2023
DOI: 10.1177/09544054221147608
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Machinability analysis of micro-milling thin-walled Ti-6Al-4V micro parts under dry, lubrication, and chatter mitigation conditions

Abstract: Micro-milling technology is an efficient method for machining titanium alloy thin-walled micro parts. However, the machinability of thin-walled microstructures can be greatly affected by chatters due to their weak stiffness, especially in difficult-to-machine materials like titanium alloys. In order to obtain high-quality thin-walled micro parts, a chatter mitigation strategy is presented and its mechanism is analyzed in this paper. The titanium alloy workpiece is submerged into a kind of high-viscosity fluid,… Show more

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Cited by 3 publications
(3 citation statements)
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“…Consequently, this leads to an augmentation in dimensional error. The reasons for small deformation errors are as follows: The micro-milling force is relatively small, and previous studies have shown that the average cutting force for the micro-milling of titanium alloys is within 5 N [31]; the positioning accuracy of the KERN EVO machining center is 1 µm, with a repeatability accuracy of ± 0.5 µm, exhibiting excellent machining precision; and Ti-6Al-4V possesses high strength, and under the influence of relatively small cutting forces, noticeable deformation errors are not likely to occur.…”
Section: Resultsmentioning
confidence: 99%
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“…Consequently, this leads to an augmentation in dimensional error. The reasons for small deformation errors are as follows: The micro-milling force is relatively small, and previous studies have shown that the average cutting force for the micro-milling of titanium alloys is within 5 N [31]; the positioning accuracy of the KERN EVO machining center is 1 µm, with a repeatability accuracy of ± 0.5 µm, exhibiting excellent machining precision; and Ti-6Al-4V possesses high strength, and under the influence of relatively small cutting forces, noticeable deformation errors are not likely to occur.…”
Section: Resultsmentioning
confidence: 99%
“…To retain and interpret the original variable information as much as possible, principal component analysis (PCA) was applied to the three indicators representing the machining quality. First, the standardization of Sa, Ds, and De was performed according to Equation (31). In order to overcome the problem of intelligent optimization algorithms easily getting trapped in local optima, this paper investigated and compared the optimal combination of process parameters using three evolutionary algorithms.…”
Section: Resultsmentioning
confidence: 99%
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