About: Digital force prediction for milling

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Digital force prediction for milling
Creator	Gomez, Michael No, Timothy Schmitz, Tony
source	Elsevier
abstract	Abstract This paper presents a fully digital, integrated approach for milling force prediction with arbitrary end mill-work material combinations. The approach includes: 1) structured light scanning to identify the end mill’s cutting edge macro-geometry along the tool axis; 2) structured light scanning to measure the cutting edge cross-sectional rake and relief profiles; 3) finite element analysis of orthogonal cutting to determine the force model coefficients that relate the force to chip area using the work material’s constitutive model and tool’s rake and relief profiles; and 4) time domain simulation with inputs that include the measured cutting edge macro-geometry, finite element-based force model, and measured structural dynamics. Milling force predictions are compared to in-process measurements to validate the method.
has issue date	2020-12-31(xsd:dateTime)
bibo:doi	10.1016/j.promfg.2020.05.125
has license	els-covid
sha1sum (hex)	d6ce3db6f7530f22064ad5e20afd28327cf23424
schema:url	https://doi.org/10.1016/j.promfg.2020.05.125
resource representing a document's title	Digital force prediction for milling
schema:publication	Procedia Manufacturing
resource representing a document's body	covid:d6ce3db6f7530f22064ad5e20afd28327cf23424#body_text
is schema:about of	named entity 'measured' named entity 'determine' named entity 'analysis' named entity 'presents' named entity 'measured' »more»

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