Energy efficiency optimization for ecological 3D printing based on adaptive multi-layer customization. (1st February 2020)

• Record Type: Journal Article
• Authors: Xu, Jinghua; Wang, Kang; Sheng, Hongsheng; Gao, Mingyu; Zhang, Shuyou; Tan, Jianrong
• Is Part Of: Journal of cleaner production. Volume 245(2020)
• Publication Date: 2020-02-01
• Subjects: Energy efficiency optimization -- Ecological 3D printing(3DP) -- Adaptive multi-layer customization (AMC) -- Generative adversarial network -- Infill trajectories planning; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.118826
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: This paper presents an energy efficient optimization method for ecological 3D printing based on Adaptive Multi-Layer Customization (AMC). With the widespread application in end-use fields, the 3D printing (3DP) has already attracted great ecological attentions in energy efficiency, material saving and emissions reduction for better sustainability. Firstly, the energy and material consumption model of 3DP is built by decomposing 3DP into three sub-systems: thermal, mechanical, and auxiliary. The potential impacts on the ecological environment can be further evaluated by correlated degree using covariance analysis. The customized parameters of 3DP such as adaptive layer thickness, infill patterns, infill trajectories are sequentially and deeply investigated to form customization schemes for higher success rate by parameter combination. Considering the implicit non-linear relationships between energy efficiency and the customized parameters, adaptive Generative Adversarial Network (GAN) is built to improve calculation accuracy and prevent premature convergence inspired by Game Theory. To determine the best customization scheme according to the requirements, the mathematical model of AMC is built using multi-objective optimization (MOO). The physical experiment of energy efficiency is implemented by testing energy consumption, temperature, emissions and scanning electron micrograph (SEM). The energy efficiency is improved by maximum ratio of 11.51% and the maximum total carbon emission is reduced by 49.91% for ecological 3DP. The experiment proves that the AMC method can improve energy efficiency of complex functional specimens in highly customized fields such as medical healthcare and astronautics manufacturing industry. Highlights: Energy efficiency optimization is proposed for ecological 3D printing (3DP) via adaptive multi-layer customization (AMC). AMC slices various 3D objects into multi-layer using customized parameters such as infill patterns, trajectories planning. Represent the relationships between energy efficiency and customized parameters via Generative Adversarial Network (GAN). Energy efficiency is improved by maximum ratio of 11.51% and total carbon emission is reduced by 49.91% for ecological 3DP. Physical experiment involves energy consumption, temperature, emissions and scanning electron micrograph (SEM).