Dynamic 3D Shape Modeling and Control for 3D and 4D Printing Processes
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Biehler, Michael
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Abstract
Author: Michael Biehler
Advisor: Dr. Jianjun Shi
Length of PhD Thesis: 150 pages
Abstract: The world around us is comprised of dynamically evolving 3D shapes. For
instance, think of 3D printing, where products are manufactured one layer at a time. The
shape of each part depends on the previous layer’s 3D shape and additional operations in
the current layer. Or think of landslides on mountains, which evolve based on the 3D
topography and changing weather conditions. Advances in sensing technologies have made
contactless scanning of these 3D shapes possible through acquisition devices such as laser
and LiDAR scanners, resulting in unstructured 3D point clouds containing millions of data
points. However, modeling the spatio-temporal 3D evolution of such phenomena, whether
in 3D or 4D printing processes, poses significant challenges due to the large volume,
permutation invariance, and unstructured nature of these 3D point clouds.
To tackle these challenges, this doctoral thesis presents a series of methodologies for
process modeling, control, and optimization, all grounded in the analysis of dynamically
evolving 3D point clouds and heterogeneous inputs. The proposed methods have been
implemented and validated in real-world systems. Specifically, this thesis explores three key
topics to address the aforementioned challenges:
(1) Nonlinear Dynamic Evolution Modeling of Time-Dependent 3D Point Cloud Profiles:
Modeling the evolution of a 3D profile over time as a function of heterogeneous data and
previous time steps’ 3D shapes presents a challenging yet fundamental problem in many
applications. To address this, a novel methodology for the nonlinear modeling of
dynamically evolving 3D shape profiles has been developed. This model integrates
heterogeneous, multimodal inputs that influence the evolution of 3D shape profiles. Both
forward and backward temporal dynamics are utilized to preserve the underlying physical
structures over time. The approach leverages the theoretical Koopman framework to create
a deep learning-based model for nonlinear, dynamic 3D modeling with consistent temporal
dynamics.
(2) Real-Time Control of Time-Dependent 3D Point Cloud Profiles: In modern manufacturing
processes, ensuring the precision of 3D profiles is critical. However, achieving this accuracy
is challenging due to the complex interactions between process inputs and the data
structure of 3D shape profiles. To overcome this, a control framework for 3D profiles has
been developed, which actively adapts and controls the manufacturing process to improve
the accuracy of 3D shapes. Since 3D profile scans serve as the ultimate measure of part
quality, using them as system feedback for control purposes provides the most direct and
eAective approach. The eAectiveness of this framework is demonstrated in a case study on
wire arc additive manufacturing.
(3) Analysis and Optimization of Process Parameters in 4D Printing for Dynamic 3D Shape
Morphing Accuracy: Additive manufacturing (AM), commonly known as 3D printing, has
made significant advancements, particularly in the area of stimuli-responsive, 3D-printable,
and programmable materials. This progress has given rise to 4D printing, a fabrication
technique that combines AM with intelligent materials, adding dynamic functionality as the
fourth dimension. Among these materials, shape memory polymers have gained
prominence, especially for critical applications in stress-absorbing components. However,
the accuracy of 3D shape morphing in 4D printed products is influenced by both the 3D
printing conditions and the stimuli activation, making precise control challenging. To model
and optimize the dynamic 3D evolution of 4D printed parts, a novel machine-learning
approach that extends the concept of normalizing flows has been developed. This method
not only optimizes the dynamic 3D profile evolution by refining the process conditions during
both 3D printing and stimuli activation but also provides interpretability of the intermediate
shape morphing process.
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Date
2024-11-14
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Text
Resource Subtype
Dissertation