Enhancing orthogonal finishing machining of Ti6Al4V with laser-ablated tool geometry modifications
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Date
2024-12
Publication Type
Journal Article
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yes
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Abstract
Finishing machining of Ti6Al4V, known for its high strength and heat conduction resistance, demands optimisation to achieve high-quality end products. This study explores modifying the chip contact length on the rake face and altering the flank face with a cavity to minimise process forces and temperatures while maintaining cutting edge integrity. The research validates the manufacturability of ultra-short pulsed laser-ablated tool geometry modifications, indicating potential for industrial scale-up. Extensive experimental evaluations under dry conditions assess the impact of tool modifications at various feed rates for planing and turning. Significant reductions in process forces and temperatures were observed with rake face modifications, particularly at a cavity distance of approximately 34 mu m. Ideal performance was noted for feed rates between 0.035 and 0.045 mm for planing and 0.040 to 0.045 mm/rev for turning. Smoothed Particle Hydrodynamics (SPH) simulations employing a Johnson-Cook material model were used to analyse chip formation and to predict the process forces. These simulations revealed a clear change in the chip formation and lower process forces and temperatures. The SPH results closely matched experimental outcomes, with a discrepancy of less than 7 % in cutting forces for both tool types, although feed forces were underestimated by about 50 %. The effect of the tool modification is reflected accurately at the respective feeds.
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Publication status
published
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Book title
Volume
135
Pages / Article No.
3317 - 3341
Publisher
Springer
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Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Ti6AI4V cutting; Laser modification; Laser micromachining; Micro-geometry; SPH simulation
Organisational unit
09473 - Mohr, Dirk / Mohr, Dirk
Notes
Funding
149436 - GPU-Enhanced Metal Cutting Simulation using Advanced Meshfree Methods (SNF)
