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Research Article
Matheus Mendes da Silva de Assis
Federal University of the ABC, Av. dos Estados, 5001, Santo André, SP 09210-580, Brazil
Corresponding Author
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Triply periodic minimal surfaces (TPMS) are porous structures which can be used to create multifunctional materials for various technological applications. TPMS have a complex geometry, therefore, additive manufacturing is shown as the ideal method for their manufacturing, due to the flexibility of such method. Thus, this work aims to predict the building orientation effects through machine learning process of additively manufactured triply periodic minimal surface scaffolds. DOE (Design of experiments) was used to determine the effect of the 30°, 60° and 90° angles of the X, Y and Z axes of the TPMS and ANOVA (Analysis of variance) to determine the relevant statistical interactions. Subsequently, the Multi-Layer Perceptron, Random Tree and Multiple Linear Regression machine learning algorithms were used through WEKA software to predict the cost and support material consumption for each TPMS. The results showed that the cost of TPMS can vary between about 12 and 15% according to the most expensive to the most economical building orientation. The Multiple Linear Regression algorithm obtained the best result for the cost prediction and in the support material consumption prediction the Multi-Layer Perceptron algorithm got the best.
Keywords
Triply periodic minimal surfaces, Building Orientation, Additive Manufacturing, Material Extrusion, Design of Experiments, Machine Learning
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This preprint is under consideration at The International Journal of Advanced Manufacturing Technology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Matheus Mendes da Silva de Assis
Federal University of the ABC, Av. dos Estados, 5001, Santo André, SP 09210-580, Brazil
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 17 Mar, 2021
No community comments so far
First submitted
On 13 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Triply periodic minimal surfaces (TPMS) are porous structures which can be used to create multifunctional materials for various technological applications. TPMS have a complex geometry, therefore, additive manufacturing is shown as the ideal method for their manufacturing, due to the flexibility of such method. Thus, this work aims to predict the building orientation effects through machine learning process of additively manufactured triply periodic minimal surface scaffolds. DOE (Design of experiments) was used to determine the effect of the 30°, 60° and 90° angles of the X, Y and Z axes of the TPMS and ANOVA (Analysis of variance) to determine the relevant statistical interactions. Subsequently, the Multi-Layer Perceptron, Random Tree and Multiple Linear Regression machine learning algorithms were used through WEKA software to predict the cost and support material consumption for each TPMS. The results showed that the cost of TPMS can vary between about 12 and 15% according to the most expensive to the most economical building orientation. The Multiple Linear Regression algorithm obtained the best result for the cost prediction and in the support material consumption prediction the Multi-Layer Perceptron algorithm got the best.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This preprint is available for download as a PDF.
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