A Framework for Limiting Tool Wear in Machining

Tool wear in machining leads to finite tool life, which is often measured in minutes rather than hours. Because tool costs can be significant, selecting a process plan that limits tool wear to an acceptable, predictable level is an important activity for part production by machining. Tool wear is driven by:

• high temperatures at the tool-chip interface, which can lead to softening of the tool material
• intimate contact between the chip and tool rake face, which encourages diffusion between the chip and tool materials, particularly at high temperatures (this can cause the carbon-hungry steel chip to rapidly wear an uncoated tungsten carbide cutting tool, for example)
• hard inclusions in the workpiece material, which can abrade the tool material
• large cutting forces and pressures, which can cause plastic deformation and fracture of the tool edge
• thermal cycling in interrupted cuts, which can cause cracking and catastrophic tool failure due to thermal fatigue

Several wear mechanisms have been identified in metalcutting. These include abrasion, adhesion, diffusion and attrition. Abrasion occurs when hard particles in the workpiece material scratch the tool surface as the chip is sheared away. It can also occur when small particles of the hard tool material are dislodged and carried away with the chip, again scratching the tool surface. The hard particles can be carbides, oxides or nitrides, for example.

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Adhesive wear occurs when the work material locally welds to the tool surface due to the high temperatures and pressures and then tears away tool material as it is removed during the chip formation process. Adhesion is also the mechanism for built-up edge (BUE), which is typically observed at lower cutting speeds. BUE occurs near the tool point where the tool-chip interface temperature is lower and the harder material tends to seize against the tool rake face. Farther along the contact, the temperature is higher and the softer chip slides more easily. The BUE can change the effective rake angle at the tool point, which affects the cutting force magnitude.

Diffusion depends on the chemical affinity between the tool and workpiece. Its rate, which is defined by the number of atoms of the tool or workpiece material that penetrates the other at the intimate, high-pressure rake-face contact, is strongly temperature dependent, so diffusion often dominates at high cutting speeds where the tool-chip interface temperature is also high. Diffusion is therefore a mechanism for crater wear, which occurs where the temperature is highest, although adhesion and abrasion can also contribute to crater development. Additionally, diffusion participates in flank wear.

Attrition occurs when tool material grains near cutting edge are broken out by contact with the chip and machined surface. It is a primary mechanism for flank wear and can dominate at low cutting speeds with non-uniform chip flow.

These wear mechanisms combine to form characteristic wear features on the cutting tool surfaces. Common wear features include flank, crater and notch wear. These are depicted in Figure 1. In many cases, a single wear feature will dominate, such as flank wear, but all may occur simultaneously.

Fig. 1:  Common wear features in metalcutting. Source (all figures): Tony Schmitz

Tool materials have progressed from high-speed steels in the 1900s to sintered carbides, such as tungsten carbide (WC) and titanium carbide (TiC) (1930s), to ceramics, such as Al₂O₃, cubic boron nitride (CBN) and polycrystalline diamond (PCD) (1960s), and coated steels and carbides (1970s). Coatings are applied by chemical vapor deposition (CVD) and physical vapor deposition (PVD). Common examples include TiC, TiN, Al₂O₃ and combinations of these in alternating layers. One important issue for coating life is the cutting-edge preparation. Edge honing can be used to increase the coating persistence at the sharp edge. It can also increase the process damping effect, where increased stable axial depths of cut are made available at low spindle speeds.

Although it has been understood since the early 1900s that the rate of tool wear depends strongly on the cutting speed, as well as other process parameters and tool geometry, its preprocess prediction remains elusive. Empirical modeling efforts are therefore common. In tool life testing, we begin with a new cutting edge, machine for a selected time or volume of material removed, and measure the flank wear width (FWW) or other feature after that interval. We then continue the test for a second interval and remeasure. This process is repeated until the tool reaches the FWW limit. This sequence is repeated using different sets of process parameters to determine the best-fit constant(s) for a power law-type tool life equation. The ranges for the experimental process parameters may be based on tool supplier recommendations or prior experience, for example.

To demonstrate the procedure for identifying an empirical tool life model, let’s examine experimental results from a milling study. Tool wear tests were completed using a 19.1-mm diameter, single-insert indexable end mill (uncoated carbide) to machine 1018 steel. The tool life model was vTⁿ = C, where v is the cutting speed, T is the time to reach the pre-selected FWW limit, and n and C are empirical constants. A digital microscope was mounted to the machine table and used to measure the FWW on the carbide insert at intervals of 12 cm³ of volume removed (no significant crater or notch wear was observed). The milling parameters were: radial depth of 4.76 mm (25% RI), axial depth of 3 mm and feed per tooth of 0.06 mm.

The FWW progressions for tests at spindle speeds of (1500, 3750 and 6250) rpm are shown in Figure 2. The tool life was defined as the time required to reach a FWW of 0.3 mm. As expected, the tool life decreased with the increased cutting speed (and temperature). The test results are summarized in Table 1.

Fig. 2: FWW vs. cutting time for all three spindle speeds. The tool wear rate increases rapidly with increasing spindle speed.

Table 1: Test results for milling 1018 steel with an uncoated carbide insert.

The tool life model constants were determined from a least-squares best fit to the data: vT^0.455 = 1,120. The data points and fit are presented in Figure 3. Given this model, a spindle speed may be selected to give the desired tool life. For example, if a tool life of 60 min is desired, the spindle speed should be 2,800 rpm or lower in this case.

Fig. 3: Tool life model (solid line) and experimental data points (×) for 1018 steel tool wear tests.