MODELLING AND OPTIMISATION OF MILLING PROCESS ON THIN-WALLED TI64 UNDER CARBON DIOXIDE CRYOGENIC USING RESPONSE SURFACE METHODOLOGY AND GENETIC ALGORITHM

BUDIMAN, ARIE YUDHA and Mohruni, Amrifan Saladin and Sharif, Safian (2025) MODELLING AND OPTIMISATION OF MILLING PROCESS ON THIN-WALLED TI64 UNDER CARBON DIOXIDE CRYOGENIC USING RESPONSE SURFACE METHODOLOGY AND GENETIC ALGORITHM. Doctoral thesis, Sriwijaya University.

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Abstract

This dissertation presents an in-depth investigation into the cryogenic machining performance of thin-walled Ti6Al4V (Ti64) alloy using carbon dioxide (CO₂) as a sustainable cooling medium. The research aims to improve surface quality while minimizing environmental impact by replacing conventional cutting fluids with cryogenic CO₂. Both uncoated and AlCrN-coated carbide end mills were used to assess the influence of key machining parameters including cutting speed (Vc), feed rate (fz), radial depth of cut (ar), and axial depth of cut (ax) on surface roughness (Ra). A Central Composite Design (CCD) with 30 experimental runs was implemented, and surface roughness was measured at multiple axial depths on each workpiece. The optimization and modeling were carried out using Response Surface Methodology (RSM) and Genetic Algorithm (GA), with the aim of predicting optimal cutting conditions and minimizing Ra. For uncoated tools, the RSM model predicted a minimum surface roughness of 0.158 μm, while GA slightly improved it to 0.1568 μm after 64 generations. In contrast, coated tools significantly enhanced machining performance, producing a minimum Ra of 0.132 μm through RSM and further reduced to 0.12725 μm by GA within 96 generations, representing an 18.8% improvement over uncoated tools. The feed rate was found to be the most influential factor in both tool conditions, while cutting speed also contributed positively to surface quality. Although RSM models yielded higher predictive accuracy based on lower Mean Square Error (MSE), GA consistently produced lower Ra values, demonstrating superior global optimization capabilities. The application of AlCrN coating also helped suppress tool wear and thermal degradation, contributing to smoother surface finishes and greater process stability. Overall, the combination of CO₂ cryogenic cooling, advanced tool coating, and hybrid optimization techniques (RSM-GA) proves to be highly effective for the precision machining of thin-walled Ti64 alloy. This approach not only enhances surface integrity and tool life but also supports the broader goal of sustainable manufacturing by reducing dependence on traditional oil-based coolants. The findings offer valuable insights for machining difficult-to-cut aerospace materials and provide a foundation for integrating green technologies into high-performance manufacturing systems. This research contributes to the evolving field of intelligent, eco-friendly machining and sets the stage for further exploration of cryogenic processes in advanced material applications.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Titanium Alloy, Thin-Walled, Surface Roughness, Cryogenic, RSM, GA
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery T Technology > TJ Mechanical engineering and machinery > TJ1040-1119 Machinery exclusive of prime movers > J1075.B48 Lubrication and lubricants.
Divisions:	03-Faculty of Engineering > 21001-Engineering Science (S3)
Depositing User:	Arie Yudha Budiman
Date Deposited:	21 Aug 2025 06:45
Last Modified:	21 Aug 2025 06:45
URI:	http://repository.unsri.ac.id/id/eprint/183115

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