Experimental and Machine Learning Approaches to Comprehending Surface Characteristics and Mechanical Properties of Direct Metal Laser Sintering-Hybrid Milling Manufactured Maraging Steel

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Experimental and Machine Learning Approaches to Comprehending Surface Characteristics and Mechanical Properties of Direct Metal Laser Sintering-Hybrid Milling Manufactured Maraging Steel

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2023

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Additive manufacturing refers to a novel group of manufacturing methods where the manufacturing process takes place by adding material gradually to build the final part instead of removing material which is done in conventional subtractive manufacturing techniques. Additive manufacturing offers significant advantages- enhanced design freedom and reduced material waste with the potential to change the paradigm of the manufacturing industry. Direct metal laser sintering (DMLS) is an additive manufacturing method where 3D printing of metal parts is conducted layer by layer from metallic powders. DMLS-hybrid milling is a new addition to additive manufacturing methods. The synergistic approach of additive and subtractive manufacturing methods offers benefits from both manufacturing paradigms. The process parameters- laser power, print speed, layer thickness, hatch distance, etc. significantly influence the quality of the final part. Hence, the process parameters are to be optimized to obtain the desired result. This work focuses on the optimization of process parameters for superior surface characteristics and mechanical properties of heat-treated maraging steel manufactured by the DMLS-hybrid milling method. Experimental approaches were adopted, and statistical analysis was performed to evaluate the performance of maraging steel parts printed at multiple process conditions guided by Taguchi L9 orthogonal array of experiment design. Significant improvement was observed in surface quality by the adoption of milling operation during printing, and mechanical properties were improved by the heat-treatment process. Moreover, machine learning was implemented to predict mechanical properties based on process parameters. This study creates the framework for future development of the Multiphysics model of the DMLS method and data-driven efficient methods for metal additive manufacturing.

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University of Minnesota M.S.M.E. thesis. Summer 2023. Major: Mechanical Engineering. Advisor: Emmanuel Enemuoh. 1 computer file (PDF); viii, 70 pages.

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Sakib, Tanvir. (2023). Experimental and Machine Learning Approaches to Comprehending Surface Characteristics and Mechanical Properties of Direct Metal Laser Sintering-Hybrid Milling Manufactured Maraging Steel. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/258603.

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