Utilizing the Structure-Property Relationship of Porosity and Composition in Additively Manufactured Metallic Systems
Abstract
As the focus in additive manufacturing shifts to manufacturing parts in load-bearing applications, microstructure and composition become of critical importance. In this work, we aim to enhance our understanding of the relationship between the process parameters, composition, and the resulting microstructure of the additively manufactured parts, utilizing the structure-processing relationships. These relationships will be examined utilizing a combination of computational and experimental approaches. The two major questions we will explore are the compositional relationship of functional gradients to their printability and the relationship of thermal histories on porosity formation. The first question explores the compositional effects of diffusion, phase formation, and evaporation; while the second question explores the relationship of melting, evaporation, and process thermal history on defect formation.
The integrity of functional gradients in alloys tends to be compromised by the presence of brittle phases. Recently, CALPHAD-based thermodynamics tools have been used to generate isothermal phase diagrams that are in turn utilized to plan gradient paths that completely avoid these phases. However, existing frameworks rely extensively on the (limited) ability of humans to visualize and navigate high-dimensional spaces. To tackle this challenge, a Machine Learning approach was used and validated by designing and additively manufacturing a functional gradient in bulk samples from 316L stainless steel to pure chromium with a multi-material direct laser deposition system. The compositional space was then increased from three powder feedstock to four, and a functional gradient from Fe9Cr to W was fabricated.
Porosity is an expensive and pervasive problem in additively manufactured parts. To minimize materials waste and save time we propose a machine learning algorithm that can address the likelihood of porosity formation based on thermal signatures to feed into a process plan optimization methodology. The proposed scheme combines extensive cross-sectional optical microscopy data from a laser powder bed fusion printed Ti-6Al-4V cylinder with a discrete thermal heat source model that produces a thermal signature at specific locations within an additively manufactured component. Experimentally determined porosity distributions are used to train and test a machine-learning algorithm to identify the likelihood of porosity formation at a given location. The effectiveness of this methodology is assessed and the overall link between porosity formation and thermal signatures is discussed.
Citation
Eliseeva, Olga Valerievna (2021). Utilizing the Structure-Property Relationship of Porosity and Composition in Additively Manufactured Metallic Systems. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /193109.