Your browser doesn't support javascript. This means that the content or functionality of our website will be limited or unavailable. If you need more information about Vinnova, please contact us.

A framework for the physics-based estimation of tool wear in machining process

Reference number
Coordinator CHALMERS TEKNISKA HÖGSKOLA AKTIEBOLAG - Material- och tillverkningsteknik
Funding from Vinnova SEK 645 000
Project duration August 2017 - March 2018
Status Completed
Venture The strategic innovation programme for Metallic material
Call 2016-04983-en

Purpose and goal

The main objective of the feasibility study was to develop physics-based tool wear modelling framework and assess if it is: (a) capable of predicting the influence of material variations due to chemical compositions (mainly variations in oxide/carbide/nitride content) on tool performance; (b) capable of predicting the tool wear response at various cutting conditions for a given tool/work material combination; (c) more robust compared to the alternative (e.g. process monitoring) approaches.

Expected results and effects

WEAR-FRAME results refer to R&D of innovative approaches/models for a more accurate/robust physics-based tool-wear prediction: (1) robust CALPHAD approach for solubility calculation of tool materials within various work materials; (2) DFT-based hardness estimations of non-metallic inclusions, carbides & nitrides at finite temperatures using Quasi-Harmonic Approximation (QHA); and (3) a hybrid approach for determination of flow stress properties of work materials at high strain rates & temperatures.

Planned approach and implementation

WEAR-FRAME included an extensive amount of experimental activity (machining tests) and material/tool characterisation to provide: (1) fundamental understanding of the wear mechanisms; (2) reliable data for development of the physics-based wear models.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 25 November 2019

Reference number 2017-02517

Page statistics