Optimization of FDM Processing Parameters
Fused Deposition Modeling fabricates inferior parts compared to conventional manufacturing methods such as injection molding or thermoforming.
Optimizing the processing parameters is challenging due to their large number and their complex interactions.
Different quality attributes require different sets of processing parameters.
Using Design of Experiments discipline combined with data driven models a predictive model is developed.
Multi-objective optimization was enabled.
95% accuracy was achieved using an ensemble of artificial neural networks.
Preliminary finite element model was proposed and verified experimentally.
Experimental Optimization 2017, FDM Taguchi 2018, FDM RSM 2020
Developing Post Processing Treatments to Improve the Performance of LPBF Parts
Metallic additively manufactured parts suffer from defects and high anisotropy, which can render them inferior to conventionally manufactured parts such as forged and cast parts.
This project aims at developing post processing heat treatments for additively manufactured Inconel 718 and 15-5PH.
The goal of the heat treatments is to homogenize the microstructure and reduce the anisotropy and the severity of the defects.
Tensile testing, SEM microscopy, fractography, X-ray diffraction, and Rietveld refinement characterization techniques were used to analyze the influence of the heat treatment.
Developed heat treatment succeeded in reducing the anisotropy and improved the reliability of the additively manufactured Inconel 718 and 15-5PH steel.
Microstructure and mechanical 2019
Characterizing the Mechanical Behavior and Microstructure of As-Built LPBF parts
This project aimed at characterizing the microstructure and mechanical behavior of two of the most common additively manufactured alloys, Inconel 718 and 15-5PH, in their as-built condition at room temperature and elevated temperatures.
Specimens were fabricated in different orientations.
Tensile testing, SEM microscopy, and fractography characterization techniques were implemented.
Preliminary Bayesian inference model was implemented to predict the tensile strength for additively manufactured 15-5PH fabricated in different orientations and at different environmental temperatures.
Effect of Man 2018, Prediction of Mech 2019
Hybrid physics and data driven modeling of post processed AM parts mechanical behavior
Previous work showed that additively manufactured parts do not behave similar to conventionally manufactured parts.
In this project a hybrid approach is adopted to model the effect of post processing heat treatments on additively manufactured Inconel 718.
Grain growth, phase transformation, precipitation, and other thermally activated mechanisms are considered.
Modified Heat Treatments of DMLS Of Precipitate Hardenable Steel
Investigating the Effect of Fused Deposition Modeling Processing Parameters Using Taguchi Design of Experiment Method
Influence of High Temperature on Fused Deposition Modelling Parts
Influence of Processing Parameters on Fused Deposition Modeling