Sensorless tool soundness monitoring

Abstract

To provide a sensorless method for cutting tool soundness monitoring which continuously evaluates the soundness of a cutting tool and does not require addition of a sensor to a machine tool.SOLUTION: During cutting work of a machine tool, time-series data of one or plural machine tool parameters such as a spindle torque or servo motor speed is collected and transformed to a frequency domain. A magnitude of data at a spindle frequency is divided by a magnitude of a reference data set for the same parameters at the spindle frequency. A ratio is designated as a tool breakage indicator. The tool breakage indicator is monitored in order to discriminate an arbitrary increase in a value. The value is compared with a predefined threshold. Based on the value of the tool breakage indicator and / or a change rate of the value, various criteria for triggering exchange of the cutting tool can be defined.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates generally to the field of cutting tool health monitoring, and more particularly to a method for tool health monitoring that collects time series data of a machine tool signal, such as spindle torque, transforms the signal into the frequency domain, and calculates an index that is the magnitude at the spindle frequency divided by the magnitude of a reference data set at the spindle frequency, where an index above a defined threshold indicates cutting tool health degradation. [Background technology]

[0002] 2. Description of Related Art The use of computer-controlled devices to perform machine operations, such as drilling and milling, on parts is known in the art. In some applications, computer numerically controlled (CNC) machines are used that move the tool along three principle directions, with or without changes in tool orientation. In other applications, multi-axis industrial robots are fitted with machining heads that allow the robot to move the tool along any spatial path while also controlling the tool orientation to any desired value. Summary of the Invention [Problem to be solved by the invention]

[0003] Regardless of the type of machine tool or robot used to perform a machining operation, the quality of the finished workpiece is always important, and conditions that may be detrimental to the quality or life of the machine tool must be avoided. The health of machine tool cutting bits is a particularly concerning issue, as deterioration in the health of cutting tools not only reduces the quality of the workpiece finish, but can ultimately lead to tool failure, causing machine tool downtime and, in severe cases, requiring repairs.

[0004] Techniques for monitoring cutting tool health are known in the art. One very basic technique is to simply track the cumulative cutting work performed by a particular cutting tool and replace the cutting tool after the amount of cutting work (number of cuts, total length / depth of cut, etc.) reaches a predetermined threshold. This technique is simple but inaccurate because it does not consider the actual tool health, which may be better or worse than predicted by the cutting history. Another existing technique uses add-on sensors, which can be contact or non-contact, to measure the actual cutting tool condition. This technique is more accurate than some other techniques, but the additional sensors and integration with the machine tool controller add cost and complexity to this type of solution.

[0005] Yet another known tool health monitoring technique measures spindle current and compares this parameter to a predetermined breakage threshold. This technique is sensorless, but is not always sensitive to changes in tool health.

[0006] In view of the above, there is a need for an improved cutting tool health monitoring method that does not require external sensors and that can reliably detect degradation in cutting tool health before tool damage or part breakage occurs. [Means for solving the problem]

[0007] This disclosure describes a method for cutting tool health monitoring that continuously assesses cutting tool health and does not require additional sensors on the machine tool or its environment. During a machine tool cutting operation, time-series data of one or more machine tool parameters, such as spindle torque or servo motor speed, is collected and converted to the frequency domain. The magnitude of the data at the spindle frequency is divided by the magnitude of a reference data set for the same parameter at the spindle frequency, where this ratio is designated as a tool breakage indicator. The tool breakage indicator is monitored over time to identify any increase in value, which is also compared to a predefined threshold. Based on the value of the tool breakage index and / or the rate of change of that value, various criteria can be defined to trigger cutting tool replacement.

[0008] Additional features of the disclosed systems and methods will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram of a system including a computer controlled machine tool for performing machining operations on a workpiece of a type applicable to the techniques of this disclosure. [Figure 2] FIG. 2 is a graph of tool wear on the vertical axis against cutting time on the horizontal axis illustrating concepts used in the development of embodiments of the present disclosure. [Figure 3] FIG. 3 is a diagram illustrating a healthy cutting tool cutting a workpiece, along with a simplified frequency response graph for the healthy cutting tool, illustrating concepts used in the development of embodiments of the present disclosure. [Figure 4] FIG. 4 illustrates a damaged cutting tool cutting a workpiece along with a simplified frequency response graph for the damaged cutting tool illustrating concepts used in the development of embodiments of the present disclosure. [Figure 5A]FIG. 5A is a graph of spindle torque time series data against time to tool failure. [Figure 5B] FIG. 5B is a graph of a tool breakage indicator versus time, where the tool breakage indicator is calculated using the spindle torque time series data from FIG. 5A according to one embodiment of the present disclosure. [Figure 6] FIG. 6 is a flowchart of a method for sensorless tool health monitoring, including calculation and analysis of tool breakage indicators, according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0010] The following description of embodiments of the present disclosure directed to sensorless cutting tool health monitoring is merely exemplary in nature and is in no way intended to limit the disclosed devices and techniques or their application or uses.

[0011] Cutting tool health is critical in machine tool operations, as degradation of cutting tool health can lead to poor workpiece quality, tool breakage, and even damage to the machine tool itself. This disclosure describes techniques for continuously monitoring cutting tool health using data readily available to the machine tool controller, without the need for additional external sensors or other data acquisition equipment.

[0012] FIG. 1 is a schematic diagram of a system 100 including a computer-controlled machine tool that performs machining operations on a workpiece, of a type applicable to the techniques of this disclosure. The machine tool 110 rotates a spindle 112 to which a cutting tool, in this case an end mill 120, is fixed. The machine tool 110 causes the end mill 120 to perform a machining operation on a workpiece 130. The machine tool 110 is in communication with a controller 140, which is a computing device that provides motion commands and spindle motor speed commands to the machine tool 110. In a typical example, the machine tool 110 moves the rotating end mill 120 from a starting point along a path that cuts material from the workpiece 130, moves the end mill 120 away from the workpiece 130, returns the end mill 120 to a position near the starting point, and then makes another pass that cuts more material from the workpiece 130. The end mill 120 is shown in more detail in the inset, and the teeth or flutes are visible on the tip 122. In this example, the end mill 120 includes four teeth or flutes, also known as cutting edges.

[0013] As described in detail below, the techniques of the present disclosure are applicable to the system 100 of Figure 1. Specifically, the tool health monitoring method of the present disclosure can be programmed in the controller 140 using data readily available in existing controller architectures. No sensors, microphones, or other data collection devices are required for data collection, and no integration of a separate data collection or sensor subsystem with the controller 140 is required.

[0014] 1 are shown in a fairly simple manner with machine tool 110 movable in three principle axes of motion, including "vertical" (parallel to the axis of end mill 120) and two "horizontal" directions (orthogonal to the axis of end mill 120). It should be understood that the cutting tool health monitoring methods of the present disclosure are applicable to any type of machine tool where cutting tool health is a concern, including, for example, multi-axis machines with tool positioning and orientation capabilities, and robotically controlled mills and drills with articulated robotic arms that provide full tool positioning and orientation flexibility.

[0015] 2 is a graph 200 of tool wear on the vertical axis against cutting time on the horizontal axis illustrating concepts used in the development of embodiments of the present disclosure. Graph 200 shows a typical life cycle of a cutting tool, where curve 202 plots tool wear that is intended to represent the actual amount of physical wear that would be found by stopping a machining operation and measuring the condition of the cutting tool, particularly the cutting edges.

[0016] When a new cutting tool is installed on a machine tool, it first undergoes an initial wear phase, indicated by double-headed arrow 210 in FIG. 2 . During the initial wear phase, the very sharp tip of the cutting edge wears fairly rapidly, but only slightly, as indicated by the initial shape of curve 202. During the steady-state wear phase, indicated by double-headed arrow 220, the cutting edge of the machine tool wears very slowly and fairly steadily. During the steady-state wear phase, machine tool performance is predictable, and workpiece finish quality is good. By the end of the steady-state wear phase, the cutting edge has worn to the point where it no longer efficiently cuts workpiece material, which causes higher tool-workpiece impact forces during cutting. Higher impact forces, in turn, result in more rapid wear. During the rapid-wear phase, indicated by arrow 230, tool wear increases rapidly in a self-propagating manner until tool failure occurs at point 240.

[0017] The duration of the steady wear phase can vary considerably from cutting tool to cutting tool. For this reason, there is no accurate and reliable way to predict when a tool will fail based solely on the cutting workload. Techniques that attempt to do this must err on the side of caution and require tool replacement based on the shortest known duration of the steady wear phase. This means that cutting tools that still have a fairly good useful life remaining may be replaced.

[0018] Based on insight into tool wear phenomena and how they can be detected, techniques of the present disclosure have been developed to recognize when a cutting tool has entered a rapid wear phase and request a tool change during the end of tool life stage but before the tool breaks.

[0019] FIG. 3 shows a healthy cutting tool cutting a workpiece, along with a simplified frequency response graph for the healthy cutting tool, illustrating concepts used in the development of embodiments of the present disclosure. In FIG. 3, a workpiece 310 is being machined by a cutting tool 320. The cutting tool 320 is rotating, as indicated by the curved arrow, and the cutting tool is moving in the direction indicated by the feed arrow. The cutting tool 320, which corresponds to the end mill 120 of FIG. 1, has four cutting edges 330 (also known as teeth or flutes). All four cutting edges 330 are in good cutting condition, which corresponds to the initial wear phase and steady wear phase of FIG. 2.

[0020] 3, each of the four cutting blades 330 cuts material from the workpiece 310, and for a given feed rate and cutting depth, the amount of material removed by each of the cutting blades 330 is essentially the same. This is indicated by the "1x" on the tip of each of the cutting blades 330, meaning that each of the cutting blades 330 removes the same amount of material. Each time the tip of one of the cutting blades 330 comes into contact with the workpiece 310, there is an impact force on the cutting tool 320 and workpiece 310, as well as a torque applied to the cutting tool 320 that tends to slow the rotation of the cutting tool 320.

[0021] The above forces and torques cause mechanical vibrations in the machine tool and noise in the surrounding environment. When the mechanical vibrations or ambient noise are analyzed, the frequency spectrum generally appears as shown in the right graph 340 of Figure 3. In particular, the frequency response graph shows the spindle frequency (f spindle ) multiplied by the number of cutting teeth (4 in this case), cut For example, if the spindle speed (rotational speed) is 7200 rpm, this is equivalent to 120 Hz (revs / second), so the cutting frequency f cut is 120*4=480 Hz. In graph 340, the dominant peak in the frequency response is indicated by line 350. As shown in graph 340, the significant peak in the frequency response is f cut This can also be seen at harmonics (2x, 3x, etc.) of f. In a frequency response graph using real data, there will be many small spikes at many different frequencies, as will be understood by those skilled in the art. Nevertheless, f cut The major spikes in the magnitude of the response at and its harmonics will dominate.

[0022] FIG. 4 is a diagram of a damaged cutting tool cutting a workpiece, along with a simplified frequency response graph for the damaged cutting tool, illustrating concepts used in the development of embodiments of the present disclosure. In FIG. 4, a workpiece 410 is being machined by a cutting tool 420 in the same manner as described above for FIG. 3. The cutting tool 420 has four cutting edges, but one of the cutting edges (labeled 430) is broken and missing its tip. As shown in FIG. 4, the damage to one cutting edge places an uneven cutting load on the other teeth, which corresponds to the rapid wear phase of FIG. 2.

[0023] 4, cutting blade 430 does not contact workpiece 410 and therefore does not cut any material. This is indicated by the "0x" at the tip of cutting blade 430. This means that the next cutting blade to encounter workpiece 410 will have to cut a disproportionately large amount of material. The next cutting blade (based on the direction of rotation of cutting tool 420) is cutting blade 432, and the "2x" near its tip indicates that cutting blade 430 will not cut any material and will therefore have to cut approximately twice as much material from workpiece 410. In this situation, the impact force and counter rotational torque on cutting tool 420 are much greater for cutting blade 432 than for the other two intact cutting blades.

[0024] When mechanical vibration or ambient noise is analyzed for the damaged cutting tool 420 of Figure 4, the frequency response spectrum generally appears as shown in the graph 440 on the right of Figure 4. In particular, as mentioned above, the frequency response graph shows the spindle frequency (f spindle ), which has a significant peak at the cutting frequency f cut The significant peak in frequency response graph 440 at spindle frequency is due to the large impact when cutting blade 432 strikes workpiece 410, which occurs once per revolution of cutting tool 420 (i.e., at spindle frequency f spindleIn contrast, in Figure 3, each cutting edge experiences approximately the same impact force, and therefore the dominant peak in the frequency response is at the frequency of the cutting edge impact (f cut ) In graph 440, the dominant peak in the frequency response is f spindle at line 450. A significant peak in the frequency response occurs at f (the 2x harmonic is shown by line 452 on graph 440). spindle The frequency response peaks are also seen at harmonics of the cutting frequency (f shown by line 460). cut ) and its harmonics.

[0025] In actual machine tool operation, it has been observed that the onset of significant tool degradation (entry into the rapid wear phase) often begins with the chipping or fracture of one cutting edge (tooth) of the cutting tool, as shown in Figure 4. Therefore, the sensorless tool health monitoring method of the present disclosure is based on tracking the magnitude of the frequency response of one or more machine tool parameters at the spindle frequency.

[0026] In accordance with the techniques of this disclosure, the tool breakage indicator is defined as follows:

number

[0027] When the cutting tool is new, the frequency response at the spindle frequency is low (essentially X) since there are no damaged cutting teeth. refTherefore, for a new tool, the tool breakage indicator value will be approximately 1 (TBI approximately 1). As tool wear progresses and the cutting tip begins to show different amounts of wear and chipping, the impact force at spindle speed also increases, |X(f spindle )| becomes larger. Therefore, the tool breakage indicator in equation (1) increases as tool wear progresses.

[0028] Research into sensorless tool health monitoring has shown that measuring and analyzing frequency response only at the spindle frequency provides the best results, i.e., the clearest indication of stool degradation, and does not include higher harmonic frequencies, as data at harmonic frequencies has issues with signal sensitivity and noise, making analysis of results less clear.

[0029] Above, it was described that X is a current sample of any suitable signal in the frequency domain. In a preferred embodiment, parameter data available to the machine controller is used for X. For example, in the case of a three-axis mill as discussed with respect to FIG. 1, time-series pulse coder (angular position) data of the X, Y, and Z servo motors may be differentiated to obtain servo angular velocity, and the time-series velocity data is converted to the frequency domain (e.g., by FFT). Spindle rotation time-series data may also be converted to the frequency domain and used for X, including spindle speed (from the machine controller), spindle torque command (calculated by the controller), or spindle motor current (as an indicator of torque). Measured vibration or sound data in the frequency domain may also be used for X.

[0030] In some embodiments, only a single data parameter, such as spindle torque, may be used to calculate the tool breakage indicator. In this case, X is (for example) the spindle torque command data converted from a time series to the frequency domain. In another embodiment, the pulse coder data for all three servo motors (differentiated to obtain velocity and converted to the frequency domain) is used along with the spindle torque data in a composite tool breakage indicator calculated as follows:

number

[0031] It should be understood that any individual parameter (e.g., only the X servo motor pulse coder data) can be used to calculate the tool break indicator using equation (1), or any combination of parameters (e.g., the Y servo motor pulse coder data and spindle torque data) can be used to calculate the composite tool break indicator using equations (1) and (2). Equation (2) above uses pulse coder data and spindle torque data from all three servo motors and is only one example. Composite tool break indicators using other calculations can also be calculated as described above.

[0032] In a preferred embodiment, the tool break indicator TBI is continuously calculated and monitored in real time during the machining operation, for example, the tool break indicator may be calculated and the value evaluated every 100 milliseconds (ms). For each new calculation of TBI, a current sample of X data may be taken and used, for example, the servo motor and spindle motor time series data described above may be collected, converted to the frequency domain, and then used in equation (1) to calculate a tool break indicator for each individual parameter. Calculation of a composite tool break indicator from multiple individual tool break indicators may be performed using equation (2). All of the data collection and analysis may be performed by the machine controller.

[0033] As described above, machine tool parameter data is collected, and one or more tool breakage indicators are periodically calculated by the machine controller in real time. The tool breakage indicator values ​​are then analyzed to assess tool health, and alerts can be issued if required by the tool breakage indicator values. The alerts can include any combination of audible alerts, visual alerts, notification messages sent to the machine operator, etc. One example of an alert is a warning issued when the tool breakage indicator has a high, but not critically high, value, such as greater than 3.0 for three seconds. Another example of an alert is an emergency alert, possibly accompanied by an automatic tool shutdown, when the tool breakage indicator has an extremely high value, such as greater than 5.0, in any calculation cycle. The sensorless tool health monitoring algorithm can also be programmed to evaluate individual tool breakage indicators along with the composite tool breakage indicator.

[0034] The tool breakage indicator calculation used in the above-described sensorless tool health monitoring system offers several advantages over prior art techniques. One significant advantage of the disclosed technique is that the tool breakage indicator can clearly and accurately detect tool health degradation when analysis of time series data is not possible.

[0035] FIG. 5A is a graph 500 of spindle torque time series data versus time to tool breakage, and FIG. 5B is a graph 520 of tool breakage indicator versus time, where the tool breakage indicator is calculated using the spindle torque time series data from FIG. 5A according to one embodiment of the present disclosure.

[0036] In graph 500, spindle torque is plotted against time for a machining operation lasting several minutes. The spindle torque is normalized time series data, e.g., a percentage of maximum spindle torque, so the units on the vertical axis are not significant. Tool breakage terminated the machining operation. However, the time series torque data shows little noticeable change in the last few seconds of the machining operation, in the portion of the graph indicated by arrow 510. The subtle changes in graph 500 make it difficult or impossible to detect tool breakage, let alone identify tool health degradation prior to tool breakage, using the time series torque data.

[0037] In graph 520, the tool breakage indicator is plotted against time for the same machining operation as in FIG. 5A. Specifically, the tool breakage indicator plotted in graph 520 was calculated from the time-series spindle torque data of FIG. 5A. In FIG. 5B, the tool breakage indicator can be seen to progress steadily, having a value of approximately 1.0 for most of the machining operation. However, near the end of the machining operation, the tool breakage indicator jumps to a much higher value as the tool becomes significantly damaged and then breaks. A rapid increase in the tool breakage indicator is evident in the portion of the graph indicated by arrow 530 in the last few seconds of the machining operation. The initial rapid increase in the tool breakage indicator can allow the machine controller to stop the machining operation (i.e., stop feeding the cutting tool and turn off the spindle motor), thereby preventing an eventual cutting tool breakage event and, in some cases, avoiding damage to the machine tool or even a dangerous debris situation.

[0038] While existing tool health monitoring systems using time series torque data can detect tool degradation or breakage in some cases, FIGS. 5A and 5B clearly demonstrate that the tool breakage indicator of the present disclosure can detect tool degradation and breakage in situations that time series-based methods cannot.

[0039] The sensorless tool health monitoring system of the present disclosure also provides other advantages over the prior art. One such advantage is that the tool breakage indicator calculated according to equation (1) is proportional to the spindle frequency f spindle The aim is to evaluate the magnitude of the frequency response at f spindle is f cut and its harmonics, and because those higher frequencies are not analyzed, the data sampling rate can be reduced without loss of accuracy. The lower data sampling rate allows the calculations of the present disclosure to be performed with fewer computational resources (CPU power, memory, and storage) required by the machine controller.

[0040] The sensorless tool health monitoring system of the present disclosure has also been demonstrated to be effective in monitoring the health of small tools, such as cutting tools having diameters in the range of approximately 1.5 mm to 3 mm, using tool breakage indicators. Because the cutting loads (and therefore spindle torque values) on small tools are very low, tool health monitoring tools using time-series torque data do not reliably detect tool defects, as experimental data show little change in normalized torque between damaged and new tools. In contrast, experiments have shown that the tool breakage indicator increases significantly from new to mildly damaged tools, and further increases from mildly damaged to more severely damaged tools. Similar to the results discussed above with respect to Figure 5, the results using small tools show a significant decrease in spindle frequency f spindle This shows the effectiveness of the tool breakage indicator calculated by

[0041] 6 is a flowchart 600 of a method for sensorless tool health monitoring, including calculation and analysis of tool breakage indicators, according to an embodiment of the present disclosure. The method of FIG. 6 is performed during a machining operation on a machine tool and, in a preferred embodiment, is performed by an algorithm running on a machine controller in communication with the machine tool. The method may also be performed by another computing device, such as one that receives data from the machine controller or measures other parameter data, as described above.

[0042] In box 602, data is collected for one or more machine tool parameters. In a preferred embodiment, the data is time-series data that is available and known to the machine controller, such as spindle torque or speed (measured or calculated / estimated) and / or servo motor position or speed data. The data measured in box 602 can also include, for example, sound data recorded in the machine tool environment or mechanical vibration data measured by an accelerometer mounted on or proximal to the machine tool. The data samples recorded in box 602 preferably have a defined duration, such as 50 or 100 milliseconds (ms). Any suitable duration of data collection can be used.

[0043] In box 604, the time series data is transformed into the frequency domain, such as by performing a Fast Fourier Transform calculation. Transforming samples of time series data into the frequency domain to generate a frequency response spectrum is known in the art. If the data collected in box 602 is already in the frequency domain, such as sound or vibration data recorded by a frequency spectrometer, box 604 is bypassed. In box 606, in the first machining operation after a new tool is installed, the frequency response data of the data sample(s) is stored as reference data for future use in calculating the tool breakage indicator. The time series data of the new tool may be stored for reference, and / or the frequency response data may be stored. However, ultimately, it is the spindle frequency f that needs to be saved as a reference. spindle This is the magnitude of the frequency response at the denominator (|X ref (fs pindle )|). If more than one machine tool parameter is being recorded and analyzed (for example, all three servos and the spindle), reference data is stored for each of the parameters.

[0044] In box 608, a tool break indicator TBI is calculated for one or more parameters for which data is collected. The tool break indicator is calculated using equation (1), as described above. If more than one machine tool parameter (e.g., all three servos and the spindle) has been recorded and analyzed, a tool break indicator for each parameter is calculated in box 608, and a composite tool break indicator may be calculated from the individual tool break indicators using equation (2), as discussed above. The calculation of the tool break indicator in box 608 is performed using the parameters (|X ref (f spindle )|), along with the stored reference data for each parameter (the magnitude of the frequency response at the spindle frequency, i.e., |X(f spindle Use the current data for )|).

[0045] In box 610, the tool breakage indicator value is analyzed according to any desired criteria. For example, if only one machine tool parameter (such as spindle torque) is measured, the calculated value of the tool breakage indicator may be compared to first and second thresholds, where the first threshold may trigger a warning while allowing the machining operation to continue, and the second (higher) threshold may trigger a critical warning and command a machine tool shutdown. Also, in box 610, a tool breakage indicator trend may be analyzed, such as the tool breakage indicator value and / or the rate of change of the tool breakage indicator value exceeding a third (lower) threshold for a period of time. If more than one machine tool parameter is measured, a tool breakage indicator for each parameter is evaluated in box 610, and a composite tool breakage indicator may also be calculated and evaluated. Each individual tool breakage indicator and composite tool breakage indicator may have different criteria for triggering a warning or action, as found appropriate by the machine tool operator.

[0046] At decision diamond 612, the process branches to the next step depending on the status of the tool break indicator analysis. If at box 610 the tool break indicator (or tool break indicators) are found to be normal, the process returns from decision diamond 612 to box 602 to obtain another data sample. The return from decision diamond 612 to box 602 can be programmed to occur on a periodic time basis, such as, for example, every 100 ms or 500 ms.

[0047] If in box 610 the tool breakage indicator is found to be above normal but not extremely high, the process proceeds from decision diamond 612 to box 614 where a warning is issued and then the process returns to box 602 to obtain another data sample. The warning issued in box 614 is designed to alert the machine operator that the tool health may be deteriorating and further investigation and action may be required. The warning may be an audible warning, a visual signal, an electronic message to a computer, controller or mobile device, or a combination of these warning types.

[0048] If the tool breakage indicator is found to be too high in box 610, the process proceeds from decision diamond 612 to box 616 where a warning is issued and the machining operation is paused. A severe warning and machine stop action in box 616 is taken when the tool breakage indicator indicates a severely damaged tool where breakage may be imminent. The actions taken in boxes 614 and 616 are merely exemplary, and machine tool operators can choose to design the analysis criteria and resulting actions in box 610 in any manner deemed appropriate for their business. In all cases, calculation of the tool breakage indicator using the disclosed techniques provides insight into tool health and degradation that is unavailable with existing methods.

[0049] Various computers and controllers have been described or implied throughout the foregoing discussion. It should be understood that the software applications and modules of these computers and controllers execute on one or more electronic computing devices having a processor and memory modules. This includes, among other things, the machine controller 140 of FIG. 1 discussed above. Specifically, the processor within the controller 140 is configured to perform the sensorless tool health monitoring described above, including the method steps of FIG. 6 and calculations using the equations and other techniques described above, along with control of the machine tool itself. Some or all of the sensorless tool health monitoring may also be performed by a separate computing device in communication with the machine controller, as described above.

[0050] While several exemplary aspects and embodiments of the method for sensorless tool health monitoring have been described above, those skilled in the art will recognize modifications, permutations, additions, and combinations thereof. Accordingly, the appended claims, and any claims hereafter introduced, are intended to be interpreted as including all such modifications, permutations, additions, and subcombinations as fall within their true spirit and scope.

Claims

1. 1. A method for sensorless tool health monitoring, the method comprising: collecting data of machine tool parameters during a machining operation by a machine controller; transforming the data into the frequency domain to generate a frequency response spectrum; calculating a tool breakage indicator as the magnitude of the frequency response spectrum at the spindle frequency divided by the magnitude of a reference frequency response spectrum at the spindle frequency; comparing the tool breakage indicator to one or more predetermined thresholds; taking corrective action by a machine controller if the tool breakage indicator exceeds one or more of the predetermined thresholds; A method for providing the above.

2. 2. The method of claim 1, wherein the machine tool parameters are spindle torque command data or machine tool positioning servo motor position data that are differentiated to generate servo velocity data before transforming to the frequency domain.

3. 2. The method of claim 1, wherein the data for the machine tool parameters is collected over a predetermined period of time, and wherein collecting the data and calculating the tool breakage indicator is repeated periodically during the machining operation.

4. The method of claim 1 , wherein the spindle frequency is equal to the spindle rotation speed in revolutions per second.

5. The method of claim 1 , wherein the reference frequency response spectrum is generated from a reference data set of machine tool parameters during the machining operation when the cutting tool was new.

6. 2. The method of claim 1, wherein the tool breakage indicator indicates a cutting tool health, a higher value of the tool breakage indicator indicates a poorer cutting tool health, and at least one of the predetermined thresholds is in the range of 1.5 to 3.

0.

7. 2. The method of claim 1, further comprising collecting data regarding at least one other machine tool parameter during the machining operation, calculating the tool broken indicator for the at least one other machine tool parameter, comparing the tool broken indicator for the at least one other machine tool parameter with one or more other predetermined thresholds, and taking corrective action when any of the tool broken indicators exceeds any of its associated thresholds.

8. 8. The method of claim 7, wherein individual tool breakage indicators are calculated for spindle torque command data and position data of at least one tool positioning servo motor, wherein the position data is differentiated to generate velocity data before converting to the frequency domain.

9. 9. The method of claim 8, further comprising: calculating a composite tool breakage indicator using a square root of the sum of squares calculation involving each of the individual tool breakage indicators; and taking corrective action if the composite tool breakage indicator exceeds an associated threshold.

10. The method of claim 1 , wherein the corrective action includes issuing a warning when any of the thresholds is exceeded and stopping the machining operation when a highest one of the thresholds is exceeded.

11. 1. A method for sensorless tool health monitoring, the method comprising: collecting data by a machine controller regarding parameters of a plurality of machine tools during a machining operation; transforming the data into the frequency domain to generate a frequency response spectrum for each of the parameters; calculating, for each said parameter, a tool breakage indicator as the magnitude of its frequency response spectrum at spindle frequency divided by the magnitude of a reference frequency response spectrum at said spindle frequency, wherein said reference frequency response spectrum was generated from a reference data set for said parameter during a machining operation when the cutting tool was new; comparing the tool breakage indicator to one or more predetermined thresholds; taking corrective action by a machine controller if any of the tool breakage indicators exceed any of the predetermined thresholds; A method for providing

12. 12. The method of claim 11 , wherein the parameters of the plurality of machine tools include spindle torque command data and position data of at least one tool positioning servo motor, and wherein the position data is differentiated to generate velocity data prior to transforming to the frequency domain.

13. 13. The method of claim 12, further comprising: calculating a composite tool breakage indicator using a square root of the sum of squares calculation involving each of the tool breakage indicators; and taking corrective action if the composite tool breakage indicator exceeds an associated threshold.

14. 1. A system for sensorless machine tool health monitoring, comprising: a machine tool configured to perform an operation on a workpiece; a computing device in communication with the machine tool, the computing device configured to monitor the health of the cutting tool by performing the steps; wherein the step comprises: collecting data on machine tool parameters during the operation; transforming the data into the frequency domain to generate a frequency response spectrum; calculating a tool breakage indicator as a magnitude of a frequency response spectrum at a spindle frequency divided by a magnitude of a reference frequency response spectrum at said spindle frequency; comparing the tool breakage indicator to one or more predetermined thresholds; taking corrective action including issuing an alert or stopping operation by the machine tool if the tool breakage indicator exceeds one or more of the predetermined thresholds; Including, the system.

15. 15. The system of claim 14, wherein the machine tool parameters are spindle torque command data or machine tool positioning servo motor position data that are differentiated to generate servo velocity data before transforming to the frequency domain.

16. 15. The system of claim 14, wherein the data for the machine tool parameters is collected over a predetermined period of time, and wherein collecting the data and calculating the tool breakage indicator is repeated periodically during the operation.

17. 15. The system of claim 14, wherein the reference frequency response spectrum is generated from a reference data set of the machine tool parameters during the operation when the cutting tool was new.

18. 15. The system of claim 14, further comprising: collecting data for at least one other machine tool parameter during the operation; calculating the tool breakage indicator for the at least one other machine tool parameter; comparing the tool breakage indicator for the at least one other machine tool parameter with one or more other predetermined thresholds; and taking corrective action when any of the tool breakage indicators exceeds any of its associated thresholds.

19. 20. The system of claim 18, wherein individual tool breakage indicators are calculated for spindle torque command data and position data of at least one tool positioning servo motor, wherein the position data is differentiated to generate velocity data prior to conversion to the frequency domain.

20. 20. The system of claim 19, further comprising: calculating a composite tool breakage indicator using a square root of the sum of squares calculation including each of the individual tool breakage indicators; and taking corrective action if the composite tool breakage indicator exceeds an associated threshold.

Images