Open AccessAccurate numerical prediction of thermo-mechanical behaviour and phase fractions in SLM components of advanced high strength steels for automotive applications
Author(s) -
Kiranmayi Abburi Venkata,
Rohith Uppaluri,
Bernd Schob,
Camilo Zopp,
Richard Kordass,
Jan Bohlen,
Matthias Höfemann,
Marcin Kasprowicz,
A. Pawlak,
Edward Chlebus
Publication year - 2022
Publication title -
technologies for lightweight structures
Language(s) - English
Resource type - Journals
ISSN - 2512-4587
DOI - 10.21935/tls.v5i1.145
Subject(s) - materials science , austenite , selective laser melting , automotive industry , deformation (meteorology) , deep drawing , mechanical engineering , hardening (computing) , sheet metal , composite material , microstructure , engineering , layer (electronics) , aerospace engineering
Conventional crash absorber in automotive applications, so called crash boxes are fabricated via deep drawn sheet metal resulting in significant lead times and costs. Laser Powder Bed Fusion processes, like Selective Laser Melting (SLM) offer an attractive alternative for the fabrication of crash parts while eliminating any need for costly forming dies and reducing the lead times, provided required material properties are achieved. Reliable numerical simulation model to predict the SLM build process with greater spatial resolution and accuracy is indispensable to understand the process further in order to ensure its applicability to crash structures. In this paper, an improved simulation methodology for SLM process is presented to predict the material behaviour via temperature, deformation, hardening, flow stress and phase fractions throughout the component with increased accuracy and greater resolution. To achieve desired spatial resolution, the equivalent layers are subdivided into individual tracks, which are then deposited sequentially to simulate the printing process. The material is a medium manganese (7-8 %) transformation induced plasticity (TRIP) steel with austenite and martensite primary phases. The multiple solid-state phase transformation cycles undergone by the material are modelled in the simulation and the final phases are predicted. The results indicate improved accuracy and higher resolution in predictions for temperature, phase fractions and deformation.