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Fracture mechanics-based modelling of tool wear in machining Ti6Al4V considering the microstructure of cemented carbide tools

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Gohari H. , Shi B. , Attia M. H. , M'saoubi R.

Journal of Machine Engineering

2024

tom Vol. 24, No. 2

5--17

This study introduces a new wear model that can predict tool life in the milling process of Ti6Al4V using a cemented carbide tool. The model uses a finite element (FE) simulation to predict crack growth in the tool material microstructure. The FE model evaluates the crack propagation rate based on the real microstructure of the tool material, which is captured from microscopic images. To determine the normal and tangential forces operating on the flank face, an experimental procedure was developed based on three different flank wear widths. The FE model utilizes the elastic and fracture properties of tungsten carbide, and the elastic-plastic and fracture characteristics of cobalt binder to determine crack growth under the applied cutting forces. The crack propagation information combined with cutting conditions and the initial wear level are used to estimate the tool wear state. The developed model can predict tool life under different cutting conditions, tool geometries, and microstructure properties. Analysis of results showed that the error for the straight cuts was less than 6%, while for the complex cuts, it reached up to 20%. The accuracy of the model can be improved by extending the calibration test to higher levels of flank wear.

Intelligent cyber-physical monitoring and control of I4.0 machining systems – an overview and future perspectives

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Hassan M. , Sadek A. , Attia M. H.

Journal of Machine Engineering

2022

tom Vol. 22, No. 1

5--24

Rapid evolution in sensing, data analysis, and industrial internet of things technologies had enabled the manufacturing of advanced smart tooling. This has been fused with effective digital inter-connectivity and integrated process control intelligence to form the industry I4.0 platform. This keynote paper presents the recent advances in smart tooling and intelligent control techniques for machining processes. Self-powered wireless sensing nodes have been utilized for non-intrusive measurement of process-born phenomena near the cutting zone, as well as tool wear and tool failure, to increase confidence in the process and tool condition monitoring accuracy. Cyber-physical adaptive control approaches have been developed to optimize the cycle time and cost while eliminating machined part defects. Novel artificial intelligence AI-based signal processing and modeling approaches were developed to guarantee the generalization and practicality of these systems. The paper concludes with the outlook for future work needed for seamless implementation of these developments in industry.