A visualization method for tool chipping monitoring based on petal graph

Abstract

A petal diagram-based visualization method for tool chipping monitoring includes the following steps: step A, vibration signal acquisition; step B, processing segment data interception; step C, full-cycle signal truncation using the amplitude maximization method; step D, time-domain synchronous averaging; and step E, drawing a polar coordinate petal diagram. The present invention utilizes time-domain synchronous averaging technology and narrowband filtering to extract key order signals, analyze the changes in the average energy of the blade during the cutting process to characterize the state of the tool, and achieve efficient identification of tool chipping. Specifically, the core of the present invention is time-domain synchronous averaging and narrowband filtering. The entire process requires little signal processing computation, is easy to operate, and is not easily disturbed by noise. The changes in the tool state can be intuitively represented by a "petal"-shaped graphic on the polar coordinate diagram, clearly and stably depicting the tool health status, providing favorable technical support for stable and reliable machine tool processing.

Description

Technical Field

[0001] The present invention relates to the technical field of detection methods, and in particular to a visualization method for monitoring tool chipping based on a petal graph. Background Art

[0002] Machine tools are the foundation of modern manufacturing and are known as the "mother of industry." Without milling machines, lathes, and other types of processing machines, the production of many high-precision parts and components would be impossible, directly impacting the development of the automotive, aviation, medical, electronics, and other high-tech industries. The machine tool industry is often a key indicator of a country's industrialization level, directly impacting employment, exports, and economic growth. Many countries prioritize the development of the machine tool industry as a strategic sector. By using advanced machine tools and related monitoring systems (such as tool failure monitoring and CNC systems), manufacturers can more precisely control product quality, thereby meeting or exceeding international standards. Specifically, machine tools use high-speed rotating cutting tools to perform machining operations such as cutting, milling, and drilling on workpieces, producing parts quickly and efficiently. The quality and condition of the cutting tools directly impact machining accuracy, final product quality, and overall productivity. Various tool failures can occur due to factors such as contact, friction, and vibration between the tool and the workpiece during high-speed machining, as well as inherent material non-uniformity. One of the most serious forms of tool failure is tool chipping. Tool chipping occurs when the tool tip or cutting edge breaks or breaks off during the cutting process due to various reasons, such as excessive load, material hardness, insufficient cooling, or tool quality issues. This not only affects cutting performance but can also damage the entire tool or the workpiece, significantly impacting production efficiency and personal safety. Statistics show that tool failures account for 7% to 20% of total downtime in machining. Monitoring tool wear in machine tools is an essential component of modern manufacturing, crucial for improving production efficiency, ensuring product quality, reducing costs, and enhancing enterprise competitiveness. To improve machining quality and efficiency and reduce the occurrence of abnormalities, effective monitoring of tool chipping during machining is essential.

[0003] At present, the vibration signal collected by the acceleration sensor installed on the machine tool or tool is usually used to perform time domain, frequency domain, and time-frequency domain analysis to monitor the status of the tool. In recent years, with the continuous development of sensor technology, computer technology, and signal processing technology, many new methods have been proposed for tool chipping monitoring, mainly including the following methods. (1) Comparing the machine tool spindle motor torque with the predicted cutting torque obtained by cutting force simulation to identify whether the tool is chipping; (2) Using the singularity analysis method based on wavelet transform to quantitatively characterize the change of vibration waveform with Holder index, extracting effective components from the Holder index index to characterize the status of the tool; (3) Using the sliding window segmentation to extract the average root mean square and peak power spectrum density indicators of the spindle vibration signal to monitor the chipping of the tool; (4) Using the peak period of the spindle vibration to detect the chipping of the tool, the time difference between the adjacent peaks of the vibration signal reflects the change of the frequency component, and then reflects the status of the tool; (5) By analyzing the axial force of the machine tool, drilling temperature and tool wear, a mathematical model of tool cutting is established based on the consideration of cutting speed and material hardness, and the model is used to predict and judge the chipping of the tool.

[0004] While the aforementioned methods have achieved some success in machine tool tool chipping monitoring, methods based on comparing simulated torque with actual torque suffer from unstable simulation results due to inaccurate predicted torque, as well as factors such as actual lubrication and machine tool aging, which can lead to misjudgments of tool chipping. Methods that extract time-domain features of vibration signals to monitor tool status are more straightforward, but their ability to characterize tool status is limited, and they are susceptible to interference from noise and external signals. This makes them unsuitable for tool chipping monitoring in complex environments and machining conditions. Methods based on establishing a mathematical model of tool status require a large number of conditions and parameters to establish an accurate mathematical model, a process that requires a significant amount of computation. For highly complex or nonlinear cutting conditions, inaccurate modeling can lead to misjudgments of tool chipping monitoring. Furthermore, traditional tool monitoring methods lack intuitive and efficient descriptions of tool status. In summary, existing technologies are still unable to effectively meet the requirements for tool chipping monitoring. Summary of the Invention

[0005] In order to overcome the drawbacks described in the background that there is no reliable and effective method for monitoring tool chipping in the prior art, the present invention provides a tool chipping monitoring visualization method based on a petal diagram that requires only a small amount of calculation under the joint action of relevant steps, has the advantages of being easy to operate and not easily disturbed by noise. By analyzing the tool vibration signal collected during the processing, the signal envelope after narrow-band time-domain synchronous averaging processing is obtained, and the signal is plotted in a polar coordinate system. The changes in the "petal"-like graphics are used to intuitively represent the status of the tool, thereby achieving a more efficient and accurate monitoring of the tool chipping situation of machine tools.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A petal diagram-based tool chipping monitoring visualization method is characterized by comprising the following steps: Step A, collecting original vibration signals. Specifically, using a vibration sensor installed on the spindle of a machine tool, starting from when a new tool is replaced, the vibration signals of the machine tool are collected during multiple machining processes. Get the dataset ; Step B, processing segment data interception, specifically observe the time domain diagram and spectrum diagram of the signal, intercept reasonable processing segment data Step C, using the amplitude maximization method to truncate the signal into a whole cycle, specifically starting from the tail of the intercepted signal Z, continuously deleting points, when the length of the signal reaches the length of the whole cycle, the amplitude of the frequency conversion reaches the maximum, and the optimal number of deletion points is determined , cut off the whole cycle of the processing section signal Z and get the signal Step D, time domain synchronous averaging, specifically select the appropriate full cycle truncated signal channel, achieve time domain synchronous averaging of the signal by frequency resampling method, and at the same time suppress the signal order by filter bank, retain a certain order edge frequency and its first order side frequency to obtain narrowband filtered signal , and then obtain the signal Step E, draw a polar coordinate petal diagram, specifically by determining the polar angle by the length of the time domain synchronous average signal , set the signal The minimum value of the envelope signal corresponds to the peak of a petal graph. The envelope is rotated and translated to align the minimum point with the peak position. Then the signal The envelope signal is multiplied on the polar axis, through The rotation angle of the petal diagram can be controlled to complete the drawing of the polar coordinate diagram. Through the dynamic changes of the polar coordinate diagram, the status of the tool in the corresponding processing process and the changes in the tool status between different processing processes can be represented, achieving a clear and stable characterization of the tool status.

[0008] Preferably, in step A, the data set includes normal processing data and data after tool edge breakage.

[0009] Preferably, in step B, the duration of intercepting the signal covers 30-50 tool rotation cycles.

[0010] Preferably, in step C, for a periodic signal, if the signal is truncated by a whole period, it refers to the window function Intercepted the signal If T is the period of the signal, then the integer period truncation can be calculated by the formula Indicates that n is a positive integer, which means that n complete cycles are intercepted. Is a rectangular window function, given by the formula Definition, for non-integer period truncation, window function Intercepted the signal A fragment of a non-integer period, given by the formula Indicates that and It is an arbitrary point in time.

[0011] Preferably, in step C, when the signal is truncated by a non-integer cycle, the discontinuity of the truncation point will cause spectrum leakage, that is, the energy of the signal "leaks" from its true frequency component to other frequency components, which will make the frequency domain representation vague and inaccurate, affecting the subsequent spectrum analysis and time domain synchronous averaging. Therefore, the signal must be truncated by a whole cycle.

[0012] Preferably, in step D, the influence of random noise and non-periodic events can be effectively eliminated or reduced by synchronous averaging, so as to more accurately observe and analyze the basic behavior of the signal. Specifically, the period is first determined, and all period truncated signals are averaged. The signal after synchronous averaging of each period length is By formula It indicates that through synchronous averaging, random noise is usually reduced during the averaging process and periodic content is highlighted, making subsequent analysis more accurate.

[0013] Preferably, in step E, the tool vibration signal is plotted as a petal diagram in polar coordinates, which is essentially to analyze the cutting energy of each blade of the tool, and the cutting energy of each blade is analyzed by order spectrum to characterize the tool state, especially to identify the chipping of the tool.

[0014] Preferably, in step E, the order spectrum is based on the operating cycle of the system. The sampling frequency is , the rotation speed of the mechanical system is , order and frequency The relationship is given by the formula Indicates that before calculating the order spectrum, the signal is resampled or interpolated, and Fourier transform (FFT or DFT) is performed on the resampled signal to obtain the order spectrum , specifically by the formula Calculated.

[0015] Preferably, in step E, in the signal, the blade frequency will be modulated by the rotation frequency amplitude, and after the blade is broken, the energy of the tool will be concentrated on certain edges. Not all order energies can reasonably represent the state of the tool. Therefore, reasonable order components are retained through narrowband filtering. Specifically, the discrete time signal is set to , use a bandpass filter to perform narrowband filtering to eliminate a specific frequency component , its transfer function Expressed as a formula ,in is the impulse response of the filter, and P is the filter order.

[0016] Compared with the technology in the field, the beneficial effects of the present invention are: the present invention draws a polar coordinate petal diagram through vibration signals for monitoring tool chipping, uses time domain synchronous averaging technology and narrowband filtering to extract key order signals, analyzes the changes in the average energy of the blade during the cutting process to characterize the state of the tool, and realizes efficient identification of tool chipping; specifically, the core of the present invention is time domain synchronous averaging and narrowband filtering, the signal processing calculation amount of the whole process is small, the operation is simple, and it is not easily disturbed by noise. It can intuitively represent the changes in the tool state through a "petal"-shaped graphic on the polar coordinate diagram, and clearly and stably portray the health status of the tool, which provides favorable technical support for stable and reliable processing of machine tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Figure 1 This is a schematic diagram of a three-edge milling cutter and tool edge chipping.

[0019] Figure 2 It is a process framework diagram of the present invention.

[0020] Figure 3 This is a schematic diagram of the vibration sensor installation.

[0021] Figure 4 It is the three-channel time domain and frequency domain diagram of the original signal of the present invention.

[0022] Figure 5 These are the time domain and frequency domain diagrams of the truncated signal during the entire normal machining cycle and the time domain and frequency domain diagrams of the truncated signal during the entire chipping cycle of the present invention.

[0023] Figure 6 It is a narrow-band TSA waveform diagram of a certain three-edged tool of the present invention.

[0024] Figure 7 This is a diagram of the filtering results of the second-order edge frequency and the first-order sideband of a certain three-edge tool of the present invention.

[0025] Figure 8 This is the envelope signal diagram of a three-edged tool after narrow-band filtering.

[0026] Figure 9 This is a petal diagram of the normal processing process.

[0027] Figure 10 This is a picture of petals after the blade breaks. DETAILED DESCRIPTION

[0028] In the present invention, a three-blade milling cutter of a machine tool is as follows Figure 1 As shown in (a), the tool chipping is shown in (b). The milling process of the D4-H10 left-hand end fine milling special milling cutter is taken as an example to further illustrate the present invention, wherein the machine tool spindle speed is 16000 rpm, the number of tool blades is 3, the sampling frequency is 6250 Hz, and the sampling time is 28 s.

[0029] Figure 2 As shown in FIG, a visualization method for monitoring tool chipping based on a petal diagram is provided. The specific steps are as follows: Step 1: Use a vibration sensor installed on the spindle of a machine tool to collect vibration signals in real time during the milling process of the machine tool. The installation of the vibration sensor is as follows: Figure 3 As shown, specifically, sampling starts from the newly replaced tool, sampling multiple processing processes of the tool, including the normal processing process and each milling process after the tool edge is broken; the sampling frequency is 6250Hz, the sensor sampling channel is 3 channels (x, y, z), and the collected signal is recorded as Where i represents the spindle vibration signal number; the time domain and frequency domain diagrams of the original three-channel signal collected for a single machining process are as follows Figure 4 As shown, all collected signals constitute the data set . Step 2, intercept the original vibration signal collected in step 1 through PC application software to ensure that the intercepted signal is a processing segment signal; specifically, in the machine tool processing process, it includes both the process of the tool milling the workpiece, that is, the interaction between the tool and the processed material, and the tool feed and retract process. For the tool chipping monitoring, the feed and retract processes are meaningless, so the present invention needs to intercept the processing segment signal; specifically, observe the time domain diagram of the original signal, select the start time t_start and the end time t_end for interception, and the intercepted signal duration can cover 30-50 tool rotation cycles. If the interception is too short, it will affect the subsequent analysis process. If the interception is too long, it will not affect the analysis results, but it will increase the amount of calculation. After reasonable interception, the processing segment data is obtained. This step is to reasonably intercept the data signal of the processing section. Because after the tool fails due to edge chipping, the signal is relatively stable during the feed and retract process. Analyzing this signal section cannot effectively monitor the status of the tool. In the data signal of the tool processing workpiece section, due to the chipping of the tool, the energy will be unevenly distributed on each cutting edge during the cutting process. Some cutting edges have high energy, while others have low energy. This will be reflected in the signal to a certain extent. Therefore, it is necessary to intercept the processing section signal and conduct further analysis to achieve reasonable monitoring of chipping.

[0030] Figure 2As shown, in step three, the processing segment data Z intercepted in step two is truncated in the whole cycle; specifically, the amplitude maximization method is used to intercept the signal in the whole cycle, which can be obtained by the formula (1) means, where is the Fourier transform of the signal after deleting k points at frequency The amplitude at , k is the number of points removed from the end of the signal, In the present invention, points are continuously deleted from the end of the signal to maximize the amplitude of the signal frequency conversion. The goal is to find the value of k such that The maximum can be obtained by the formula (2) After calculating and determining k, the signal is truncated to obtain the full cycle truncated signal The time domain and frequency domain diagrams of the three channels of the full-cycle truncated signal are as follows: Figure 5 As shown, combined Figure 4 It can be seen that after the full cycle is cut off, the frequency amplitude is more accurate. In step 3, for a periodic signal, if the signal is cut off by the full cycle, it refers to the window function Intercepted the signal If T is the period of the signal, then the integer period truncation can be expressed as Indicates that n is a positive integer, which means that n complete cycles are intercepted. Is a rectangular window function, given by the formula Definition; For non-integer period truncation, the window function Intercepted the signal A fragment of a non-integer period, which can be expressed by the formula Indicates that and is an arbitrary time point. This step is important because, since the rotation frequency is the most significant periodic signal, subsequent time-domain synchronous averaging is performed based on the rotation frequency. Therefore, the amplitude of the rotation frequency must be accurately determined, necessitating integer-cycle truncation. When a signal is truncated with a non-integer period, the discontinuity at the truncation point can lead to spectral leakage. This means that the signal's energy "leaks" from its true frequency component to other frequency components, blurring and inaccurate the frequency domain representation. This can affect subsequent spectral analysis and time-domain synchronous averaging, so integer-cycle truncation is necessary.

[0031] Figure 2As shown, in step 4, the signal processed by the present invention is a vibration signal of three channels. It is necessary to select a suitable channel for truncating the signal in the whole cycle. Specifically, the frequency resampling is achieved by Chirp-Z transform to achieve synchronous averaging of the signal. Chirp-Z transform (CZT) is a numerically efficient method for calculating the Z transform of complex signals. Chirp-Z transform allows flexible specification of frequency range and frequency resolution. Therefore, it is particularly suitable for resampling the spectrum of the signal on a non-uniform frequency grid. The general form of Chirp-Z transform is defined as formula ,in, is the input signal, is the output, i.e. the Z transform on the complex plane, A and W are complex numbers used to define the frequency and time range, L is the length of the signal, i is the index of the selected frequency sample, and * represents conjugate. During the calculation process, for the case of low sampling frequency, a larger interpolation multiple rt can be set, which is equivalent to increasing the sampling rate by rt ​​times, which can make the waveform output by TSA smoother. In this example, rt is set to 10. Since the number of tool blades in this example is 3, the narrowband filter chooses to retain the second-order blade frequency and its first-order sideband, that is, retain the 5th, 6th, and 7th order frequency components of the rotation frequency as a representation of the energy change of the cutting edge. The narrowband TSA waveform of this tool is as follows Figure 6 As shown, the filtering results of the second-order edge frequency and its first-order side frequency are as follows Figure 7 As shown, the envelope of the narrowband filtered signal is as follows Figure 8 As shown. In step 4, time domain synchronous averaging (TSA) is a technology used to extract basic or repetitive features from periodic events. This method is mainly used to process signals from rotating machinery, motors or other periodic systems. Through synchronous averaging, the influence of random noise and non-periodic events can be effectively eliminated or reduced, so as to more accurately observe and analyze the basic behavior of the signal. Specifically, first determine the period. For a machine tool, each rotation of the tool is a periodic event. Then select the synchronization point, align the signals of different periods, and then intercept multiple complete period segments, and average all the periodic truncated signals. Assume that the original signal is , a total of cycles, each cycle is , then the signal after time domain synchronous averaging is The formula Synchronous averaging typically reduces random noise during the averaging process, highlighting periodic content and facilitating more accurate subsequent analysis. This step is necessary to highlight the periodic components of the signal—namely, the rotational frequency and its multiples, and the edge frequency and its multiples—while suppressing noise. Narrowband filtering is performed because the essence of petal plotting is to analyze the cutting energy of each cutting edge. Theoretically, a perfect cutting process should produce a vibration signal consisting solely of the edge frequency and its multiples. Tool imbalance, eccentricity, or chip jamming can lead to rotational frequency components and their multiples. Uneven wear, chipping, and uneven cutting can alter the energy distribution between cutting edges, with some edges experiencing greater energy and others experiencing less. From a signal processing perspective, this can be interpreted as amplitude modulation. This involves multiplying the original signal by a scaling factor (envelope) to distribute the energy between cutting edges. Amplitude modulation generates sidebands around the edge frequency and its multiples. Generally speaking, the more pronounced the sidebands, the greater the energy difference between cutting edges. However, it's difficult to determine the distribution of these differences based solely on the sidebands. Instead, we can extract the edge frequency (or its harmonics) along with the sidebands from the original signal and then calculate their envelope. The mean of the envelope gives the average energy of the edge frequency (or its harmonics), and the fluctuations in the envelope reflect the imbalance between the edges, thereby characterizing the tool's condition. Because edge frequencies close to the rotational frequency are more severely modulated by the rotational frequency, we prefer to extract second-order or higher-order edge frequencies.

[0032] Figure 2 As shown in step 5, the software interface draws the petal diagram in the polar coordinate system. Specifically, the radius of the petal diagram By the formula Given, where tn represents the number of cutting edges of the tool, in this case 3, is the envelope after rotation and translation, (4); then solve the minimum position of the envelope, for the envelope signal , minimum value and corresponding index pass Get, No. The position of the petal apex By formula Calculate, where is the length of the synchronous average signal. Envelope signal By cyclic displacement The displacement is obtained The petals corresponding to two normal processing processes of the three-blade milling cutter of the present invention are as follows Figure 9 As shown in the figure, the petal diagrams of the two processing processes corresponding to the chipping failure are as follows Figure 10As shown. The dynamic changes of the polar coordinate diagram can be used to characterize the health status of the tool in the corresponding machining process, and the tool health status can be clearly and stably portrayed. In step five, a petal diagram under polar coordinates is drawn for the tool vibration signal. In essence, the cutting energy of each blade of the tool is analyzed. The cutting energy of each blade is analyzed through the order spectrum to characterize the tool state, especially to identify the chipping of the tool. The order spectrum is a special spectrum representation used to analyze the vibration signals of rotating machinery or other periodic motion systems; the order spectrum uses the operating cycle of the system (usually the rotation speed) as a reference. If the discrete time signal The sampling frequency is , the rotation speed of the mechanical system is , order and frequency The relationship is given by the formula In other words, calculating the order spectrum often requires resampling or interpolating the signal first, and performing Fourier transform (FFT or DFT) on the resampled signal to obtain the order spectrum. , according to the formula In the signal, the blade frequency will be modulated by the rotation frequency amplitude, and after the blade is broken, the energy of the tool will be concentrated on certain edges. Not all order energies can reasonably represent the state of the tool. Therefore, reasonable order components are retained through narrowband filtering. Assuming that the discrete time signal is , use a bandpass filter to perform narrowband filtering to eliminate a specific frequency component , its transfer function Expressed as a formula ,in is the impulse response of the filter, is the filter order.

[0033] Figure 2 As shown, through the technical solution, the present invention uses time-domain synchronous averaging technology and narrow-band filtering to extract key order signals, analyze the changes in the average energy of the blade during the cutting process to characterize the state of the tool, and achieve efficient identification of tool chipping; the core of the present invention is time-domain synchronous averaging and narrow-band filtering. The signal processing calculation amount of the entire process is small, the operation is simple, and it is not easily disturbed by noise. The signal envelope after narrow-band time-domain synchronous averaging processing is obtained and plotted in a polar coordinate system. The changes in the tool state can be intuitively represented through a "petal"-shaped graphic on the polar coordinate graph, and the tool health status can be clearly and stably portrayed, thereby achieving effective monitoring of the tool chipping condition.

[0034] In the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; they may refer to mechanical or electrical connections; they may refer to direct connections or indirect connections through an intermediary; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0035] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0036] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A visualization method for tool chipping monitoring based on petal graph, characterized in that: The following steps are included: Step A: collecting original vibration signals. Specifically, using a vibration sensor installed on the spindle of a machine tool, starting from when a new tool is replaced, the vibration signals of the machine tool are collected during multiple processing steps. Get the dataset ; Step B, processing segment data interception, specifically observe the time domain diagram and spectrum diagram of the signal, intercept reasonable processing segment data Step C, using the amplitude maximization method to truncate the signal into a whole cycle, specifically starting from the tail of the intercepted signal Z, continuously deleting points, when the length of the signal reaches the length of the whole cycle, the amplitude of the frequency conversion reaches the maximum, and the optimal number of deletion points is determined , cut off the whole cycle of the processing section signal Z and get the signal Step D, time domain synchronous averaging, specifically select the appropriate full cycle truncated signal channel, achieve the time domain synchronous averaging of the signal by frequency resampling method, and at the same time suppress the signal order by the filter bank, retain a certain order edge frequency and its first order side frequency to obtain a narrowband filtered signal , and then obtain the signal Step E, draw a polar coordinate petal diagram, specifically by determining the polar angle by the length of the time domain synchronous average signal , set the signal The minimum value of the envelope signal corresponds to the peak of a petal graph. The envelope is rotated and translated to align the minimum point with the peak position. Then the signal The envelope signal is multiplied on the polar axis to control the rotation angle of the petal diagram and complete the drawing of the polar coordinate diagram. The dynamic changes of the polar coordinate diagram can be used to characterize the status of the tool in the corresponding machining process and the changes in the tool status between different machining processes, thereby realizing the characterization of the tool status.

2. The tool chipping monitoring visualization method based on petal graph according to claim 1 is characterized in that: In step A, the data set contains normal machining data and data after tool edge breakage.

3. The tool chipping monitoring visualization method based on petal graph according to claim 1 is characterized in that: In step B, the intercepted signal duration covers 30-50 tool rotation cycles.

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