Tool wear

Last updated

In machining, tool wear is the gradual failure of cutting tools due to regular operation. Tools affected include tipped tools, tool bits, and drill bits that are used with machine tools.

Contents

Types of wear include:

Crater wear Crater wear.png
Crater wear

Effects of tool wear

Some general effects of tool wear include:

Reduction in tool wear can be accomplished by using lubricants and coolants while machining. These reduce friction and temperature, thus reducing the tool wear.


A more general form of the equation is

where

Temperature considerations

Temperature gradient of tool, workpiece and chip during orthogonal cutting. As can easily be seen, heat is removed from the workpiece and the tool to the chip. Crater wear occurs around the 720 degree area of the tool. Temperature gradient tool.png
Temperature gradient of tool, workpiece and chip during orthogonal cutting. As can easily be seen, heat is removed from the workpiece and the tool to the chip. Crater wear occurs around the 720 degree area of the tool.

At high temperature zones crater wear occurs. The highest temperature of the tool can exceed 700 °C and occurs at the rake face whereas the lowest temperature can be 500 °C or lower depending on the tool.

Energy considerations

Energy comes in the form of heat from tool friction. It is a reasonable assumption that 80% of energy from cutting is carried away in the chip. If not for this the workpiece and the tool would be much hotter than what is experienced. The tool and the workpiece each carry approximately 10% of the energy. The percent of energy carried away in the chip increases as the speed of the cutting operation increases. This somewhat offsets the tool wear from increased cutting speeds. In fact, if not for the energy taken away in the chip increasing as cutting speed is increased; the tool would wear more quickly than is found.

Multi-criteria of machining operation

Malakooti and Deviprasad (1989) introduced the multi-criteria metal cutting problem where the criteria could be cost per part, production time per part, and quality of surface. Also, Malakooti et al. (1990) proposed a method to rank the materials in terms of machinability. Malakooti (2013) presents a comprehensive discussion about tool life and its multi-criteria problem. As an example objectives can be minimizing of Total cost (which can be measured by the total cost of replacing all tools during a production period), maximizing of Productivity (which can be measured by the total number of parts produced per period), and maximizing of quality of cutting.

Tool Condition Monitoring

Monitoring tool wear has become increasingly important for ensuring product quality, preventing equipment damage, and optimizing productivity and cost efficiency in manufacturing processes, and in recent years to address this challenge, several experts have proposed an efficient deep learning framework and integrated other techniques to accurately predict tool wear in high-speed CNC milling operations. [3]

See also

References

  1. . Swan et al (September 7, 2018). "Tool Wear of Advanced Coated Tools in Drilling of CFRP." ASME. J. Manuf. Sci. Eng. November 2018; 140(11): 111018. https://doi.org/10.1115/1.4040916
  2. Nguyen, Dinh et al. "Tool Wear of Superhard Ceramic Coated Tools in Drilling of CFRP/Ti Stacks." Proceedings of the ASME 2019 14th International Manufacturing Science and Engineering Conference. Volume 2: Processes; Materials. Erie, Pennsylvania, USA. June 10–14, 2019. V002T03A089. ASME. https://doi.org/10.1115/MSEC2019-2843
  3. Hamdy K. Elminir, Mohamed A. El-Brawany, Dina Adel Ibrahim, Hatem M. Elattar , E.A. Ramadan. "Efficient deep learning model for predicting cutter life in high-speed CNC milling". Tonzamaking.{{cite web}}: CS1 maint: multiple names: authors list (link)