AI 3D Printing Process Parameters Optimization

Park Seok, Nguyen Son

Abstract

Optimization parameters of Selective Laser Melting (SLM) process is a significant question currently. Due to attractive advantages, namely high density of printed products and freely design, the SLM has been increasingly applied in industrial manufacturing. However, not only various influenced factors but also their range affects to the printing process. Therefore, it is difficult and requires much testing time and cost to select a suitable process parameter for manufacturing a desirable product. In this article, a supervised learning Artificial Neural Network was applied to build an optimization system for finding out optimal process parameters. Inputs of the system are desirable properties of a product as relative density ratio while outputs are the crucial parameters as laser power, laser velocity, hatch distance, and layer thickness. The developed system is a powerful contribution to industrial SLM manufacturing. By applying the system, it requires less pre-manufacturing expenditure and also helps the printing users to choose approximately process parameters for printing out a desirable product.

Download


Paper Citation


in Harvard Style

Seok P. and Son N. (2020). AI 3D Printing Process Parameters Optimization.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 356-361. DOI: 10.5220/0008903303560361


in Bibtex Style

@conference{icaart20,
author={Park Seok and Nguyen Son},
title={AI 3D Printing Process Parameters Optimization},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={356-361},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008903303560361},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - AI 3D Printing Process Parameters Optimization
SN - 978-989-758-395-7
AU - Seok P.
AU - Son N.
PY - 2020
SP - 356
EP - 361
DO - 10.5220/0008903303560361