Experimental study on cutting factors of multi-layer machining for large aerospace thin-walled structural parts

Yu, Hangzhuo; Zhong, Han; Chen, Yong; Lin, Lei; Shi, Jingping; Yue, Tao; Shen, Yong

doi:10.1177/09544089211040303

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Agarwal and Desai 17 utilised a mechanistic cutting force model to estimate the induced cylindricity error during end milling of thin-walled circular components using finite element based framework and particle swarm optimisation algorithm. Yu et al 18 reported an experimental method based on multi-layer machining for obtaining optimum cutting parameters based on analysing the machining error. The development of comprehensive predictive and experimental methods that consider the structural deflections of the cutting tool/workpiece and the chatter instability requires further research effort to address the milling error challenge as reported in.…”

Section: Introductionmentioning

confidence: 99%

Machining error prediction scheme aided smart fixture development in machining of a Ti6Al4V slender part

Liu

Afazov

Becker

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Machining of slender (low rigidity) parts is associated with tool/workpiece deflections due to induced cutting forces resulting in machining error (dimensional inaccuracy of the machined surface). The development of smart fixtures is seen as an enabler for reduction of machining error. To reduce lead times, the smart fixtures need to be designed in an efficient way by using more virtual simulations and less physical iterations. This paper presents the development of a novel methodology for machining error prediction in milling of a fixture-workpiece system. The methodology integrates a cutting force model, a finite element based fixture-workpiece system and a multi-step error predictive approach. The methodology was first validated on a flexible thin-wall Ti6Al4V slender part where less than 6% difference was achieved between predicted and measured machining error. The difference between predicted and measured cutting forces was approximately 6%. After the gained confidence, the methodology was applied to the flexible thin-wall Ti6Al4V slender part encompassed by a fixture with three actuators acting as supports. The predicted machining error was reduced from the range of 0.2–0.33 mm (no actuators) to the range of 0.12–0.14 mm (with three actuators). This demonstrated the capability of the developed methodology to aid the design of future smart fixtures with the potential to reduce lead times during their development.

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Section: Introductionmentioning

confidence: 99%