Abstract

Additive manufacturing (AM) is a revolutionary technology that greatly improves the flexibility of fabricating parts with complex structures and eliminates the cost of making molds. While AM techniques offer unique benefits over traditional manufacturing processes, it is challenging to predict the mechanical behaviors of additively manufactured parts based on design and process parameters. With recent advances in machine learning, data-driven methods have the potential to overcome such limitations. In this work, data-driven modeling frameworks were proposed to predict the tensile, flexural, and compressive behaviors of additively manufactured plastics and composites. Ensemble learning was used to predict the tensile strength of polylactic acid (PLA) with cooperative AM process parameters. A 12.97% mean absolute percentage error (MAPE) was achieved by combining lasso, support vector regression, and extreme gradient boosting in the computational framework. An enhanced ensemble learning method that combines eight different machine learning algorithms was introduced to predict the flexural strength of continuous carbon fiber and short carbon fiber reinforced nylon (CCF-SCFRN) composites with design parameters. Learned knowledge from CCF-SCFRN composites was transferred to continuous glass fiber and short carbon fiber reinforced nylon (CGF-SCFRN) composites for flexural stress-strain curve prediction using an optimal transport (OT) integrated transfer learning framework. Compared with traditional transfer learning, the OT-integrated framework improves the stress-strain curve prediction accuracy by 10.46% in terms of MAPE. The transfer learning framework was further demonstrated in predicting the compressive stress-strain curves of PLA scaffolds with both AM process and design parameters. Three cases were studied by selecting different parameters for domain transfer to validate the generalizability of the proposed framework in predicting mechanical behaviors of additively manufactured materials with limited data.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2022

Semester

Fall

Advisor

Wu, Dazhong

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Mechanical Engineering

Format

application/pdf

Identifier

CFE0009425; DP0027148

URL

https://purls.library.ucf.edu/go/DP0027148

Language

English

Release Date

December 2023

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

Download

Included in

Manufacturing Commons

Share

COinS