Worn tool geometry–based flank wear prediction in micro turning. (March 2020)

• Record Type: Journal Article
• Authors: Elias, Jiju V; Asams, S; Mathew, Jose
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 4(2020)
• Page Start: 710
• Page End: 719
• Publication Date: 2020-03
• Subjects: Micromachining -- micro turning -- Ti-6Al-4V -- tool-wear model -- tool-wear prediction; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405419889239
• Languages: English
• ISSNs: 0954-4054

Abstract

Mechanical micromachining techniques have gained much popularity in the manufacturing of microcomponents with complex shapes in the past couple of decades. Machining at the microscale poses several challenges such as size effect, which highly influences the material deformation mechanism resulting in a nonlinear variation in specific cutting energy, which accelerates the tool wear. Since micromachining is associated with micro-features, high precision and tight tolerances, the prediction of tool wear in advance is essential. Calibrated tool-wear models are generally used for the prediction of tool wear in the macro machining regime, whereas the applicability of these tool-wear models in the microscale machining is not explored much in the past. In the present work, Usui tool-wear model and worn tool geometry–based Malakizadi model are calibrated for the tool flank wear prediction during micro turning of Ti-6Al-4V alloy, using a hybrid approach involving both finite element simulations and cutting experiments. The validation experiments show that both the models can satisfactorily predict the tool-wear rate during micro turning with a percentage error of less than 15%. Results indicate that the worn tool geometry–based tool-wear model outperforms the conventional Usui model as it incorporates the instantaneous tool geometry, which also makes it suitable for different tool geometries.