Title: Dynamic 3D Shape Modeling and Control for 3D and 4D Printing Processes
Date: Monday, November 11th , 2024
Time: 3:00 PM – 5:00 PM ET
Location: https://gatech.zoom.us/j/94542927960
Michael Biehler
Industrial Engineering PhD Candidate
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Committee:
Dr. Jianjun Shi (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Jing Li, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Kamran Paynabar, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Yu Ding, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Jionghua Jin, Department of Industrial and Operations Engineering, University of Michigan Ann Arbor
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 effective approach. The effectiveness 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.