Tempering kinetics during multilayer laser additive manufacturing of a ferritic steel. (November 2022)

• Record Type: Journal Article
• Authors: Mukherjee, T.; DebRoy, T.; Lienert, T.J.; Maloy, S.A.; Lear, C.R.; Hosemann, P.
• Is Part Of: Journal of manufacturing processes. Volume 83(2022)
• Page Start: 105
• Page End: 115
• Publication Date: 2022-11
• Subjects: 3D printing -- Directed energy deposition -- Martensite -- Heat transfer and fluid flow -- Neural network -- Johnson Mehl Avrami -- Grade 91 steel; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2022.08.061
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: Grade 91 steel forms martensite during additive manufacturing and the extent of tempering of martensite significantly affects the mechanical properties of parts. Currently, there is a lack of quantitative understanding of the tempering kinetics for Grade 91 steel, and as a result, the effects of repeated thermal cycles on properties for different processing conditions cannot be determined. Here we evaluate the tempering kinetics by determining the constant terms in the Johnson Mehl Avrami kinetic equation from the tempering data available in the literature and the thermal cycles computed using a rigorously-tested heat and fluid flow model of multi-layer additive manufacturing. The raw tempering data are cleaned using a neural network to enhance accuracy. The lower layers experience repeating cycles of heating and cooling when the upper layers are added. As a result, the hardness is reduced owing to the tempering of martensite. In contrast, martensite formed in the upper layers is not tempered to the same extent and the hardness remains high. Therefore, the hardness of the part increases with the distance from the substrate. Variations in the heat input at different laser powers and scanning speeds significantly affect the extent of tempering. Since the method used here can provide a quantitative understanding of the tempering of martensite and the spatial variation of hardness, it can be used to tailor the microstructure and hardness of heat treatable printed metallic parts. Graphical abstract: Unlabelled Image Highlights: Tempering kinetics of laser directed energy deposition of Grade 91 steel is studied. Tempering data are cleaned using a neural network and used to estimate JMA constants. A heat and fluid flow model and JMA kinetics are combined to evaluate tempering. Spatial and temporal variations of hardness of Grade 91 steel are investigated. Computed layer wise variations in tempering and hardness agree with the experiments.