse
design methods by employing benchmark problems across various scenarios. Based
on the findings of this study, we provide guidelines that can be taken into
account for the future utilization of deep learning-based inverse design. It is
anticipated that these guidelines will enhance the practical applicability of
this approach to real engineering design problems
Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel
Engineering design research integrating artificial intelligence (AI) into
computer-aided design (CAD) and computer-aided engineering (CAE) is actively
being conducted. This study proposes a deep learning-based CAD/CAE framework in
the conceptual design phase that automatically generates 3D CAD designs and
evaluates their engineering performance. The proposed framework comprises seven
stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of
experiment in latent space, (4) CAD automation, (5) CAE automation, (6)
transfer learning, and (7) visualization and analysis. The proposed framework
is demonstrated through a road wheel design case study and indicates that AI
can be practically incorporated into an end-use product design project.
Engineers and industrial designers can jointly review a large number of
generated 3D CAD models by using this framework along with the engineering
performance results estimated by AI and find conceptual design candidates for
the subsequent detailed design stage
Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress
The impact performance of the wheel during wheel development must be ensured
through a wheel impact test for vehicle safety. However, manufacturing and
testing a real wheel take a significant amount of time and money because
developing an optimal wheel design requires numerous iterative processes of
modifying the wheel design and verifying the safety performance. Accordingly,
the actual wheel impact test has been replaced by computer simulations, such as
Finite Element Analysis (FEA), but it still requires high computational costs
for modeling and analysis. Moreover, FEA experts are needed. This study
presents an aluminum road wheel impact performance prediction model based on
deep learning that replaces the computationally expensive and time-consuming 3D
FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and
barrier mass value used for wheel impact test are utilized as the inputs to
predict the magnitude of maximum von Mises stress, corresponding location, and
the stress distribution of 2D disk-view. The wheel impact performance
prediction model can replace the impact test in the early wheel development
stage by predicting the impact performance in real time and can be used without
domain knowledge. The time required for the wheel development process can be
shortened through this mechanism