Tool Wear Prediction in Manufacturing

Dear Colleagues,

In both metal cutting and metal forming, controlling the tool wear rate is critical as it affects part geometry, surface, and subsurface integrity. Furthermore, the selection of process parameters, thin film tool coatings and cutting environment, especially for materials with high strain hardening sensitivity and low thermal properties, is dependent on striking a balance between tool wear rate and productivity. At the present moment, the majority of the models could predict wear rate at the flank face region only. Unfortunately, few have the ability to predict the crater wear rate. The tool wear rate predicted by analytical or empirical models is triggered by either mechanical or thermal loadings and at the steady-state wear rate region. Limited published models have the ability to predict the transition between transient and steady-state wear rate. However, this limitation could be overcome using finite element (F.E.) methods. In F.E., the accuracy is controlled by both material’s empirical constant and friction models with a coefficient of friction that is dependent on temperature. The downside of F.E. is the computational time, which is directly proportion to the tool wear rate and, even with the present hardware speeds, is still computationally intensive. When using hybrid approaches like F.E./ empirical models, the computational time could be substantially reduced, but extensive model calibration is required. Regardless of the tool wear prediction approaches, few models could incorporate wear induced by chemical reactivity between the workpiece and tool material.

In this Special Issue of JMMP, we are interested in contributions that focus on, but are not limited to:

• Development of unique analytical models to predict wear on the flank and crater regions;
• Evaluation of different empirical wear models on “difficult to cut or deform” materials, metal matrix composite, and stack materials;
• Methodology to predict the transient erosion rate before reaching steady-state;
• Predicting wear pattern and rate using numerical models (finite element method or finite difference technique) in either orthogonal or oblique configuration;
• Using a hybrid approaches corresponding to analytical/empirical, mechanistic/empirical or numerical/empirical models to simulate tool life;
• Estimating tool performance using non-analytical models like artificial neural network or survival life analysis, etc.;
• Effect of material constitutive models and friction models on tool degradation rate accuracy;
• Advancement in estimating tool life for non-orthogonal metal cutting processes like drilling, ball nosed end milling, tapping, etc.;
• Multivariable empirical models to predict the effects of tool coating characteristics like thickness, micro cutting-edge geometry, friction properties, surface roughness on tool performance;
• Models that are capable of simulating the effect of cutting environment (high-pressure cutting pressure, minimal quantity lubricant, cryogenic cooling) on tool life.

Prof. Dr. Eu-Gene Ng
Guest Editor

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