Statistical Approach for Modeling Abrasive Tool Wear and Experimental Validation when Turning the Difficult to Cut Titanium Alloys Ti6Al4V

Abstract

Tool wear and tool failure are critical problems in the industrial manufacturing field since they affect the quality of the machined workpiece and raises the production cost. Improving our knowledge of wear mechanisms and capabilities of wear prediction are therefore of great importance in machining. The three main wear modes usually identified at the tool/chip and the tool/workpiece interfaces are abrasion, adhesion and diffusion. Besides, because of their difficult experimental analysis and measurements of their friction interface features (such as temperature, pressure, particles embedded in the contact …), understanding mechanisms that govern these wear modes is still incomplete. The objective of this research work is to develop a new wear model in which abrasive particles are assumed embedded between the tool and the chip at the interface. These particles are considered with a conical shape and are characterized by two main geometric parameters: the corresponding apex angle and size. The wear particles can be seen as a non-metallic inclusions or wear debris generated during the machining process. A probability density function has been adopted to describe the fluctuation of the size and the apex angle of particles in the tool/chip contact area. The influence of the used statistical distribution has been analyzed depending on which law has been adopted: Gaussian or Weibull. The Volume of the removed material per unit of time was chosen, in this study as the main abrasive wear parameter and detailed on a parametric study. Finally, wear tests were carried out with an uncoated WC-Co carbide tool machining a Ti6Al4V titanium alloy to validate the proposed approach.