Articulated 3D model matching using multi-scale histograms of shape features for customized additive manufacturing

A novel additive manufacturing algorithm was developed to increase the consistency of three-dimensional (3D) printed curvilinear or conformal patterns on freeform surfaces. The algorithm dynamically and locally compensates the nozzle location with respect to the pattern geometry, motion direction, and topology of the substrate to minimize lagging or leading during conformal printing. The printing algorithm was implemented in an existing 3D printing system that consists of an extrusion-based dispensing module and an XYZ-stage. A dispensing head is fixed on a Z-axis and moves vertically, while the substrate is installed on an XY-stage and moves in the x–y plane. The printing algorithm approximates the printed pattern using nonuniform rational B-spline (NURBS) curves translated directly from a 3D model. Results showed that the proposed printing algorithm increases the consistency in the width of the printed patterns. It is envisioned that the proposed algorithm can facilitate nonplanar 3D printing using common and commercially available Cartesian-type 3D printing systems.

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Statistical Partial Constraints for 3D Model Matching and Pose Estimation Problems

Procedings of the British Machine Vision Conference 1993 ◽

10.5244/c.7.11 ◽

1993 ◽

Cited By ~ 2

Author(s):

M. Waite ◽

M. Orr ◽

R. Fisher ◽

J. Hallam

Keyword(s):

Pose Estimation ◽

3D Model ◽

Model Matching ◽

Estimation Problems

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“TORINO 1911” PROJECT: A CONTRIBUTION OF A SLAM-BASED SURVEY TO EXTENSIVE 3D HERITAGE MODELING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-225-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 225-234 ◽

Cited By ~ 6

Author(s):

F. Chiabrando ◽

C. Della Coletta ◽

G. Sammartano ◽

A. Spanò ◽

A. Spreafico

Keyword(s):

3D Model ◽

Handheld Devices ◽

Complex Environments ◽

Existing Structures ◽

Multi Scale ◽

Rapid Mapping ◽

Digital Documentation ◽

Immersive Visualization ◽

Interactive Navigation ◽

The University

In the framework of the digital documentation of complex environments the advanced Geomatics researches offers integrated solution and multi-sensor strategies for the 3D accurate reconstruction of stratified structures and articulated volumes in the heritage domain. The use of handheld devices for rapid mapping, both image- and range-based, can help the production of suitable easy-to use and easy-navigable 3D model for documentation projects. These types of reality-based modelling could support, with their tailored integrated geometric and radiometric aspects, valorisation and communication projects including virtual reconstructions, interactive navigation settings, immersive reality for dissemination purposes and evoking past places and atmospheres. The aim of this research is localized within the “Torino 1911” project, led by the University of San Diego (California) in cooperation with the PoliTo. The entire project is conceived for multi-scale reconstruction of the real and no longer existing structures in the whole park space of more than 400,000&thinsp;m<sup>2</sup>, for a virtual and immersive visualization of the Turin 1911 International “Fabulous Exposition” event, settled in the Valentino Park. Particularly, in the presented research, a 3D metric documentation workflow is proposed and validated in order to integrate the potentialities of LiDAR mapping by handheld SLAM-based device, the ZEB REVO Real Time instrument by GeoSLAM (2017 release), instead of TLS consolidated systems. Starting from these kind of models, the crucial aspects of the trajectories performances in the 3D reconstruction and the radiometric content from imaging approaches are considered, specifically by means of compared use of common DSLR cameras and portable sensors.

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Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation ◽

10.1115/msec2020-8379 ◽

2020 ◽

Author(s):

Zhuo Wang ◽

Chen Jiang ◽

Mark F. Horstemeyer ◽

Zhen Hu ◽

Lei Chen

Keyword(s):

Temperature Field ◽

Additive Manufacturing ◽

Uncertainty Quantification ◽

Surrogate Modeling ◽

Data Driven ◽

Modeling Framework ◽

High Quality ◽

Experimental Calibration ◽

Multi Scale ◽

Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

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