scholarly journals An efficient triangle mesh slicing algorithm for all topologies in additive manufacturing

2020 ◽  
Author(s):  
Bethany King ◽  
Allan Rennie ◽  
Graham Bennett
Keyword(s):  
Additive Manufacturing ◽  
Triangular Mesh ◽  
Construction Method ◽  
Large Percentage ◽  
Triangle Mesh ◽  
Worst Case ◽  
Sorting Method ◽  
Construction Algorithm ◽  
Edge Matching ◽  
Maximum Improvement

AbstractTo date, slicing algorithms for additive manufacturing is the most effective for favourable triangular mesh topologies; worst-case models, where a large percentage of triangles intersect each slice plane, take significantly longer to slice than a like-for-like file. In larger files, this results in a significant slicing duration, when models are both worst cases and contain more than 100,000 triangles. The research presented here introduces a slicing algorithm which can slice worst-case large models effectively. A new algorithm is implemented utilising an efficient contour construction method, with further adaptations, which make the algorithm suitable for all model topologies. Edge matching, which is an advanced sorting method, decreases the number of sorts per edge from n total number of intersections to two, alongside additional micro-optimisations that deliver the enhanced efficient contour construction algorithm. The algorithm was able to slice a worst-case model of 2.5 million triangles in the 1025s. Maximum improvement was measured as 9400% over the standard efficient contour construction method. Improvements were also observed in all parts in excess of 1000 triangles. The slicing algorithm presented offers novel methods that address the failings of other algorithms described in literature to slice worst-case models effectively.

Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data

2019 ◽  
Author(s):  
Nathan Decker ◽  
Qiang Huang
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Three Dimensional ◽  
Triangular Mesh ◽  
Geometric Accuracy ◽  
New Approach ◽  
Machine Learning Model ◽  
3D Printed ◽  
3D Shapes ◽  
Complex Parts

Abstract While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.

scholarly journals Cumulative Inaccuracies in Implementation of Additive Manufacturing Through Medical Imaging, 3D Thresholding, and 3D Modeling: A Case Study for an End-Use Implant

2020 ◽  
Vol 10 (8) ◽  
pp. 2968 ◽  
Author(s):  
Jan Sher Akmal ◽  
Mika Salmi ◽  
Björn Hemming ◽  
Linus Teir ◽  
Anni Suomalainen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Additive Manufacturing ◽  
Medical Imaging ◽  
3D Modeling ◽  
Computer Aided Design ◽  
Digital Design ◽  
Worst Case ◽  
Custom Made ◽  
End Use

In craniomaxillofacial surgical procedures, an emerging practice adopts the preoperative virtual planning that uses medical imaging (computed tomography), 3D thresholding (segmentation), 3D modeling (digital design), and additive manufacturing (3D printing) for the procurement of an end-use implant. The objective of this case study was to evaluate the cumulative spatial inaccuracies arising from each step of the process chain when various computed tomography protocols and thresholding values were independently changed. A custom-made quality assurance instrument (Phantom) was used to evaluate the medical imaging error. A sus domesticus (domestic pig) head was analyzed to determine the 3D thresholding error. The 3D modeling error was estimated from the computer-aided design software. Finally, the end-use implant was used to evaluate the additive manufacturing error. The results were verified using accurate measurement instruments and techniques. A worst-case cumulative error of 1.7 mm (3.0%) was estimated for one boundary condition and 2.3 mm (4.1%) for two boundary conditions considering the maximum length (56.9 mm) of the end-use implant. Uncertainty from the clinical imaging to the end-use implant was 0.8 mm (1.4%). This study helps practitioners establish and corroborate surgical practices that are within the bounds of an appropriate accuracy for clinical treatment and restoration.

Optimization and Application of STL Model Slicing Algorithm in Rapid Prototyping

2012 ◽  
Vol 605-607 ◽  
pp. 669-672
Author(s):  
Chun Xiang Wang ◽  
Zhen Hua Li
Keyword(s):  
Rapid Prototyping ◽  
Optimization Algorithm ◽  
High Efficiency ◽  
Triangular Mesh ◽  
Contour Line ◽  
Triangle Mesh ◽  
Output Section ◽  
Important Link ◽  
Relationship Of ◽  
Delivery Line

The stratification of STL files model is an important link of rapid prototyping systems. In order to improve the efficiency of layered, this paper puts forward a layered optimization algorithm: According to the triangle mesh generation's two axis coordinates, we can group and sort from small to large, then get the line intersection on these triangular mesh, and output section contour line and data in turn. This algorithm can not only save time, but also improve the layered efficiency, because it is not necessary to establish topology relation of the triangle mesh and to deal with the connection relationship of delivery line. Through analysis of the turtle and bucket teeth model stratified, the practicality and high efficiency of the proposed algorithm is verified.

scholarly journals Constructions of general polynomial lattices for multivariate integration

2007 ◽  
Vol 76 (1) ◽  
pp. 93-110 ◽  
Author(s):  
Peter Kritzer ◽  
Friedrich Pillichshammer
Keyword(s):  
Hilbert Spaces ◽  
Lattice Rules ◽  
Irreducible Polynomials ◽  
Worst Case ◽  
Multivariate Integration ◽  
Construction Algorithm ◽  
General Polynomial ◽  
Construction Algorithms ◽  
Digital Nets

We study a construction algorithm for certain polynomial lattice rules modulo arbitrary polynomials. The underlying polynomial lattices are special types of digital nets as introduced by Niederreiter. Dick, Kuo, Pillichshammer and Sloan recently introduced construction algorithms for polynomial lattice rules modulo irreducible polynomials which yield a small worst-case error for integration of functions in certain weighted Hilbert spaces. Here, we generalize these results to the case where the polynomial lattice rules are constructed moduloarbitrarypolynomials.

Development of Bulk Metallic Glasses and their Composites by Additive Manufacturing – Evolution, Challenges and a Proposed Novel Solution

2019 ◽  
Vol 1155 ◽  
pp. 1-28
Author(s):  
Muhammad Musaddique Ali Rafique ◽  
Dong Qiu ◽  
Milan Brandt
Keyword(s):  
Additive Manufacturing ◽  
Metallic Glasses ◽  
Structural Engineering ◽  
Bulk Metallic Glasses ◽  
Single Step ◽  
Alloy Chemistry ◽  
Edge Matching ◽  
Type Size ◽  
Inoculation Procedure ◽  
Large Elastic Strain

Bulk metallic glasses (BMGs) and their composites (BMGMCs) have emerged as competitive materials for structural engineering applications exhibiting superior tensile strength, hardness along with very large elastic strain limit. However, they suffer from lack of ductility and subsequent low toughness due to the inherent brittleness of the glassy structure which makes them amenable to failure without appreciable yielding. Various mechanisms and methods have been proposed to counter this effect out of which, recently Additive Manufacturing has gained widespread attention. It is proposed that additive manufacturing can overcome these difficulties in single step due to inherent existence of very high cooling rate in the process which is essential for glass formation. This, when coupled with careful selection of alloy chemistry is proposed to be the best solution to fabricate near net shape parts in a single step with excellent properties. In this report, an effort has been made to describe one possible route to achieve this. Solidification processing employing carefully selected inoculants based on edge to edge matching technique along with the carefuly controlled inoculation procedure is proposed to reflect upon enhanced mechanical properties. It is hypothesized that number density, size and distribution of ductile crystalline phase would best be able to improve microstructure and hence properties. This is meant to be controlled by manipulating type, size and the amount of inoculants. The proposed methodology is claimed to bear maximum potential.

scholarly journals INDOOR SPATIAL DATA CONSTRUCTION FROM TRIANGLE MESH

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 101-108
Author(s):  
D. Kim ◽  
A. Bolat ◽  
K.-J. Li
Keyword(s):  
Spatial Data ◽  
Experimental Studies ◽  
Triangular Mesh ◽  
Mesh Simplification ◽  
Geometric Representation ◽  
Triangle Mesh ◽  
Indoor Space ◽  
Fine Grained ◽  
Standard Data ◽  
Spatial Data Model

<p><strong>Abstract.</strong> The 3D triangle mesh is widely used to represent indoor space. One of widely used methods of generating 3D triangle mesh data of indoor space is the construction from the point cloud collected using LIDAR. However, there are many problems in using generated triangle mesh data as a geometric representation of the indoor space. First, the number of triangles forming the triangle mesh is very large, which results in a bottleneck of the performance for storage and management. Second, no consideration on the properties of indoor space has been done by the previous work on mesh simplification for indoor geometric representation. Third, there is no research to construct indoor spatial standard data from triangle mesh data. For resolving these problems, we propose the a method for generating triangular mesh data for indoor geometric representation based in the observations mentioned above. First this method removes unnecessary objects and reduces the number of surfaces from the original fine-grained triangular mesh data using the properties of indoor space. Second, it also produces indoor geometric data in IndoorGML &amp;ndash; an OGC standard for indoor spatial data model. In experimental studies, we present a case study of indoor triangle mesh data from real world and compare results with raw data.</p>

Development of Bulk Metallic Glasses and their Composites by Additive Manufacturing - Evolution, Challenges and a Proposed Novel Solution

2021 ◽  
Vol 1163 ◽  
pp. 1-26
Author(s):  
Muhammad Musaddique Ali Rafique ◽  
Emeodi Christian Emeka
Keyword(s):  
Additive Manufacturing ◽  
Metallic Glasses ◽  
Structural Engineering ◽  
Bulk Metallic Glasses ◽  
Single Step ◽  
Alloy Chemistry ◽  
Edge Matching ◽  
Type Size ◽  
Inoculation Procedure ◽  
Large Elastic Strain

Bulk metallic glasses (BMGs) and their composites (BMGMCs) have emerged as competitive materials for structural engineering applications exhibiting superior tensile strength, hardness along with very large elastic strain limit. However, they suffer from lack of ductility and subsequent low toughness due to the inherent brittleness of the glassy structure which makes them amenable to failure without appreciable yielding. Various mechanisms and methods have been proposed to counter this effect out of which, recently Additive Manufacturing has gained widespread attention. It is proposed that additive manufacturing can overcome these difficulties in single step due to inherent existence of very high cooling rate in the process which is essential for glass formation. This, when coupled with careful selection of alloy chemistry is proposed to be the best solution to fabricate near net shape parts in a single step with excellent properties. In this report, an effort has been made to describe one possible route to achieve this. Solidification processing employing carefully selected inoculants based on edge to edge matching technique along with the carefuly controlled inoculation procedure is proposed to reflect upon enhanced mechanical properties. It is hypothesized that number density, size and distribution of ductile crystalline phase would best be able to improve microstructure and hence properties. This is meant to be controlled by manipulating type, size and the amount of inoculants. The proposed methodology is claimed to bear maximum potential.

Sign in / Sign up

Export Citation Format

Share Document