Browse by author
Lookup NU author(s): Dr Wanqun Chen, Xiangyu Teng, Dr Dehong Huo
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Micro milling, as a versatile micro machining process, is kinematically similar to conventional milling, however, it is significantly different from conventional milling with respect to chip formation mechanisms, uncut chip thickness modelling, due to the comparable size of the edge radius to chip thickness, and the small per tooth feeding. Considering tool run-out and dynamic displacement between the tool and workpiece, the contour of the workpiece left by previous tool paths are typically in a wavy form, and the wavy surface provides a feedback mechanism to cutting forces generation because the instantaneous uncut chip thickness changes with both the vibration during the current tool path and the surface left by the previous tool paths. In this study, a more accurate uncut chip thickness model was established including the precise trochoidal trajectory of the cutting edge, tool run-out and dynamic modulation caused by the machine tool system vibration. The dynamic regenerative effect is taken into account by considering the influence of all the previous cutting trajectories using numerical iteration, thus the multiple-time-delays (MTD) is considered in this model. It is found that transient separation of the tool-workpiece occurs at low feed per tooth, caused by MTD and the existing cutting force models, are no longer applicable when transient tool-workpiece separation occurs. Based on the proposed uncut chip thickness model, an improved cutting force model of micro milling is developed by full consideration of the ploughing effect and elastic recovery of the workpiece material. The proposed cutting force model is verified by micro end milling experiments and the results show that the proposed model is capable of producing more accurate cutting force prediction than other existing models, particularly at small feed per tooth.
Author(s): Chen W, Teng X, Huo D, Wang Q
Publication type: Article
Publication status: Published
Journal: International Journal of Advanced Manufacturing Technology
Year: 2017
Volume: 93
Issue: 9-12
Pages: 3005-3016
Print publication date: 01/12/2017
Online publication date: 12/07/2017
Acceptance date: 22/06/2017
Date deposited: 05/07/2017
ISSN (print): 0268-3768
ISSN (electronic): 1433-3015
Publisher: Springer
URL: https://doi.org/10.1007/s00170-017-0706-2
DOI: 10.1007/s00170-017-0706-2
Altmetrics provided by Altmetric
