Date of Award

10-19-2015

Publication Type

Master Thesis

Degree Name

M.A.Sc.

Department

Mechanical, Automotive, and Materials Engineering

First Advisor

Johrendt, Jennifer

Rights

info:eu-repo/semantics/openAccess

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.

Abstract

Carbon fiber-reinforced composite material properties can be directly related to the manufacturing process. No generally accepted model or system exists that can model the relationship between manufacturing process parameters and composite material properties. The purpose of this research is to develop an artificial neural network model to predict the manufacturing process parameters’ influence on the properties of carbon fiber-reinforced composite material. Different types of artificial neural networks are compared in current research in order to obtain the best prediction results. In this research, the calculated sensitivities from the trained neural network are used to find the effect of processing on material properties. Finally, a complete artificial neural network model for predicting composite material performance manufactured using the LFT-D process was built.

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