Comparing Slicing Technologies for Digital Light Processing Printing

In additive manufacturing (AM), slicing is a crucial step in process planning to convert a computer-aided design (CAD) model to a machine-specific format. Digital light processing (DLP) printing is an important AM process that has a good surface finish, high accuracy, and fabrication speed and is widely applied in many dental and engineering industries. However, as DLP uses images for fabrication different from other toolpath-based processes, its process planning is understudied. Therefore, the main goal of this paper is to study and compare the slicing technologies for DLP printing. Three slicing technologies are compared: contour, voxelization, and ray-tracing

DNSS: Dual-Normal-Space Sampling for 3-D ICP Registration

Rigid registration is a fundamental process in many applications that require alignment of different datasets. Iterative closest point (ICP) is a widely used algorithm that iteratively finds point correspondences and updates the rigid transformation. One of the key variants of ICP to its success is the selection of points, which is directly related to the convergence and robustness of the ICP algorithm. Besides uniform sampling, there are a number of normal-based and feature-based approaches that consider normal, curvature, and/or other signals in the point selection. Among them, normal-space sampling (NSS) is one of the most popular techniques due to its simplicity and low computational cost. The rationale of NSS is to sample enough constraints to determine all the components of transformation, but this paper finds that NSS actually can constrain the translational normal space only. This paper extends the fundamental idea of NSS and proposes Dual NSS (DNSS) to sample points in both translational and rotational normal spaces. Compared with NSS, this approach has similar simplicity and efficiency without any need of additional information, but has a much better effectiveness. Experimental results show that DNSS can outperform the normal-based and feature-based methods in terms of convergence and robustness. For example, DNSS can achieve convergence from an orthogonal initial position while no other methods can achieve. Note to Practitioners-ICP is commonly used to align different data to a same coordination system. While NSS is often used to speed up the alignment process by down-sampling the data uniformly in the normal space. The implementation of NSS only has three steps: 1) construct a set of buckets in the normal-space; 2) put all points of the data into buckets based on their normal direction; and 3) uniformly pick points from all the buckets until the desired number of points is selected. The algorithm is simple and fast, so that it is still the common practice. However, the weakness of NSS comes from the reason that it cannot handle rotational uncertainties. In this paper, a new algorithm called DNSS is developed to constrain both translation and rotation at the same time by introducing a dual-normal space. With a new definition of the normal space, the algorithm complexity of DNSS is the same as that of NSS, and it can be readily implemented in all types of application that are currently using ICP. The experimental results show that DNSS has better efficiency, quality, and reliability than both normal-based and feature-based methods have

GDFE: Geometry-Driven Finite Element for Four-Dimensional Printing

Four-dimensional (4D) printing is a new category of printing that expands the fabrication process to include time as the forth dimension, and its process planning and simulation have to take time into consideration as well. The common tool to estimating the behavior of a deformable object is the finite element method (FEM). Although FEM is powerful, there are various sources of deformation from hardware, environment, and process, just to name a few, which are too complex to model by FEM. This paper introduces Geometry-Driven Finite Element (GDFE) as a solution to this problem. Based on the study on geometry changes, the deformation principles can be drawn to predict the relationship between the 4D-printing process and the shape transformation. Similar to FEM, the design domain is subdivided into a set of GDFEs, and the principles are applied on each GDFE, which are then assembled to a larger system that describes the overall shape. The proposed method converts the complex sources of deformation to a geometric optimization problem, which is intuitive and effective. The usages and applications of the GDFE framework have also been presented in this paper, including freeform design, reserve design, and design validation

Customization and topology optimization of compression casts/braces on two-manifold surfaces

This paper applies the topology optimization (TO) technique to the design of custom compression casts/braces on two-manifold mesh surfaces. Conventional braces or casts, usually made of plaster or fiberglass, have the drawbacks of being heavy and unventilated to wear. To reduce the weight and improve the performance of a custom brace, TO methods are adopted to optimize the geometry of the brace in the three-dimensional (3D) space, but they are computationally expensive. Based on our observation that the brace has a much smaller thickness compared to other dimensions and the applied loads are normal forces, this paper presents a novel TO method based on thin plate elements on the two-dimensional manifold (2-manifold) surfaces instead of 3D solid elements. Our working pipeline starts from a 3D scan of a human body represented by a 2-manifold mesh surface, which is the base design domain for the custom brace. Similar to the concept of isoparametric representation, the 3D design domain is mapped onto a two-dimensional (2D) parametric domain. An Finite Element Analysis (FEA) with bending moments is performed on the parameterized 2D design domain, and the Solid Isotropic Material with Penalization (SIMP) method is applied to optimize the pattern in the parametric domain. After the optimized cast/brace is obtained on the 2-manifold mesh surface, a solid model is generated by our design interface and then sent to a 3D printer for fabrication. Compared with the optimization method with solid elements, our method is more efficient and controllable due to the high efficiency of solving FEA in the 2D domain

V4PCS: Volumetric 4PCS Algorithm for Global Registration

With the advances in three-dimensional (3D) scanning and sensing technologies, massive human-related data are now available and create many applications in data-driven design. Similarity identification is one of basic problems in data-driven design and can facilitate many engineering applications and product paradigm such as quality control and mass customization. Therefore, reusing information can create unprecedented opportunities in advancing the theory, method, and practice of product design. To enable information reuse, different models have to be aligned so that their similarity can be identified. This alignment is commonly known as the global registration that finds an optimal rigid transformation to align two 3D shapes (scene and model) without any assumptions on their initial positions. The Super 4-Points Congruent Sets (S4PCS) is a popular algorithm used for this shape registration. While S4PCS performs the registration using a set of 4 coplanar points, we find that incorporating the volumetric information of the models can improve the robustness and the efficiency of the algorithm, which are particularly important for mass customization. In this paper, we propose a novel algorithm, Volumetric 4PCS (V4PCS), to extend the 4 coplanar points to non-coplanar ones for global registration, and theoretically demonstrate the computational complexity is significantly reduced. Experimental tests are conducted on a number of models such as tooth aligner and hearing aid to compare with S4PCS. The experimental results show that the proposed V4PCS can achieve a maximum of 20 times speedup and can successfully compute the valid transformation with very limited number of sample points. An application of the proposed method in mass customization is also investigated

An Interactive Product Customization Framework for Freeform Shapes

Additive Manufacturing (AM) enables the fabrication of three-dimensional (3D) objects with complex shapes without additional tools and refixturing. However, it is difficult for user to use traditional computer-aided design tools to design custom products. In this paper, we presented a design system to help user design custom 3D printable products on top of some freeform shapes. Users can define and edit styling curves on the reference model using our interactive geometric operations for styling curves. Incorporating with the reference models, these curves can be converted into 3D printable models through our fabrication interface. We tested our system with four design applications including a hollow-patterned bicycle helmet, a T-rex with skin frame structures, a face mask with Voronoi patterns, and an AM-specific night dress with hollow patterns. The executable prototype of the presented design framework used in the customization process is publicly available

Surfel convolutional neural network for support detection in additive manufacturing

Support generation is one of the crucial steps in 3D printing to make sure the overhang structures can be fabricated. The first step of support generation is to detect which regions need support structures. Normal-based methods can determine the support regions fast but find many unnecessary locations which could be potentially self-supported. Image-based methods conduct a layer-by-layer comparison to find support regions, which could make use of material self-support capability; however, it sacrifices the computational cost and may still fail in some applications due to the loss of topology information when conducting offset and boolean operations based on the image. In order to overcome the difficulties of image-based methods, this paper proposes a surfel convolutional neural network (SCNN)-based approach for support detection. In this method, the sampling point on the surface with normal information, named surfel (surface element), is defined through layered depth-normal image (LDNI) sampling method. A local surfel image which represents the local topology information of the sampling point in the solid model is then constructed. A set of models with ground-truth support regions is used to train the deep neural network. Experimental results show that the proposed method outperforms the normal-based method and image-based method in terms of accuracy, reliability, and computational cost

In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Liquid Metal Jet Printing (LMJP) is a revolutionary three-dimensional (3D) printing technique in fast but low-cost additive manufacturing. The driving force is produced by magneto-hydrodynamic property of liquid metal in an alternating magnetic field. Due to its integrated melting and ink-jetting process, it can achieve 10x faster speed at 1/10th of the cost as compared to current metal 3D printing techniques. However, the jetting process is influenced by many uncertain factors, which impose a significant challenge to its process stability and product quality. To address this challenge, we present a closed-loop control framework by seamlessly integrating vision-based technique and neural network tool to inspect droplet behaviours and accordingly stabilize the printing process. This system automatically tunes the drive voltage applied to compensate the uncertain influence based on vision inspection result. To realize this, we first extract multiple features and properties from images to capture the droplet behaviour. Second, we use a neural network together with PID control process to determine how the drive voltage should be adjusted. We test this system on a piezoelectric-based ink-jetting emulator, which has a very similar jetting mechanism to the LMJP. Results show that significantly more stable jetting behavior can be obtained in real-time. This system can also be applied to other droplet related applications owing to its universally applicable characteristics

Parametric design for human body modeling by wireframe-assisted deep learning

Statistical learning of human body shape can be used for reconstructing or estimating body shapes from incomplete data, semantic parametric design, modifying images and videos, or simulation. A digital human body is normally represented in a high-dimensional space, and the number of vertices in a mesh is far larger than the number of human bodies in public available databases, which results in a model learned by Principle Component Analysis (PCA) can hardly reflect the true variety in human body shapes. While deep learning has been most successful on data with an underlying Euclidean or grid-like structure, the geometric nature of human body is non-Euclidean, it will be very challenging to perform deep learning techniques directly on such non-Euclidean domain. This paper presents a deep neural network (DNN) based hierarchical method for statistical learning of human body by using feature wireframe as one of the layers to separate the whole problem into smaller and more solvable sub-problems. The feature wireframe is a collection of feature curves which are semantically defined on the mesh of human body, and it is consistent to all human bodies. A set of patches can then be generated by clustering the whole mesh surface to separated ones that interpolate the feature wireframe. Since the surface is separated into patches, PCA only needs to be conducted on each patch but not on the whole surface. The spatial relationships between the semantic parameter, the wireframe and the patches are learned by DNN and linear regression respectively. An application of semantic parametric design is used to demonstrate the capability of the method, where the semantic parameters are linked to the feature wireframe instead of the mesh directly. Under this hierarchy, the feature wireframe acts like an agent between semantic parameters and the mesh, and also contains semantic meaning by itself. The proposed method of learning human body statistically with the help of feature wireframe is scalable and has a better quality

4D Printing: Design and Fabrication of Smooth Curved Surface Using Controlled Self-Folding

Traditional origami structures fold along pre-defined hinges, and the neighboring facets of the hinges are folded to transform planar surfaces into three-dimensional (3D) shapes. In this study, we present a new self-folding design and fabrication approach that has no folding hinges and can build 3D structures with smooth curved surfaces. This four-dimensional (4D) printing method uses a thermal-response control mechanism, where a thermo shrink film is used as the active material and a photocurable material is used as the constraint material for the film. When the structure is heated, the two sides of the film will shrink differently due to the distribution of the constraint material on the film. Consequently, the structure will deform over time to a 3D surface that has no folding hinges. By properly designing the coated constraint patterns, the film can be self-folded into different shapes. The relationship between the constraint patterns and their correspondingly self-folded surfaces has been studied in the paper. Our 4D printing method presents a simple approach to quickly fabricate a 3D shell structure with smooth curved surfaces by fabricating a structure with accordingly designed material distribution