Development of rheology and computational flow model for robotized external finishing on additively manufactured components

Development of rheology and computational flow model for robotized external finishing on additively manufactured components
Title:
Development of rheology and computational flow model for robotized external finishing on additively manufactured components
Other Titles:
INCASE 2019: Advanced Surface Enhancement
Publication Date:
31 August 2019
Citation:
Turangan C.K., Wan Yee Ming S., Itoh S., Ho J. (2020) Development of Rheology and Computational Flow Model for Robotized External Finishing on Additively Manufactured Components. In: Itoh S., Shukla S. (eds) Advanced Surface Enhancement. INCASE 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore
Abstract:
Stream finishing is accepted as one of the post processing operations. It is not only capable of grinding but also offers polishing of additive manufactured components, having advantages of larger material removal rates and controllable toolpath. We have developed a modelling stream finishing through semi quantitative prediction via computational fluid dynamics (CFD) simulations. The scheme couples the granular flow field with the material removal model by solving the granular flow using a continuum-based method. For the rheology, the media viscosity is determined to resolve the flow field so the pressure induced by the media and the material removal rate can be predicted. The model calibration involves developing a tribometer and using it to measure the media pressure for several scenarios based on the rotational speed of the drum (30rpm), radial distances of the tribometer (100, 250, 400mm), submerged depths (100, 200, 250mm) and its glancing angles (0, 15, 30, 45, 60, 75, 90degree). The work is extended to study the media flow for a simplified square work piece. The results indicated that the particle velocities on the surface of the work piece predicted by simulations are comparable to those of experiments. They show similar patterns and magnitudes for the parameters tested, which demonstrate the capability of the model to correctly predict the granular flow field.
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Funding Info:
There was no specific funding for this research
Description:
This is a post-peer-review, pre-copyedit version of an article published in INCASE 2019: Advanced Surface Enhancement. The final authenticated version is available online at: https://doi.org/10.1007/978-981-15-0054-1_24
ISSN:
978-981-15-0053-4
978-981-15-0054-1
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