Abstract

Binder jetting technology is an additive manufacturing technology in which powder materials are binded together layer by layer forming the product from input CAD model. The process involves printing the product layer by layer, curing and sintering. The mechanical properties of 3D printed samples varies based on process parameters, hence there is a need to tune the process parameters for optimal characteristics. Three main parameters namely layer thickness, sintering time and sintering temperature were identified and the study focuses on the effect of parameters on dimensional accuracy and compressive strength of the samples. Full factorial experimental approach was used to conduct the experiments and analysis of variance was performed to determine the significance of parameters. Along with parameters optimization, feed forward back propagation artificial neural network model is developed to quantify the relationship between three parameters and compressive strength, the model is developed based on experimental data and validated with known data. Also, Compressive behavior of four lattice designs considered in the study were simulated by finite element analysis and numerical results were compared with experimental data in order to validate the finite element model. FE models of different lattice designs were developed from experimental test data using ANSYS and the simulated compressive behavior is compared to that experimental compression test results.

Advisor

Shaobiao Cai

First Committee Member

Jin Park

Second Committee Member

Kuldeep Agarwal

Date of Degree

2017

Language

english

Document Type

Thesis

Degree

Master of Science (MS)

Department

Mechanical and Civil Engineering

College

Science, Engineering and Technology

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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