ProgramMaster Logo
Conference Tools for 2023 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Symposium
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
Sponsorship TMS Materials Processing and Manufacturing Division
TMS Structural Materials Division
TMS: Additive Manufacturing Committee
TMS: Phase Transformations Committee
TMS: Advanced Characterization, Testing, and Simulation Committee
Organizer(s) Sharniece Holland, Washington University in St. Louis
Eric J. Payton, University of Cincinnati
Edwin J. Schwalbach, Air Force Research Labroatory
Joy Gockel, Colorado School of Mines
Ashley Elizabeth Paz y Puente, University of Cincinnati
Paul Wilson, The Boeing Company
Amit K. Verma, Carnegie Mellon University
Sriram Vijayan, The Ohio State University
Jake Benzing, National Institute of Standards and Technology
Scope The transient heat transfer conditions encountered in additive manufacturing (AM) result in unusual microstructures and textures that can have different properties from conventional wrought or cast processes. The unique microstructure results from the combination of rapid melting and solidification from the AM process. The directional heat transfer results in strongly textured columnar grains, and this microstructure affects the mechanical properties of the final part. Conventionally processed products have been considered superior compared to AM in many of the most demanding and safety critical engineering applications due to the heterogeneity and orientation dependency of mechanical properties, potential for life-limiting defect content, and qualification challenges. This limits adoption of AM parts where they could otherwise offer an advantage, for example in weight savings or reduction in final machining. Mechanical anisotropy results from the strong crystallographic texture in as-fabricated AM parts, and this anisotropy can be influenced with an optimization of the laser scanning strategy or a post fabrication heat treatment. Because the initial microstructures from AM are different from conventional processes, optimal heat treatment times and temperatures for AM materials can differ from those used in conventional thermomechanical processing. The lack of standardization between machines creates an additional level of complexity. As a result, the qualification of materials from AM would benefit from an accurate digital twin of the process, capable of predicting defect probabilities and local microstructure heterogeneity. This symposium will explore the unique thermal sequence of AM materials and their distinctive microstructures, which affect their performance.

Contributions are sought that address microstructure development during AM from experimental and computational perspectives, including but not limited to:
- quantitative microstructure characterization
- mechanisms of defect formation
- correlation of in-situ process monitoring data with microstructure
- defect probability predictions
- uncertainty quantification
- multiphysics simulations, both of the manufacturing process and the effects of microstructure on performance.

References
[1] Seifi, M., et al. "Progress towards metal AM standardization to support qualification and certification." JOM 69.3 (2017): 439-455.
[2] Kok, Y., et al. "Anisotropy and heterogeneity of microstructure and mechanical properties in metal AM: A critical review." Materials & Design 139 (2018): 565-586.
[3] Lindgren, L.-E., and A. Lundbäck. "Approaches in computational welding mechanics applied to AM: Review and outlook." Comptes Rendus Mécanique 346.11 (2018): 1033-1042.
[4] Gatsos, T., et al. "Review on computational modeling of process–microstructure–property relationships in metal AM." JOM 72.1 (2020): 403-419.
[5] Rezaei, A., et al. "Microstructural and mechanical anisotropy of selective laser melted IN718 superalloy at room and high temperatures using small punch test." Materials Characterization 162 (2020): 110200.

Abstracts Due 07/17/2022
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

3D Computer Vision and Machine Learning for Porosity Analysis in Additive Manufacturing
A study of microstructural and mechanical properties of 14YWT Oxide Dispersion Strengthened steel fabricated using Laser Powder Bed Fusion Additive Manufacturing from Gas Atomized Reaction Synthesis feedstock
Additive Manufacturing beyond the Gaussian Beam: Insights from Microstructure-based Modeling Studies
Build Geometry and Parameter Influence on Alloy 718 Microstructure, Properties and Spatial Variation in Additive Manufacturing
Characterization of Titanium Additions in Selectively Laser Melted High-Strength Aluminum Alloy by Correlative X-Ray and Electron Microscopy
Control of Residual Stress and Distortion in Metal Additive Manufacturing via Inverse Mapping of Textures
Correlative Modeling of Laser Powder Bed Fusion Surface Characteristics to Internal Defects
Effects of Laser Process Parameters on Denudation Zone Width in Laser Powder Bed Fusion Additive Manufacturing
Effects of Processing Conditions and Build Geometry on Microstructure Development in Laser Powder Bed Fusion and Wire Arc Additively Manufactured 316L
Heterogeneous Microstructure and Location-Specific Mechanical Performance of Ti-6Al-4V Parts Made by Laser Directed Energy Deposition
In situ monitoring of recrystallization during Laser Powder Bed Fusion of 316L stainless steel by means of Synchrotron X-ray diffraction
Intentionally Seeding Pores in Laser Powder Bed Fusion IN718: Microstructure, Defects, and Fatigue
Investigating the potential of indentation-based methods for microstructure heterogeneity assessment during manufacturing
Large-scale image analysis of melt pools in complex additively manufactured artifacts
Location specific characterization of additively manufactured stainless steel to inform build data analytics
Long-term Process Stability in Laser Powder Bed Fusion
Microstructural and Mechanical Validation of Thin-Walled Additively Manufactured Inconel 625
Microstructure and mechanical property variations in commercially produced laser powder bed fusion 316L stainless steel
Microstructure Evolution According to Heat Treatment Design of Alloy 625 Produced by Selective Laser Melting
NASA’s approach on the evaluations of “material engineering equivalence” methodology in achieving and sustaining efficient qualification and certification of AM materials and parts
Opportunities & Challenges with Laser Powder Bed Fusion for Automotive Applications: Steel and Aluminum Alloys
Optimizing Creep Performance of Haynes 282 Printed via Laser Powder Bed Fusion through Microstructure Control
Predicting Crystallographic Texture in Laser Powder Bed Fusion via a Machine Learning Approach
Quantification of Microstructural Heterogeneities in Additively Manufactured and Heat-Treated Haynes 282
Quantitative Analysis of Computed Tomography Characterization of Porosity in AM Ti64 using Serial Sectioning Ground Truth
Quantitative analysis of low concentration elements at the nanoscale in additively manufactured alloys
Strategizing with hot isostatic pressing treatments to increase productivity during post-processing of laser-melted Inconel 718 parts
Strong impact of minor elements on the microstructural evolution of an additively manufactured Inconel 625 alloy
Synchrotron-based X-ray microtomography characterization of solidification cracks in additively manufactured IN738LC alloy
The Effect of Beam Shaping Strategies on Additively Manufactured Microstructures
The impact of volumetric energy density on mechanical properties of additively manufactured 718 Ni alloy
Towards Validation of Thermo-Mechanical Finite Element Modeling of the Additive Manufacturing Solidification Process
Use of Profilometry-based Indentation Plastometry (PIP) to Study Inhomogeneities in Additively Manufactured Components
X-ray Diffraction Peak Estimation Using In-Situ Melt-pool Sensors


Questions about ProgramMaster? Contact programming@programmaster.org