Title
Optimization of Milling Parameters to Minimise Surface Roughness for Laser Processing Based on Digital Technologies and 3D Analysis
Authors
[ 1 ] Instytut Technologii Mechanicznej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2025
Chapter type
chapter in monograph / paper
Publication language
english
Abstract
EN This study investigates the optimization of milling parameters for minimizing the surface roughness of Ti-6Al-4V titanium before laser texturing, which is essential for achieving uniform laser-induced periodic surface structures (LIPSS). The research emphasizes the significance of optimizing milling parameters, including the cutting-edge rounding radius, feed per tooth, and cutting speed, to achieve minimal surface roughness, significantly impacting the effectiveness of subsequent laser processing. Optimal milling conditions were determined using gradient descent and the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method, implemented through the SciPy library. These methods precisely identify optimal cutting parameters for minimizing the average roughness parameter ( ). Numerical simulations showed that the optimal cutting-edge radius and feed rate lead to a minimum roughness of approximately ≈ 0.25 µm. Experimental results confirm that milling parameters, particularly the cutting edge radius, feed per tooth, and cutting speed, significantly affect the titanium surface microtopography. Optimizing these parameters substantially reduces surface roughness, crucial for subsequent laser processing. The optimization of milling conditions leads to a surface with minimal roughness, promoting uniform laser energy distribution, enhancing final surface quality, and improving functional properties. The study also highlights the importance of technological inheritance, as surface defects introduced during milling can negatively affect laser texturing results. Therefore, minimizing roughness during milling is crucial for high-quality laser texturing. Further research is needed to refine predictive models and improve real-time adaptive control to enhance process stability and efficiency in industrial applications.
Date of online publication
11.06.2025
Pages (from - to)
182 - 192
Ministry points / chapter
20
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