Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/13738
Title: | Laser Surface Treatment Optimization of 1.2379 (Aisi D2) Tool Steel | Authors: | Ozbey, Sayit Artem, Hatice Secil |
Keywords: | Fiber Laser Laser Surface Treatment 1.2379 Tool Steel Hardness Roughness Stochastic Optimization Neuro-Regression |
Publisher: | Springernature | Abstract: | Industrial applications require materials with specific surface quality and hardness properties. Laser surface treatment stands out as a cost-effective and effective method that improves surface performance by changing the structural and physical properties of the material. 1.2379 cold work tool steel is a commonly used material in die and mold industries for injection mold inserts; therefore, the surface properties of the material play a significant role. In this study, it is aimed to optimize laser parameters; the laser power, pulse duration, repetition rate and line spacing for the responses such as hardness and surface roughness. For this purpose, 1.2379 cold work tool surfaces were treated using a commercially available industrial ytterbium low-power pulsed fiber laser experimentally. Experiments were conducted based on 34 full factorials. Vickers hardness and micro-roughness measurements were performed on the laser-treated surfaces. Regression models were developed using experimental data and the appropriate models were selected for each response. The response variables were then optimized based on stochastic optimization methods: Nelder-Mead, Differential Evolution, Random Search and Simulated Annealing. The results indicate that a maximum hardness of 495 HV0.5 and a minimum surface roughness of 0.277 mu m were achieved, corresponding to a 61% increase and a 43% decrease, respectively, compared to the base metal. | URI: | https://doi.org/10.1007/s40516-025-00297-6 | ISSN: | 2196-7229 2196-7237 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Description | Size | Format | |
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10554957.pdf | Master Thesis | 3.37 MB | Adobe PDF | View/Open |
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