Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/28768
Title: | An AI-Based Optimization Framework for Optimal Composition and Thermomechanical Processing Schedule for Specialized Micro-alloyed Multiphase Steels |
Authors: | Kafuko, Martha |
Advisor: | Srinivasan, Seshasai |
Department: | Mechanical Engineering |
Keywords: | Genetic algorithm;Artificial Neural Network;Steel optimization;Thermomechanical Processing;Cost optimization of multiphase steel;Mechanical properties prediction |
Publication Date: | 2023 |
Abstract: | Steel is an important engineering material used in a variety of applications due to its mechanical properties and durability. With increasing demand for higher performance, complex structures, and the need for cost reduction within manufacturing processes, there are numerous challenges with traditional steel design options and production methods with manufacturing cost being the most significant. In this research, this challenge is addressed by developing a micro-genetic algorithm to minimize the manufacturing cost while designing steel with the desired mechanical properties. The algorithm was integrated with machine learning models to predict the mechanical properties and microstructure for the generated alloys based on their chemical compositions and heat treatment conditions. Through this, it was demonstrated that new steel alloys with specific mechanical property targets could be generated at an optimal cost. The research’s contribution lies in the development of a different approach to optimize steel production that combines the advantages of machine learning and evolutionary algorithms while increasing the number of input parameters. Additionally, it uses a small dataset illustrating that it can be used in applications where data is lacking. This approach has significant implications for the steel industry as it provides a more efficient way to design and produce new steel alloys. It also contributes to the overall material science field by demonstrating its ability in a material’s design and optimization. Overall, the proposed framework highlights the potential of utilizing machine learning and evolutionary algorithms in material design and optimization. |
URI: | http://hdl.handle.net/11375/28768 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Kafuko_Martha_2023July_MASc.pdf | 3.28 MB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.