Abstract
This research highlights the novelty of using Monte Carlo Simulation Method (MCSM) for material prediction in 3D printing. In the recent years, the production processes pay particular attention to optimize the use of materials. optimizing the product for a certain percentage positively affects the saving of resources, and this fact motivates the development of industries concerning the use of materials. The promising potential of solving this problem has great influence in Additive Manufacturing (AM), especially in 3D printing. In recent years, this technology has received a tremendous developmental jump by introducing the idea of using several materials simultaneously in one production process. Furthermore, a new concept of 3D printers is being introduced to implement this idea both in Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS) printing. There are still many factors to be considered, and one of them is the mechanical properties of potential multi-material products, especially the determination of these properties and the automation of this process. Therefore, this paper proposes one of the possible methods to solve this problem by introducing Rule of Mixture based on MCSM.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018 |
| Editors | Teen-Hang Meen |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 384-387 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538656099 |
| DOIs | |
| Publication status | Published - Jan 16 2019 |
| Event | 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018 - Yunlin, Taiwan Duration: Nov 16 2018 → Nov 18 2018 |
Publication series
| Name | Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018 |
|---|---|
| Country | Taiwan |
| City | Yunlin |
| Period | 11/16/18 → 11/18/18 |
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Keywords
- 3D printing
- Additive manufacturing
- Material property
- Monte Carlo simulation
- Rule of mixture
ASJC Scopus subject areas
- Biomedical Engineering
- Mechanics of Materials
- Safety, Risk, Reliability and Quality
- Management, Monitoring, Policy and Law
- Education
- Fluid Flow and Transfer Processes
- Industrial and Manufacturing Engineering
Cite this
Composite materials property determination by Rule of Mixture and Monte Carlo Simulation. / Yerbolat, Gaziz; Amangeldi, Shynggys; Ali, Md Hazrat; Badanova, Nazym; Ashirbeok, Asset; Islam, Gulzhahan.
Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018. ed. / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. p. 384-387 8615034 (Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Composite materials property determination by Rule of Mixture and Monte Carlo Simulation
AU - Yerbolat, Gaziz
AU - Amangeldi, Shynggys
AU - Ali, Md Hazrat
AU - Badanova, Nazym
AU - Ashirbeok, Asset
AU - Islam, Gulzhahan
PY - 2019/1/16
Y1 - 2019/1/16
N2 - This research highlights the novelty of using Monte Carlo Simulation Method (MCSM) for material prediction in 3D printing. In the recent years, the production processes pay particular attention to optimize the use of materials. optimizing the product for a certain percentage positively affects the saving of resources, and this fact motivates the development of industries concerning the use of materials. The promising potential of solving this problem has great influence in Additive Manufacturing (AM), especially in 3D printing. In recent years, this technology has received a tremendous developmental jump by introducing the idea of using several materials simultaneously in one production process. Furthermore, a new concept of 3D printers is being introduced to implement this idea both in Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS) printing. There are still many factors to be considered, and one of them is the mechanical properties of potential multi-material products, especially the determination of these properties and the automation of this process. Therefore, this paper proposes one of the possible methods to solve this problem by introducing Rule of Mixture based on MCSM.
AB - This research highlights the novelty of using Monte Carlo Simulation Method (MCSM) for material prediction in 3D printing. In the recent years, the production processes pay particular attention to optimize the use of materials. optimizing the product for a certain percentage positively affects the saving of resources, and this fact motivates the development of industries concerning the use of materials. The promising potential of solving this problem has great influence in Additive Manufacturing (AM), especially in 3D printing. In recent years, this technology has received a tremendous developmental jump by introducing the idea of using several materials simultaneously in one production process. Furthermore, a new concept of 3D printers is being introduced to implement this idea both in Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS) printing. There are still many factors to be considered, and one of them is the mechanical properties of potential multi-material products, especially the determination of these properties and the automation of this process. Therefore, this paper proposes one of the possible methods to solve this problem by introducing Rule of Mixture based on MCSM.
KW - 3D printing
KW - Additive manufacturing
KW - Material property
KW - Monte Carlo simulation
KW - Rule of mixture
UR - http://www.scopus.com/inward/record.url?scp=85062233627&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062233627&partnerID=8YFLogxK
U2 - 10.1109/AMCON.2018.8615034
DO - 10.1109/AMCON.2018.8615034
M3 - Conference contribution
AN - SCOPUS:85062233627
T3 - Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018
SP - 384
EP - 387
BT - Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
ER -