6th International Congress on 3D Materials Science (3DMS 2022): Additive Manufacturing I: Process Control and Monitoring
Program Organizers: Dorte Juul Jensen, Technical University of Denmark; Marie Charpagne, University of Illinois; Keith Knipling, Naval Research Laboratory; Klaus-Dieter Liss, University of Wollongong; Matthew Miller, Cornell University; David Rowenhorst, Naval Research Laboratory
Monday 1:20 PM
June 27, 2022
Room: Capitol A
Location: Hyatt Regency Washington on Capitol Hill
Session Chair: Richard Fonda, Naval Research Laboratory
1:20 PM Break
1:50 PM
Heat Transfer Modeling and Microstructure Evolution in Directed Energy Deposition Process for Al-0.5Sc-0.5Si Alloy: Amit Singh1; Yasham Mundada1; Priyanshu Bajaj2; Markus Wilms3; Eric Jägle4; Dierk Raabe2; Amit Arora1; 1Indian Institute of Technology Gandhinagar; 2Max-Planck-Institut für Eisenforschung GmbH; 3Fraunhofer Institute for Laser Technology ILT; 4Universität der Bundeswehr München
The temperature and material flow field are computed using the heat transfer and material flow model for the directed energy deposition process. The solidification parameters, such as thermal gradient and solidification rate are computed along the melt pool fusion line to determine the solidification morphology. The effect of preheat temperature on the solidification morphology is analysed with the developed model. The model predicts columnar morphology for deposition of Al-0.5Sc-0.5Si alloy with room temperature substrate. However, the presence of equiaxed morphology is predicted with increasing substrate preheat temperature. The size of the morphology also varies from bottom to top for varying melt pool size. The solidification morphology predicted using computation agrees with the experimental observations.
2:10 PM
Global Local Modeling of Bead Formation and Geometry in Laser Bed Powder Melting Process Using a Comprehensive Multi-physics Simulation: Faiyaz Ahsan1; Jafar Razmi1; Leila Ladani1; 1Arizona State University
Laser powder bed fusion (LPBF) is a state-of-the-art manufacturing process which involves rapid heating of the powder bed by the laser heat source and subsequent flow of fluid around melt pool. This work aims to assess the impact of various process parameters like laser power, scan speed, layer thickness etc. on the shape and geometry of the solidified bead from melt pool, which dictate the final build property and establish a relation between them to optimize the process, leading towards optimum bead geometry shapes pertaining to improved properties of the final build. The computational model will be validated by comparing the result with experiment. This work also attempts to calculate the convective heat transfer coefficient around melt pool region during LPBF process. Marangoni convection, recoil pressure and buoyancy force will be included to properly simulate the heat transfer coefficient along with non-gaussian beam to model the laser-powder interaction.
2:30 PM
Correlative Microscopy and Microstructural Characterization of Porosity Induced by Contouring in a Selective Laser Melted AA6061 Alloy: Hamidreza T-Sarraf1; Sridhar Niverty1; Arun Singaravelu1; Nikhilesh Chawla1; 1Arizona State University
Selective Laser Melting (SLM) is an excellent near-net shape technique for manufacturing alloys and composites. Improving surface quality and densification is important for the development of SLM. Boundary re-melting or contouring, in which the same slice is scanned twice before recoating powders, offers a solution to improve the surface quality. In this study, we conducted a quantitative correlative study to investigate the effect of contouring on microstructure and porosity of AA6061-RAM2 using X-ray micro-computed tomography, and Electron Backscatter Diffraction. 3D x-ray tomography showed that contouring modifies the surface finish yet induces porosity under the surface. During re-melting, a wider melt pool allows formation of keyholes on the melting tracks and entrapped gas pores around the laser line. In addition, correlative microscopy revealed that ceramic particles were refined due to contouring. The effect of processing on 3D microstructural evolution will be discussed in detail.
2:50 PM Cancelled
Automated Post-processing and Surface Standardization of AM Components at Scale: Konstantin Rybalcenko1; Luis Folgar1; Rory Charlesworth2; Joseph Crabtree1; 1Additive Manufacturing Technologies; 2Additive Manufacturing Technologies Ltd.
Additively Manufactured components exhibit high variability of their surface topography, bringing the need for fast and effective surface control measurements. Automatic control of this variability implemented in production chain would enable mass scale production of standardized components. This work presents methodology to handle and post-process Additively Manufactured components at scale: post-processing stages were automated and fast in-line optical system was developed to analyze and quality control the surfaces that are finished at different stages. The technique is already used to manufacture millions of Covid-19 Nasopharyngeal Swabs allowing hundreds of thousands of people get tested and re-join the economy. The talk presents the system, performed tests and discusses its further possibilities.
3:10 PM Break
3:40 PM
4D Nanoscale Imaging of Powder Feedstock Processing for Additive Manufacturing: Stephen Kelly1; Hrishi Bale1; Jordan Kone1; Kyle Tsaknopoulos2; Danielle Cote2; 1Carl Zeiss Microscopy Inc.; 2Worcester Polytechnic Institute
Additive manufacturing holds the promise to produce geometrically complex parts which are not achievable by conventional manufacturing techniques. As with subtractive manufacturing processes, changes to the constitutive materials can have profound effects on the process and the performance of the part. In additive manufacturing, each process along the way from powder to part must be optimized to ensure ultimate success. For example, the microstructure of feedstock powder can influence how the manufacturing process proceeds.We present results from examining how the microstructure of a AM feedstock particle changes after heat treatment. By leveraging the non-destructive nature of laboratory x-ray nanotomography, we imaged the internal microstructure of a single(~40 µm) powder particle evolving as it was annealed from its pristine state. Correlative analysis using FIB-SEM + EDS verify the chemical nature of the developing inclusions. We will discuss the implications of this analysis towards powder performance in a cold-spray manufacturing process.
4:00 PM
Machine Learning Framework for Spiking Defect Detection in Electron Beam Welding: Sanjib Jaypuria1; Bondada Venkatasainath1; Santosh Gupta1; Dilip Kumar Pratihar1; Debalay Chakrabarti1; 1IIT Kharagpur
Although electron beam joints are known for the large aspect ratio, the inherent spiking defects in the weld is inevitable. Spiking is one of 3D weld defect and it is characterized as non-uniform penetration along the welding direction. The fluctuation in penetration in these partial penetration weld act as stress raiser and becomes prone site of cracking. Therefore, industries need a trade-off between penetration and spiking to accept the joint with acceptable penetration and spiking level. In this study, machine learning based unsupervised clustering approaches are used to classify the acceptable and unacceptable joints. Fuzzy c-mean clustering (FCM) and density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms have been used for the classification of these joints. Comparison of these approaches are done through Silhouette coefficient (SC), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI). Fuzzy c-mean clustering is found to be more accurate in classification and gives necessary information about the process variables of electron beam welding.