Experimental contribution to the study of the Ti6Al4V chip formation in orthogonal cutting on a milling machine
Source abstract
Abstract
Machining by removing chips with a cutting tool is a complex operation involving many phenomena. Ti6Al4V chip formation, and particularly when it is saw-toothed (or segmented), still needs to be investigated numerically and experimentally to understand the mechanisms it involves. The use of numerical modeling has been increasingly growing since the last decade but experiments remain essential to validate the models. These two approaches are complementary and closely linked. Experiments are, however, time consuming and often difficult to implement. Moreover, orthogonal cutting as a method should be employed as to compare with the modelings. This paper presents a novel setup to perform strictly orthogonal cutting experiments with a milling machine at cutting speeds of up to 30 m/min. The configuration adopted does not require the machine tool to be modified and the small dimensions of the sample limit the costs linked to the workpiece material. The experiments, performed with the titanium alloy Ti6Al4V, constitute the basis of a benchmark for numerical orthogonal cutting validation based on chip morphology (without chip distortion due to unrolling), mechanism of chip formation, cutting forces and teeth formation frequency. The experimental results of this study highlight that the formation of a saw-toothed chip is due to the deformation and the propagation of a crack inside the primary shear zone for the cutting conditions of this paper. They also show that there is no material between the teeth on the lateral faces of the chip and that the FFT of the roughness of the machined surface is a good estimator for the teeth formation frequency.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced machining processes and optimization · Advanced Surface Polishing Techniques · Tunneling and Rock Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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