Ultrasonic-assisted milling of advanced titanium alloys

Zhang, Yiming (2025). Ultrasonic-assisted milling of advanced titanium alloys. University of Birmingham. Ph.D.

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Abstract

Titanium alloys, known for their outstanding mechanical properties, are highly utilised in crossgeneration aeroengine applications. Amongst these alloys, γ-TiAl has been developed as a potential substitute for nickel-based superalloys, specifically components used in the hot section of gas turbine engines due to its superior lightweight characteristics. However, the extremely poor ductility of γ-TiAl at room temperature presents a major bottleneck, restricting its broader engineering applications. Researchers have extensively explored the machinability of γ-TiAl, with most investigations concentrating on conventional processing methods. The primary challenges in cutting γ-TiAl include severe tool wear/low tool life, high cutting forces, and poor machined surface integrity. Therefore, further research is essential to develop innovative methods for the efficient and high-quality processing of γ-TiAl. One promising approach is ultrasonic-assisted cutting, a hybrid manufacturing process well-suited for hard and brittle materials. However, research on ultrasonic-assisted cutting of γ-TiAl remains scarce, while theoretical investigations into ball-end ultrasonic-assisted milling, particularly in developing models for cutting force and tool wear, require more in-depth study.
Following a comprehensive literature review covering the properties of aerospace metallic materials, machinability, tool wear and failure, workpiece surface integrity, ultrasonic-assisted cutting technology and theoretical modelling, particularly for milling titanium alloys, two principal phases of experimental work (Phase 1 and 2) were conducted. These were aimed to investigate the performance of the ultrasonic system, influence of cutting parameters on machinability of γ-TiAl, associated tool wear mechanisms/failure and the effect of ultrasonic vibration on cutting γ-TiAl. Phase 1 involved preliminary trials to evaluate the stability of the integrated ultrasonic system and identify significant cutting parameters. Initially, the influence of tool parameters on the ultrasonic system was systematically studied to determine the ideal tool size and clamping method for achieving the maximum vibration amplitude. The results indicated that lightweight tools with small size or low-weight materials consistently achieved the largest amplitude when the overhang was about 70% of the overall tool length. For example, an amplitude of 7.6 μm was recorded for high-speed steel (HSS) tools while tungsten carbide (WC) cutters of the same size and geometry only reached 3.5 μm. Similarly, amplitudes of up to 8.0 μm were produced using Ø4 mm (12.13 g) tools compared to 2.4 μm with Ø6 mm (25.96 g) tools. The existence of an operating threshold for the ultrasonic system was also discovered, as when cutting forces exceeded a critical value of approximately 90 N, the ultrasonic actuation of the tool was effectively suppressed. Subsequently, the influence of cutting parameters on machinability of γ-TiAl following conventional/ball-end ultrasonic-assisted milling was investigated in terms of cutting force, tool wear and machined workpiece surface integrity using a fractional factorial Taguchi experimental design. In general, higher cutting speeds, smaller depths of cut, lower feed rates and dry cutting environment resulted in longer tool life and better machining quality.
Based on the conclusions from Phase 1 trials, Phase 2 tests involved a more in-depth investigation of tool wear mechanisms/failure and machinability of γ-TiAl via a full factorial comparative experiment. Four primary wear mechanisms were identified during the milling of γ-TiAl including abrasion, adhesion, oxidation and diffusion wear. Under wet cutting conditions, the TiAlN coating was prevented from fully interacting oxygen in the air, with the large temperature gradient in the cutting zone leading to coating failure. As a result, abrasive wear dominated when machining with cutting fluid, while adhesive wear was the primary wear mechanism in dry cutting when using TiAlN coated tools. Additionally, the high aluminium and titanium content in γ-TiAl induced a protective film on the tool surface during dry machining. The comparative experimental results showed that ultrasonic-assisted milling significantly improved tool life by 15.9% and reduced cutting forces by up to 47.2% compared to conventional operation in the first 100 m distance machined. Surface defects were noticeably reduced while the edge burr at the tool exit position was alleviated. The high-frequency energy from ultrasonic vibration resulted in strain hardening, increased surface microhardness, and high surface compressive residual stress. These effects suppressed subsurface cracks and likely improved fatigue life.
Phase 3 work focused on the theoretical/analytical evaluation of ball-end ultrasonic-assisted milling. A modified surface morphology model was initially developed based on the kinematics of ball-end ultrasonic-assisted milling using the Z-map method. This was followed by formulation of a static cutting force model that accounted for tool bending deflection, while a tool flank wear prediction model was proposed by extending the pin-on-disc approximation. Validation tests demonstrated that the improved models were capable of predicting workpiece surface morphology, cutting forces and tool flank wear after ball-end ultrasonic-assisted milling with high accuracy and significantly reduced calculation time. For instance, compared with similar methods, the calculation of modified surface morphology simulation was improved by 68%. The maximum error in predicted cutting force was 18.42% while the minimum coefficient of determination for tool flank wear trend was 0.7773. Axial runout was identified as the root cause of multiple/overlapping feed marks on the machined surface, while the effect of increasing tool vibrational amplitude in reducing cutting forces was theoretically verified. Results from the models can be used to provide a basis for selecting optimal/preferred operating parameters in practical applications.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):