skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Thursday, October 10 until 2:00 AM ET on Friday, October 11 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the NSF-PAR

Title: Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing
Many industries, such as human-centric product manufacturing, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes flexible design and manufacturing possible. However, the personalized designs bring challenges for the shape matching and analysis, owing to the high complexity and shape variations. Traditional shape matching methods are limited to spatial alignment and finding a transformation matrix for two shapes, which cannot determine a vertex-to-vertex or feature-to-feature correlation between the two shapes. Hence, such a method cannot measure the deformation of the shape and interested features directly. To measure the deformations widely seen in the mass customization paradigm and address the issues of alignment methods in shape matching, we identify the geometry matching of deformed shapes as a correspondence problem. The problem is challenging due to the huge solution space and nonlinear complexity, which is difficult for conventional optimization methods to solve. According to the observation that the well-established massive databases provide the correspondence results of the treated teeth models, a learning-based method is proposed for the shape correspondence problem. Specifically, a state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected deformed shapes. Through learning the deformations of the models, the underlying variations of the shapes are extracted and used for finding the vertex-to-vertex mapping among these shapes. We demonstrate the application of the proposed approach in the orthodontics industry, and the experimental results show that the proposed method can predict correspondence fast and accurate, also robust to extreme cases. Furthermore, the proposed method is favorably suitable for deformed shape analysis in mass customization enabled by 3D printing.  more » « less
Award ID(s):
2017968 1727190
NSF-PAR ID:
10185531
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of manufacturing science and engineering
Volume:
142
Issue:
6
ISSN:
1087-1357
Page Range / eLocation ID:
061003 (10 pages)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , IISE ANNUAL CONFERENCE & EXPO)
    null (Ed.)
    There is a growing importance in characterizing 3D shape quality in additive manufacturing (a.k.a. 3D printing). To accurately define the shape deviation between the designed product and actual build, shape registration of scanned point cloud data serves as a prerequisite for a reliable measurement. However, manual registration is currently heavily involved, for example, in obtaining initial matching of the design and the scanned product based on landmark features. The procedure can be inefficient, and more importantly, introduce potentially large operator-to-operator variations for complex geometries and deformation. Finding a sparse shape correspondence before refined registration would be meaningful to address this problem. In that case, automatic landmark selection has been a challenging issue, particularly for complicate geometric shapes like teeth. In this work we present an automatic landmark selection method for complicated 3D shapes. By incorporating subject matter knowledge (e.g., dental biometric information), a 3D shape will be first segmented through a new density-based clustering method. The geodesic distance is proposed as the distance metric in the revised clustering procedure. Geometrically informative features in each segment are automatically selected through the principal component analysis and Hotelling's T2 statistic. The proposed method is demonstrated in dental 3D printing application and could serve as a basis of sparse shape correspondence. 
    more » « less
  2. ; ; ; ( , IEEE transactions on visualization and computer graphics)
    This paper presents a novel surface registration technique using the spectrum of the shapes, which can facilitate accurate localization and visualization of non-isometric deformations of the surfaces. In order to register two surfaces, we map both eigenvalues and eigenvectors of the Laplace-Beltrami of the shapes through optimizing an energy function. The function is defined by the integration of a smoothness term to align the eigenvalues and a distance term between the eigenvectors at feature points to align the eigenvectors. The feature points are generated using the static points of certain eigenvectors of the surfaces. By using both the eigenvalues and the eigenvectors on these feature points, the computational efficiency is improved considerably without losing the accuracy in comparison to the approaches that use the eigenvectors for all vertices. In our technique, the variation of the shape is expressed using a scale function defined at each vertex. Consequently, the total energy function to align the two given surfaces can be defined using the linear interpolation of the scale function derivatives. Through the optimization of the energy function, the scale function can be solved and the alignment is achieved. After the alignment, the eigenvectors can be employed to calculate the point-to-point correspondence of the surfaces. Therefore, the proposed method can accurately define the displacement of the vertices. We evaluate our method by conducting experiments on synthetic and real data using hippocampus, heart, and hand models. We also compare our method with non-rigid Iterative Closest Point (ICP) and a similar spectrum-based methods. These experiments demonstrate the advantages and accuracy of our method 
    more » « less
  3. ; ; ; ; ; ( , Journal of Intelligent Manufacturing)
    Given a part design, the task of manufacturing process selection chooses an appropriate manufacturing process to fabricate it. Prior research has traditionally determined manufacturing processes through direct classification. However, an alternative approach to select a manufacturing process for a new design involves identifying previously produced parts with comparable shapes and materials and learning from them. Finding similar designs from a large dataset of previously manufactured parts is a challenging problem. To solve this problem, researchers have proposed different spatial and spectral shape descriptors to extract shape features including the D2 distribution, spherical harmonics (SH), and the Fast Fourier Transform (FFT), as well as the application of different machine learning methods on various representations of 3D part models like multi-view images, voxel, triangle mesh, and point cloud. However, there has not been a comprehensive analysis of these different shape descriptors, especially for part similarity search aimed at manufacturing process selection. To remedy this gap, this paper presents an in-depth comparative study of these shape descriptors for part similarity search. While we acknowledge the importance of factors like part size, tolerance, and cost in manufacturing process selection, this paper focuses on part shape and material properties only. Our findings show that SH performs the best among non-machine learning methods for manufacturing process selection, yielding 97.96% testing accuracy using the proposed quantitative evaluation metric. For machine learning methods, deep learning on multi-view image representations is best, yielding 99.85% testing accuracy when rotational invariance is not a primary concern. Deep learning on point cloud representations excels, yielding 99.44% testing accuracy when considering rotational invariance. 
    more » « less
  4. ; ; ; ; ; ( , Journal of Materials Chemistry C)
    Personalized healthcare (PHC) is a booming sector in the health science domain wherein researchers from diverse technical backgrounds are focusing on the need for remote human health monitoring. PHC employs wearable electronics, viz. group of sensors integrated on a flexible substrate, embedded in the clothes, or attached to the body via adhesive. PHC wearable flexible electronics (FE) offer numerous advantages including being versatile, comfortable, lightweight, flexible, and body conformable. However, finding the appropriate mass manufacturing technologies for these PHC devices is still a challenge. It needs an understanding of the physics, performance, and applications of printing technologies for PHC wearables, ink preparation, and bio-compatible device fabrication. Moreover, the detailed study of the operating principle, ink, and substrate materials of the printing technologies such as inkjet printing will help identify the opportunities and emerging challenges of applying them in manufacturing of PHC wearable devices. In this article, we attempt to bridge this gap by reviewing the printing technologies in the PHC domain, especially inkjet printing in depth. This article presents a brief review of the state-of-the-art wearable devices made by various printing methods and their applications in PHC. It focuses on the evaluation and application of these printing technologies for PHC wearable FE devices, along with advancements in ink preparation and bio-compatible device fabrication. The performance of inkjet, screen, gravure, and flexography printing, as well as the inks and substrates, are comparatively analyzed to aid PHC wearable sensor design, research, fabrication, and mass manufacturing. Moreover, it identifies the application of the emerging mass-customizable printing technologies, such as inkjet printing, in the manufacturing of PHC wearable devices, and reviews the printing principles, drop generation mechanisms, ink formulations, ink-substrate interactions, and matching strategies for printing wearable devices on stretchable substrates. Four surface matching strategies are extracted from literature for the guidance of inkjet printing of PHC stretchable electronics. The electro-mechanical performance of the PHC FE devices printed using four surface matching strategies is comparatively evaluated. Further, the article extends its review by describing the scalable integration of PHC devices and finally presents the future directions of research in printing technologies for PHC wearable devices. 
    more » « less
  5. ; ; ; ( , ACM Transactions on Graphics)

    Although shape correspondence is a central problem in geometry processing, most methods for this task apply only to two-dimensional surfaces. The neglected task ofvolumetriccorrespondence—a natural extension relevant to shapes extracted from simulation, medical imaging, and volume rendering—presents unique challenges that do not appear in the two-dimensional case. In this work, we propose a method for mapping between volumes represented as tetrahedral meshes. Our formulation minimizes a distortion energy designed to extract maps symmetrically, i.e., without dependence on the ordering of the source and target domains. We accompany our method with theoretical discussion describing the consequences of this symmetry assumption, leading us to select a symmetrized ARAP energy that favors isometric correspondences. Our final formulation optimizes for near-isometry while matching the boundary. We demonstrate our method on a diverse geometric dataset, producing low-distortion matchings that align closely to the boundary.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email