Application of a Variable Path Length Repetitive Process Control for Direct Energy Deposition of Thin-Walled Structures

Direct Energy Deposition (DED) Additive Manufacturing is Well Suited to Fabricating Large Thin-Walled Metal Structures Such as Rocket Nozzles but Suffers from Layer-To-Layer Defect Propagation. Propagating Defects May Exhibit as Slumping or a Ripple in Bead Geometry. Recent Works Have Used Repetitive Process Control (RPC) Methods for Additive Manufacturing to Stabilize the Layer-Wise Defect Propagation, But These Methods Require Repetition of the Same Path. However, Typical Thin-Wall DED Applications, Sometimes Referred to as Vase Structures, Have Changing Paths with Each Layer Such as Expanding or Contracting Diameters and Changing Profiles. This Paper Presents an Extension to Optimal RPC that Uses a Geometric Mapping Method in the Learning Algorithm to Project Previous Layer Defects onto the Current Layer, Even When Paths Are of Differing Profile and Length. the Novel Method is Implemented on a DED System and Sample Parts with Layer-Changing Geometry Are Printed. the Experimental Results Demonstrate that the Method is Capable of Stabilizing the Layer-To-Layer Ripple Instability and Producing Parts of Good Quality

Design, fabrication, and characterization of functionally graded materials

“The aim of this research was to investigate the feasibility of fabricating custom designed, graded materials using Laser Metal Deposition (LMD) that will cater for functionality and unconventional repair. The ultimate goal of the project is to establish the versatility of LMD for fabricating advanced materials and tackling problems that have been conventionally difficult or in cases infeasible. In order to accomplish these goals, this research involved investigations into, the feasibility of using elemental powders as modular feedstocks, the feasibility of fabricating tailored gradients with these custom compositions, and finally leveraging the advantages of grading materials using LMD to successfully fabricate conventionally infeasible material systems. The copper-nickel material system was chosen for demonstrating the modular feedstock concept. While the use of elemental nickel lead to porosity issues, Delero-22 a nickel-silicon-boron alloy was identified as a viable substitute. A wide range of copper-nickel alloys were fabricated through the deposition of blended powder feedstocks. Also, using these blended powder feedstocks, graded material structures of copper-nickel alloys were successfully fabricated. Varying energy input through pulse width modulation of laser power was identified as a viable means for manipulating chemistry gradient within these graded materials. The influence of varying chemistry on mechanical properties was evaluated through the use of DIC coupled mini-tensile testing. A clear distinction in the strain field indicating the spatially varying chemistry was identified during tensile testing. Also, the feasibility of depositing on highly reflective alloys of aluminum such as Al2024 and Al6061 was also investigated. Leveraging the higher absorptivity of Al4047 and remelting during LMD, a strong metallurgical bond was obtained between the substrate and the deposit. Preheating the substrate was identified to increase the reliability and quality of deposition. The bond between the substrate and deposit was found to be stronger than the deposit --Abstract, page iv

Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser's Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. The present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.Comment: 28 pages, 18 figures, 4 table

EXPLORING THE USE OF PULSED ERBIUM LASERS TO RETRIEVE A ZIRCONIA CROWN FROM A ZIRCONIA IMPLANT ABUTMENT

Removal of zirconia crowns, commonly used as cement-retained implant fixed restorations, can be challenging. Conventional methods of crown removal are time consuming and often leave irreparable damage to the crown, which can be costly to patients and practitioners. This research explored the use of two different types of pulsed erbium lasers, erbium-doped yttrium aluminum garnet laser (Er:YAG) and erbium, chromium-doped yttrium, scandium, gallium and garnet (Er,Cr:YSGG), as non-invasive tools to retrieve cemented zirconia crowns from zirconia implant abutments. Times needed to remove the crowns were recorded and analyzed using ANOVA (��=0.05). No statistical differences between the debond times of each laser were observed. The surfaces of the crown and the abutment were further examined using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) examination. SEM and EDS examinations of the materials showed no visual surface damaging or material alteration from the two pulsed erbium lasers