Journal Article

Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation

Gao, Baoyun, Yu, Shoukang, Liu, Bin, Peng, Shitong, Xiao, Haibo, Cao, Sheng

2024

Overview

Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO 2 equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.

• Publisher: Springer Nature Singapore,Springer Nature B.V,Springer
• Subject: Additive manufacturing / Alloy powders / Aluminum / Artificial Intelligence / Atomizing / Bayesian analysis / Control and Systems Theory / Cooling / Data imputation / Emissions / Energy consumption / Engineering / Environmental impact / Gas atomization / Heterogeneity / Impact prediction / Life cycle assessment / Machine Learning / Metal powders / Nickel alloys / Nickel base alloys / Original Article / Parameter estimation / Parameter uncertainty / Particle size / Physical properties / Raw materials / Robotics and Automation / Stochastic Kriging model / Titanium alloys / Titanium base alloys / Uncertainty