Please use this identifier to cite or link to this item:
Title: Laser power based surface characteristics models for 3-D printing process
Authors: Garg, Akhil
Lam, Jasmine Siu Lee
Savalani, Monica Mahesh
Keywords: Engineering::Environmental engineering
Issue Date: 2015
Source: Garg, A., Lam, J. S. L., & Savalani, M. M. (2018). Laser power based surface characteristics models for 3-D printing process. Journal of Intelligent Manufacturing, 29(6), 1191-1202. doi:10.1007/s10845-015-1167-9
Journal: Journal of Intelligent Manufacturing
Abstract: Selective laser melting (SLM) is one of the important 3-D Printing processes that builds components of complex 3D shapes directly from the metal powder. It is widely used in manufacturing industries and is operated on significant amount of laser power drawn from the electric grid. The literature reveals that the properties such as surface roughness, waviness, tensile strength and dimensional accuracy of an SLM fabricated parts, depend on the laser power and can be improved by its appropriate adjustment. Determination of accurate values of laser power and the other inputs could lead to an improvement in energy efficiency and thus contributing to a clean and healthy environment. For determining the accurate value of laser power in achieving the required surface characteristics, the formulation of generalized mathematical models is an essential pre-requisite. In this context, an artificial intelligence approach of multi-gene genetic programming (MGGP) which develops the functional expressions between the process parameters automatically can be applied. The present work introduces an ensemble-based-MGGP approach to model the SLM process. Experiments on the SLM process with measurement of surface characteristics, namely surface roughness and waviness, based on the variations of laser power and other inputs are conducted, and the proposed ensemble-based-MGGP approach is applied. Statistical evaluation concludes that the performance of the proposed approach is better than that of the standardized MGGP approach. Sensitivity and parametric analysis conducted reveals the hidden relationships between surface characteristics and the laser power, which can be used to optimize the SLM process both economically and environmentally.
ISSN: 0956-5515
DOI: 10.1007/s10845-015-1167-9
Rights: © 2015 Springer Science+Business Media New York. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

Citations 20

Updated on Dec 5, 2022

Web of ScienceTM
Citations 20

Updated on Dec 9, 2022

Page view(s)

Updated on Dec 9, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.