Quantification of Tool Wear Condition

Quantification of Tool Wear Condition

This is some text inside of a div block.


In the process of continuous cutting operations using machine tools, tool wear is an unavoidable issue. As tools wear out, their sharpness decreases, leading to a reduction in the dimensional accuracy of the products. This results in decreased production efficiency and quality issues. Therefore, proper management of tools in the production environment is extremely important.

The conventional method of tool management, known as Time-Based Maintenance (TBM), involves replacing tools after a predetermined amount of operating time or number of operations. However, since TBM does not continuously monitor the wear condition of the tools, accurately grasping the actual state of wear is challenging, which risks producing products with inadequate dimensional accuracy. As a result, it is common to replace tools before reaching their full lifespan. On the other hand, in mass production cutting operations, reducing lead times directly contributes to increased profits, necessitating a reduction in the frequency of tool replacements. To meet these needs, MAZIN has been developing technology to estimate the condition of cutting tools.


Experimental Method

As the wear of cutting tools progresses, their sharpness decreases, leading to an increase in cutting resistance. The spindle motor of the machine tool is controlled to maintain a constant rotation speed, so an increase in cutting resistance results in a higher workload for the spindle motor. By utilizing this principle, monitoring the current data of the spindle motor enables the indirect estimation of the tool's wear condition. This method offers the advantage of being non-contact and allows for real-time monitoring of wear status.

Figure 1. Servo Motor

In the experiment, a clamp-type current sensor was attached to the inverter driving the spindle motor of the machine tool to collect current data during processing. Two types of new, unused cutting tools were used to perform repeated end-face cutting of round bars until noticeable tool wear occurred. The collected data were used to extract characteristic values from the current data for each processing count, allowing for the estimation of the tool's wear amount.


The following Figure 2 presents the results of this series of experiments and data analysis. Even with tools of the same type, there were differences in the estimated rate of wear progression between tools used, namely N1 and N2.

Figure 2. Trend of Estimated Wear Values Over Number of Machining Operations

In the figure, the orange line (N1) and the blue line (N2) represent the progression of estimated wear amount in relation to the number of machining operations for the first and second experiments, respectively. It was observed that, despite being of the same type, tool N2 exhibited a faster rate of estimated wear progression compared to N1.


Figure 3. Condition of the Tool After 60 Machining Cycles

Upon observing the condition of the tools at the 60th machining operation under a microscope, it was confirmed that the damage on tool N2 was more severe than on tool N1. This observation suggests that the wear amount estimated from the current data reflects the actual extent of wear.


Figure 4. Correlation between Measured Wear Amount and Estimated Wear Value

Furthermore, Figure 4 compares the wear amount estimated from the current data for four cutting tools with the wear width determined from images taken with a microscope. While there are some variations due to measurement errors in the current data and errors in determining wear width from the images, a generally positive correlation was observed between the two sets of data.

The lack of correlation observed beyond a measured wear amount of 600μm is attributed to measurement errors. This discrepancy arose due to partial loss of the tool, preventing accurate measurement of wear width from the images.


Tool wear directly affects the dimensional accuracy of products. Since the allowable range of dimensional accuracy varies depending on the classification of machining methods and target products, accurately grasping the state of tool wear is crucial. By using the technology developed in this research, it becomes possible to perform tool replacement at the optimal timing based on the estimated amount of wear. This management method is known as Condition-Based Maintenance (CBM) and serves as a replacement for the traditional Time-Based Maintenance (TBM). The implementation of CBM is expected to reduce the lead time required for tool replacement and enhance the productivity of mass production cutting operations. Furthermore, CBM allows for more precise management of tool conditions without wasting the unused lifespan of tools.

Moving forward, we plan to further develop this technology and advance the development of systems that can be applied to more complex machining conditions and a wider variety of tool types.