Integrated Computational and Experimental Study of Additively Manufactured Steels
Time: Thu 2023-04-27 10.00
Location: F3, Lindstedtsvägen 26 & 28, Stockholm
Video link: https://kth-se.zoom.us/j/65939831819
Subject area: Materials Science and Engineering
Doctoral student: Chia-Ying Chou , Materialvetenskap
Opponent: Professor Johan Moverare, Konstruktionsmaterial, Linköpings universitet
Supervisor: Univ.lektor Greta Lindwall, Materialvetenskap; Professor Joakim Odqvist, Materialvetenskap; Professor Annika Borgenstam, Materialvetenskap
The design freedom Additive Manufacturing (AM) offers provides new solutions for improving functionality in industrial applications. It also offers unique opportunities when it comes to materials design.
Powder Bed Fusion – Laser Beam (PBF-LB) is currently one of the most popular commercial AM techniques for metallic materials partly due to the relatively low surface roughness and the large design flexibility. However, the number of materials suitable for the PBF-LB process is still rather low and to accelerate the development of grades tailored for this AM process, dedicated computational tools for alloy design are needed. Of importance for materials design, is computational thermodynamics and kinetics coupled with CALPHAD materials descriptions since it enables calculations for multicomponent materials making it possible to predict the effect of varying composition.
In this thesis, computational thermodynamic and kinetics coupled with materials characterization are applied to study the microstructure evolution during PBF-LB. Two material classes are in focus – hot-work tool steels and ferritic stainless steels. For the hot-work tool steel, the cooling rates during PBF-LB processing are high enough to induce martensite transformation and in the as-built microstructure a martensitic matrix is observed and some fraction of retained austenite. A solidification sub-structure due to micro-segregation during printing is also observed. Solidification calculations are performed to predict the micro-segregation showing agreement with experimental measurements. The segregation results are then used as input to a semi-empirical martensite start temperature model making it possible to explain the location and amount of retained austenite.
In addition to compositional optimization, a computational framework for AM alloy design needs to include the possibility to tailor the AM post heat treatments. An alternative to the conventional hardening treatment is thus studied in the current work. A model for precipitation kinetics is combined with experimental characterization to explore the effect of tempering on the as-built microstructure in comparison to the tempering effect on an austenitized microstructure. The results show that the precipitation kinetics is strongly dependent on the starting structure and that direct tempering of the as-built microstructure changes the precipitation sequence compared to the conventional heat treatment route. The calculations reproduce this result suggesting that it is a thermodynamic effect stemming from different matrix compositions.
The other material class, the ferritic stainless steels, is studied in terms of its response to post-heat treatments. The as-built microstructure is characterized by high dislocation density and a fine grain structure in some cases as well as a solidification sub-structure. The mechanical properties of the as-built material are in general good for these steels, however, stress relieving is most often a required post process for the as-built components which may decrease the mechanical properties. To maximize the gained benefits from the unique process condition of PBF-LB, simulations are applied to study the possibility of post heat treatment optimization.
To construct a computational framework for AM materials design, multiscale modeling capabilities are needed. This work shows the value of computational thermodynamics and kinetics for understanding the materials behavior on the microscale and hence, contributes to the construction of such a framework. By understanding the physical metallurgy, and enable modeling of the AM processes, the industrialization of AM can be accelerated.