Experimental-Numerical Analysis of Microstructure-Property Linkages for Additively Manufactured Materials
Research output: Contribution to book/conference proceedings/anthology/report › Chapter in book/anthology/report › Contributed › peer-review
The innovation of new or improved products fabricated from additive manufacturing processes with desired properties depends on a multitude of trials as stated by the Materials Genome Initiative for Global Competitiveness of the US National Science and Technology Council. Therefore, a systematic approach is essential to accelerate materials development. This can be realised by developing systematic materials knowledge in the form of process-structure-property linkages. In this envisioned framework, the present work aims to derive the structure-property linkages of additively manufactured Ti-6Al-4V alloy. One main focus is to investigate the influence of potential defects, in the form of pores, inherited from the fabrication process on the fatigue properties. For this purpose, the pore microstructure is obtained by x-ray computed tomography and a low-dimensional representation of the structure is derived by a statistical analysis. In a following numerical analysis, statistical volume elements (SVEs) with varying pore microstructures are reconstructed and microscale crystal plasticity simulations are performed in DAMASK to obtain the material properties such as yield strength and fatigue indicator parameters (FIPs). The influence of pore statistics on FIPs is obtained numerically and a comparison with Murakami’s empirical square root area concept is made. In a second part, the influence of the grain microstructure on mechanical properties is analysed. To this end, the grain microstructure is obtained by scanning electron microscopy (SEM) for specimens manufactured with different process configurations. Those structures are characterised through spatial three-point auto-correlation functions. The main properties of this high-dimensional descriptor are transformed to a low-dimensional representation by employing principal component analysis (PCA). Using LASSO regression, a meta model is derived, which allows for linking the microstructure to experimentally obtained micro hardness. This makes predictions of the hardness for new, unknown microstructures possible.
|Title of host publication||Advanced Structured Materials|
|Publisher||Springer Science and Business Media B.V.|
|Number of pages||18|
|Publication status||Published - 2022|
|Series||Advanced Structured Materials|