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Lookup NU author(s): Amin Dadgari,
Dr Dehong Huo,
Dr David Swailes
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper investigates the effects of machining tool path and cutting layer strategies on machining efficiency and accuracy in micro-milling of linear and circular micro-geometric features. Although micro-milling includes many characteristics of the conventional machining process, detrimental an effect in downscaling the process can be excessive tool wear which could, in turn, increase the machining forces and hence affect the geometrical accuracy and surface roughness. Most of the research in micro milling reported in the literature has focused on optimising machining parameters, such as feed rate and depth of cut to achieve lower cutting forces, better surface roughness, and better machining efficiency. However, is there yet little known about the effect and stability of machining tool paths and cutting layers strategies for the micro-milling process. Various tool path strategy, including lace(0°), lace(45°), lace(90°), concentric and waveform in producing linear and circular micro geometric features were compared and analysed. The effect of various cutting layer strategies in producing thin walled structure was investigated. The optimisation method with respect to surface roughness and dimensional accuracy is proposed for selection of optimum machining strategies experimentally tested. Experimental results show that the most commonly used strategy lace(0°) and concentric, reported in the literature have provided the least satisfactory machining performance, while the waveform strategy provides the best balance of machining performance for both linear and circular geometries. Adopting an optimum sequence of material removal layer in micromachining of thin walls has proven to improve the overall accuracy. This paper concludes that an optimal choice of machining strategies in process planning is as important as balancing machining parameters to achieve desired machining performance.
Author(s): Dadgari A, Huo D, Swailes D
Publication type: Article
Publication status: Published
Journal: International Journal of Robotics and Mechatronics
Online publication date: 30/11/2017
Acceptance date: 04/09/2017
Date deposited: 16/01/2018
ISSN (print): 2288-5889
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