An optimal algorithm for 3D triangle mesh slicing

Minetto, Rodrigo; Volpato, Neri; Stolfi, Jorge; Gregori, Rodrigo M.M.H.; Silva, Murilo V. G. da

doi:10.1016/j.cad.2017.07.001

Cited by 72 publications

(38 citation statements)

References 13 publications

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“…The number of mesh triangles influences the way a part is sliced on the layer, which results in the construction of parts' contours at each layer for an additive machine, and thus contributes to the energy concentration for each specific part. This assumption is in good agreement with previously published studies [42,43].…”

Section: Feature Importance For Prediction Of Nominal Stresssupporting

confidence: 93%

Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Baturynska

2019

Applied Sciences

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Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

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Section: Feature Importance For Prediction Of Nominal Stresssupporting

confidence: 93%

Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Baturynska

2019

Applied Sciences

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“…This criterion obviously depends on the build orientation. The computation of section areas of a triangulated surface is a subject of interest in the CAD literature (see for example [34]).…”

Section: Minimizing the Variation Of Section Areasmentioning

confidence: 99%

Support optimization in additive manufacturing for geometric and thermo-mechanical constraints

Allaire

Bihr

Bogosel

2020

Struct Multidisc Optim

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“…To reduce the complexity of slicing algorithms, Reference [16] introduces interval trees, which reduces the complexity of mesh slicing algorithms by testing subsets of mesh triangles in given z-intervals. For regularly spaced slicing planes, the complexity can be further reduced by sorting the mesh triangles in ascending z-order [17].…”

Section: Parallel-planes Mesh Slicingmentioning

confidence: 99%

Level Sets of Weak-Morse Functions for Triangular Mesh Slicing

et al. 2020

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In the context of CAD CAM CAE (Computer-Aided Design, Manufacturing and Engineering) and Additive Manufacturing, the computation of level sets of closed 2-manifold triangular meshes (mesh slicing) is relevant for the generation of 3D printing patterns. Current slicing methods rely on the assumption that the function used to compute the level sets satisfies strong Morse conditions, rendering incorrect results when such a function is not a Morse one. To overcome this limitation, this manuscript presents an algorithm for the computation of mesh level sets under the presence of non-Morse degeneracies. To accomplish this, our method defines weak-Morse conditions, and presents a characterization of the possible types of degeneracies. This classification relies on the position of vertices, edges and faces in the neighborhood outside of the slicing plane. Finally, our algorithm produces oriented 1-manifold contours. Each contour orientation defines whether it belongs to a hole or to an external border. This definition is central for Additive Manufacturing purposes. We set up tests encompassing all known non-Morse degeneracies. Our algorithm successfully processes every generated case. Ongoing work addresses (a) a theoretical proof of completeness for our algorithm, (b) implementation of interval trees to improve the algorithm efficiency and, (c) integration into an Additive Manufacturing framework for industry applications.

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An optimal algorithm for 3D triangle mesh slicing

Cited by 72 publications

References 13 publications

Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

Support optimization in additive manufacturing for geometric and thermo-mechanical constraints

Level Sets of Weak-Morse Functions for Triangular Mesh Slicing

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