The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.

https://www.mdpi.com/1996-1944/15/12/4059/pdf?version=1654666231

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

https://onlinelibrary.wiley.com/doi/pdf/10.1002/eng2.12240

Cutting forces and crater wear prediction in orthogonal cutting using two approaches of finite element modeling

Study on the modified power-law constitutive model for the FEM cutting simulation of Inconel 625 deposited metal using the electroslag welding process

Prediction of cutting forces including tool wear in high-feed turning of Nimonic® C-263 superalloy: a geometric distortion-based model

FE analysis of Ultrasonic vibration assisted turning of magnesium AZ31B alloy

In this study, a FEM-based tool wear approach with a focus on the geometry of the worn tool, especially the changes of flank wear land inclination angle, was developed. The relationship between the...

Tool wear estimation in machining based on the flank wear inclination angle changes using the FE method

A new non-uniform sequential mobile packing (NSMP) algorithm featuring an efficient collision detection scheme was developed to rapidly generate representative volume elements (RVEs) for advanced heterogeneous and/or composite material systems with either spherical or cylindrical inclusions and a broad range of microstructural characteristics (i.e., various inclusion orientations and sizes, and agglomerations). Statistical assessment of inclusion spatial dispersion using nearest neighbor-based and pair correlation functions revealed that the NSMP algorithm generated RVEs with realistic microstructures for all cases considered. Subsequent statistical finite element micromechanical modeling was conducted to compute the effective properties for each generated microstructure. A case study for a 3D-printed nanocomposite material revealed that a combination of aligned and misoriented rod inclusions was the most representative microstructure. Contour plots of normalized effective stiffness for the 3D-printed composites and normalized average hydrostatic stress in the matrix phase enabled comparison of RVEs with different microstructures. The material system with aligned rod inclusions benefited from simultaneous high effective stiffness and effective load transfer to the inclusions (at higher inclusion volume fractions), which is indicated by low average hydrostatic stress in the matrix. The microstructures comprising spherical inclusions exhibited moderate stiffness, while those with clustered misoriented rod inclusions had the lowest effective stiffness and load transfer to the inclusions. The NSMP algorithm can be used for further microstructural optimizations and assessments of renifornced or porous heterogeneous and/or composite materials using machine learning, while the generated contours can be used as general design guidelines. 

Ramin Yousefi Nooraie

Papers

Tool wear estimation in machining based on the flank wear inclination angle changes using the FE method

A sequential mobile packing algorithm for micromechanical assessment of heterogeneous materials