Theoretical modelling and prediction of surface roughness for hybrid additive–subtractive manufacturing processes. (1st February 2019)

• Record Type: Journal Article
• Authors: Li, Lin; Haghighi, Azadeh; Yang, Yiran
• Is Part Of: IISE transactions. Volume 51:Number 2(2019)
• Page Start: 124
• Page End: 135
• Publication Date: 2019-02-01
• Subjects: Additive manufacturing -- hybrid additive–subtractive manufacturing process -- milling process -- surface roughness -- surface profile; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2018.1458268
• Languages: English
• ISSNs: 2472-5854

Abstract

ABSTRACT: Hybrid additive–subtractive manufacturing processes are becoming increasingly popular as a promising solution to overcome the current limitations of Additive Manufacturing (AM) technology and improve the dimensional accuracy and surface quality of parts. Surface roughness, as one of the most important surface quality measures, plays a key role in the fit of assemblies and thus needs to be thoroughly evaluated at the design and manufacturing stages. However, most of the studies on surface roughness modelling and analysis employ empirical approaches, and only consider the effect of a single manufacturing process. In particular, the existing surface roughness models are not applicable to hybrid additive–subtractive manufacturing processes in which a secondary process is involved. In this article, analytical models are established to predict the surface roughness of parts fabricated by AM as well as hybrid additive–subtractive manufacturing processes. A novel surface profile representation scheme is also proposed to increase the prediction accuracy. Case studies are performed to validate the effectiveness of the proposed models. An average of 4.25% error is observed for the AM case, which is significantly smaller than the prediction error of the existing models in the literature. Furthermore, in the hybrid case, an average of 91.83% accuracy is obtained.