## Minimizing material consumption of 3d printing with stress-guided optimization

**Authors: **Zheng, A., Bian, S., Chaudhry, E., Chang, J., Haron, H., You, L. and Zhang, J.

**Journal:** Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

**Volume:** 12141 LNCS

**Pages:** 588-603

**eISSN:** 1611-3349

**ISBN:** 9783030504250

**ISSN:** 0302-9743

**DOI:** 10.1007/978-3-030-50426-7_44

**Abstract:**

3D printing has been widely used in daily life, industry, architecture, aerospace, crafts, art, etc. Minimizing 3D printing material consumption can greatly reduce the costs. Therefore, how to design 3D printed objects with less materials while maintain structural soundness is an important problem. The current treatment is to use thin shells. However, thin shells have low strength. In this paper, we use stiffeners to stiffen 3D thin-shell objects for increasing the strength of the objects and propose a stress guided optimization framework to achieve minimum material consumption. First, we carry out finite element calculations to determine stress distribution in 3D objects and use the stress distribution to guide random generation of some points called seeds. Then we map the 3D objects and seeds to a 2D space and create a Voronoi Diagram from the seeds. The stiffeners are taken to be the edges of the Voronoi Diagram whose intersections with the edges of each of the triangles used to represent the polygon models of the 3D objects are used to define stiffeners. The obtained intersections are mapped back to 3D polygon models and the cross-section size of stiffeners is minimized under the constraint of the required strength. Monte-Carlo simulation is finally introduced to repeat the process from random seed generation to cross-section size optimization of stiffeners. Many experiments are presented to demonstrate the proposed framework and its advantages.

http://eprints.bournemouth.ac.uk/34297/

**Source:** Scopus

## Minimizing material consumption of 3d printing with stress-guided optimization

**Authors: **Zheng, A., Bian, S., Chaudhry, E., Chang, J., Haron, H., You, L. and Zhang, J.

**Conference:** ICCS 2020

**Dates:** 3-5 June 2020

**Journal:** Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

**Volume:** 12141 LNCS

**Pages:** 588-603

**eISSN:** 1611-3349

**ISBN:** 9783030504250

**ISSN:** 0302-9743

**DOI:** 10.1007/978-3-030-50426-7_44

**Abstract:**

© Springer Nature Switzerland AG 2020. 3D printing has been widely used in daily life, industry, architecture, aerospace, crafts, art, etc. Minimizing 3D printing material consumption can greatly reduce the costs. Therefore, how to design 3D printed objects with less materials while maintain structural soundness is an important problem. The current treatment is to use thin shells. However, thin shells have low strength. In this paper, we use stiffeners to stiffen 3D thin-shell objects for increasing the strength of the objects and propose a stress guided optimization framework to achieve minimum material consumption. First, we carry out finite element calculations to determine stress distribution in 3D objects and use the stress distribution to guide random generation of some points called seeds. Then we map the 3D objects and seeds to a 2D space and create a Voronoi Diagram from the seeds. The stiffeners are taken to be the edges of the Voronoi Diagram whose intersections with the edges of each of the triangles used to represent the polygon models of the 3D objects are used to define stiffeners. The obtained intersections are mapped back to 3D polygon models and the cross-section size of stiffeners is minimized under the constraint of the required strength. Monte-Carlo simulation is finally introduced to repeat the process from random seed generation to cross-section size optimization of stiffeners. Many experiments are presented to demonstrate the proposed framework and its advantages.

http://eprints.bournemouth.ac.uk/34297/

**Source:** Manual

**Preferred by: **Lihua You