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The effect of machining toolpath on surface roughness and dimensional accuracy for high-speed micro milling

Lookup NU author(s): Amin Dadgari, Dr Dehong Huo, Dr David Swailes


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This paper investigates different machining toolpath strategies on machining efficiency and accuracy in the micro milling of linear and circular micro geometric features. Although micro milling includes many characteristics of the conventional machining process, detrimental size effect in downscaling of the process can lead to excessive tool wear and machining instability, which would, in turn, affects the geometrical accuracy and surface roughness. Most of the research in micro milling reported in literature focused on optimising specific machining parameters, such as feed rate and depth of cut, to achieve lower cutting force, better surface roughness, and higher material removal rate. However, there was little attention given to the suitability and effect of machining tool path strategies. In this research, a tool path optimisation method with respect to surface roughness and dimensional accuracy is proposed and tested experimentally. Various toolpath strategies, including lace(0°), lace(45°), lace(90°), concentric and waveform in producing linear and circular micro geometric features were compared and analysed. Experimental results show that the most common used strategies lace(0°) and concentric reported in the literature have provided the least satisfactory machining performance, while waveform toolpath provides the best balance of machining performance for both linear and circular geometries. Hence, at process planning stage it is critical to assign a suitable machining toolpath strategy to geometries accordingly. The paper concludes that an optimal choice of machining strategies in process planning is as important as balancing machining parameters to achieve desired machining performance.

Publication metadata

Author(s): Dadgari A, Huo D, Swailes D

Publication type: Article

Publication status: Published

Journal: Solid State Phenomena

Year: 2017

Volume: 261

Pages: 69-76

Online publication date: 21/08/2017

Acceptance date: 05/08/2017

ISSN (print): 1662-9779

Publisher: Trans Tech Publications Ltd


DOI: 10.4028/


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