Sensor-based feed optimization

Abstract

It provides sensor-based feed optimization. [Solution] A method for optimizing cutting tool feed rates involves collecting acceleration data for a reference machining cycle and calculating an optimized feed rate profile, where the rate is increased at locations in the machining cycle where the acceleration is below a target. The acceleration parameter calculated from the acceleration data represents the cutting tool force. Time-series data is recorded for the reference machining cycle, including the tool center position and axial acceleration values. The target value of the acceleration parameter is the maximum value from the reference cycle. An optimal feed rate profile is calculated, where, for each time step at the tool position, the new feed rate is the ratio of the target value to the original feed rate × acceleration parameter. The controller uses the optimal profile in the machining operation. Feedback control may be incorporated into the controller, and the feed angle may be taken into consideration in the acceleration parameter calculation.

Description

[Technical Field]

[0001] This disclosure relates in general to the field of cutting tool feed rate control, and more specifically to a method for feed rate optimization, the method which collects axial acceleration data as an indicator of cutting force at a number of points in a reference machining cycle, calculates an optimized feed rate profile, in the optimized feed rate profile, the feed rate is increased at locations in the machining cycle where the acceleration, and therefore the cutting force, is below a target value, and vice versa.

[0002] Description of related technologies Using computer-controlled devices to perform machining operations such as drilling and milling on parts is well known in the art. In some applications, computer numerical control (CNC) machines that move tools along three principal directions are used with or without changes in tool orientation. In other applications, multi-axis industrial robots equipped with machining heads can move tools along arbitrary spatial paths, while also controlling the tool orientation to any desired value.

[0003] Regardless of the type of machine tool or robot used to perform the machining operation, the quality of the finished workpiece is always important, and conditions that could be detrimental to the quality of the workpiece or the lifespan of the machine tool must be avoided. At the same time, machine productivity is also extremely important for manufacturers who need to provide cost-competitive products. Therefore, the feed rate of the cutting tool must be determined in a way that satisfies both the objectives of quality and productivity.

[0004] In a typical machining operation, the path of a cutting tool when the cutting tool cuts material from a workpiece is defined by a control program. In many machining operations, the shape of the raw workpiece and / or finished workpiece is such that the amount of material cut by the cutting tool changes as the tool moves along the tool path. To avoid damage to the machine tool or the workpiece, a user may refer to a cutting tool catalog to select an appropriate tool feed rate for the tool path based on the cutting depth and the type of workpiece material.

[0005] In conventional machine control programs, a constant feed rate is specified throughout the entire machining operation, which prevents the maximum chip load (and therefore spindle torque) from exceeding an acceptable level. While constant feed rate technology is easy to program, it is also inefficient because the amount of material removed by the cutter varies along the tool path. This means that the cutting speed can be increased at multiple points along the tool path to reduce cycle time while still maintaining an acceptable chip load.

[0006] Techniques for improving cutting tool feed rates are known in the art, but all of these techniques have specific drawbacks and limitations. One known technique involves the use of a simulator, where a three-dimensional model of the workpiece and the machining operation is used to estimate the volume of workpiece material cut at all locations along the programmed tool path, and the cut volume is used to calculate the feed rate at points along the tool path. However, such machining operation simulator systems are expensive, and simulating small-scale machining can be very time-consuming.

[0007] Yet another known tool feed rate improvement technique uses online feedback control that adjusts the feed rate in real time to maintain consistent cutting torque. Although feed rate feedback control can be effective in some applications, a certain amount of overshoot of the target torque value may be unavoidable due to the nature of feedback control. In addition, since machining processes typically vary over time, adjustment of the parameters of a proportional-integral-derivative (PID) controller may not be intuitive.

[0008] In view of the above situation, there is a need for an improved cutting feed rate optimization method that does not require simulation software and can accurately calculate a feed rate profile that satisfies both load management and cycle time requirements in machining operations. Summary of the Invention

[0009] This disclosure describes a method for optimizing cutting tool feed rates, which involves collecting axial acceleration data for a reference machining cycle, calculating an optimized feed rate profile, in which the feed rate is increased at locations in the machining cycle where the acceleration is less than a target value. The axial acceleration amplitude, which can be processed to provide an acceleration parameter, is an indicator of cutting tool force. Time-series data is recorded for a number of time steps of the reference machining cycle, including the tool center point position and the corresponding axial acceleration value. The acceleration parameter target value is determined as the maximum value of the acceleration parameter from the reference machining cycle. A new optimal feed rate profile is then calculated, where, for each time step at the tool center point position, the new feed rate is determined for the time step in the reference machining cycle by multiplying the original feed rate by the ratio of the target value to the acceleration parameter. The optimal feed rate profile is used by a machine controller in production machining operations. Feedback control may be incorporated into the controller to adjust the feed rate in real time based on real-time acceleration readings, and optionally, the feed angle may be taken into consideration in the acceleration parameter calculation.

[0010] Additional features of the systems and methods of this disclosure will become apparent from the following description and the attached claims, in conjunction with the attached drawings. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is a schematic diagram of a system including a computer-controlled machine tool that performs machining operations on a workpiece, of a type applicable to the technology of this disclosure. [Figure 2A] Figure 2A is a diagram illustrating a machine tool cutting operation in which the tool moves at a constant feed rate using conventional programming techniques. [Figure 2B] Figure 2B is a diagram illustrating a machine tool cutting operation according to an embodiment of the present disclosure, in which the tool moves at a feed rate that varies based on the amount of material to be removed. [Figure 3] Figure 3 includes a graph of spindle torque versus time and a graph of feed rate versus time according to an embodiment of the present disclosure, each graph including a data trace for a conventional constant feed rate and a data trace for a feed rate that changes based on the amount of material being removed. [Figure 4] Figure 4 is a block diagram of a system for sensorless feed rate optimization with auxiliary feedback control, according to an embodiment of the present disclosure, which includes providing a reference feed rate from an optimized feed rate profile and adjusting the reference feed rate using feedback control. [Figure 5] Figure 5 is a flowchart illustrating a sensorless feed rate optimization method according to an embodiment of the present disclosure, which includes calculating an optimal feed rate profile based on spindle torque data from a reference machining cycle. [Figure 6] Figure 6 is a plan view of the tool feed angle workspace in an implementation of sensor-based feed rate optimization according to an embodiment of the present disclosure, where a plurality of sectors are defined, and a target value is selected at each time step based on which sector the tool feed angle vector falls into. [Figure 7] Figure 7 is a flowchart of a sensor-based feed rate optimization method according to an embodiment of the present disclosure, which includes calculating an optimal feed rate profile based on axial acceleration data from a reference machining cycle. [Figure 8] Figure 8 is a diagram of a machining operation provided to illustrate how tool motion creates a scalloped shape on the surface of a workpiece and to illustrate the concepts used in the surface roughness prediction technique of this disclosure. [Figure 9] Figure 9 shows an ideal cutting tool with a perfect shape and a real-world cutting tool with runout, illustrating how tool runout affects the scallop shape of a workpiece after machining and how this effect can be simulated in embodiments of the present disclosure. [Figure 10]Figure 10 is a diagram illustrating how the surface roughness of a workpiece is simulated in embodiments of the present disclosure, showing the path of the tip of the cutting tool groove and the corresponding surface profile in the operating plane. [Figure 11] Figure 11 is a flowchart illustrating a method for calculating a machining feed rate profile according to an embodiment of the present disclosure, which includes first performing sensorless or sensor-based feed rate optimization, and then, if necessary, evaluating surface roughness and adjusting the feed rate profile. [Figure 12] Figure 12 shows a graphical user interface (GUI) screen of a software application configured to optimize the feed rate of a machining operation according to an embodiment of the present disclosure. [Figure 13] Figure 13 shows the graphical user interface (GUI) screen of Figure 12 after the feed rate optimization calculation has been performed according to an embodiment of this disclosure. [Figure 14] Figure 14 shows a workpiece machined by a toolpath defined by a program including a command line with sparse control point spacing, and a workpiece having a toolpath again defined with increased control point density, according to an embodiment of the present disclosure. [Figure 15] Figure 15 shows a GUI screen of a software application configured to optimize feed rate and predict surface roughness for machining operations according to an embodiment of the present disclosure. [Modes for carrying out the invention]

[0012] The following descriptions relating to embodiments of the Disclosure relating to sensorless and sensor-based feed optimization are essentially illustrative and are not intended to limit the devices and technologies of the Disclosure or their applications or uses in any way.

[0013] Feed rates that result in excessively high material removal rates can damage the workpiece and / or the tool, while feed rates that result in excessively low material removal rates can lead to unnecessarily long cycle times in machining operations. Therefore, controlling the feed rate in machine tool operation is extremely important. This disclosure describes a technique for feed rate optimization that does not require an external sensor used with the machine tool, or requires only a simple accelerometer used with the machine tool, and is easily and cost-effectively implemented without relying on expensive and cumbersome simulator software.

[0014] Figure 1 is a schematic diagram of a system 100 including a computer-controlled machine tool that performs a machining operation on a workpiece, of a type applicable to the technology of this disclosure. The machine tool 110 rotates a spindle 112 on 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 communicates with a controller 140, which is a computing device that provides the machine tool 110 with operation commands and spindle motor speed commands. 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 and back to a location near the starting point, and then makes another pass to cut more material from the workpiece 130. The end mill 120 is shown in more detail in an inset, where the teeth or grooves are recognizable at the tip 122. In this example, the end mill 120 includes four teeth or grooves, which are also known as cutting edges. The sensor 150 will be discussed later in relation to sensor-based feed rate optimization techniques.

[0015] As will be described in detail below, the technology of this disclosure is applicable to system 100 in Figure 1. Specifically, the sensorless feed rate optimization method of this disclosure may be programmed in controller 140 using data readily available in existing controller architectures. Sensors, microphones, or other data acquisition devices are not required for data acquisition in some embodiments, and it is not necessary to incorporate a separate data acquisition subsystem or sensor subsystem into controller 140.

[0016] The elements in Figure 1 are depicted in a very simplified manner, where the machine tool 110 is movable in three main operating axes, including a "vertical" direction (parallel to the axis of the end mill 120) and two "horizontal" directions (orthogonal to the axis of the end mill 120). It should be understood that the sensorless and sensor-based feed rate optimization methods of this disclosure are applicable to any type of machine tool, including multi-axis machines having tool positioning and orientation functions, as well as robot-controlled mills and drills having articulated robot arms that provide complete tool positioning and orientation flexibility.

[0017] Figure 2A is a diagram of a machine tool cutting operation in which the tool moves at a constant feed rate using conventional programming techniques. Workpiece 210 is being machined by a cutting tool 220 following a tool path 230 as shown. The machining operation in Figure 2A is of a type that can be performed by the system in Figure 1, where workpiece 210 in Figure 2A corresponds to workpiece 130 in Figure 1, and cutting tool 220 corresponds to end mill 120. Workpiece 210 is shown in Figure 2A in a partially machined state, where the material that will soon be removed from workpiece 210 is shown in a darker shade in the upper right portion of workpiece 210. As shown, the tool path 230 is a tool path such that the amount of material removed from workpiece 210 changes as the cutting tool 220 moves along the tool path 230.

[0018] In conventional computer numerical control (CNC or simply NC) machining operations, the cutting tool feed rate is constant and predetermined to ensure that the chip load (in particular, torque) on the cutting tool 220 remains below a specified target value. The constant feed rate is illustrated by a series of arrows 240 in Figure 2A. Graph 250 includes a plot of material removal rate (MRR) versus time for the machining operation depicted in Figure 200. MRR is the volume of material removed from the workpiece 210 per unit time, e.g., cubic millimeters per minute. MRR is a function of the cutting tool feed rate (or feed amount) and the machining volume (essentially the thickness of the cut [in the vertical direction in Figure 2A] and the axial depth of the cut [in the page-to-page direction]). The torque on the cutting tool 220, which is also the spindle torque, changes approximately proportionally to the MRR, which will be discussed further below. Thus, the vertical axis in Graph 250 is labeled with respect to both MRR and torque.

[0019] When the feed rate is kept constant and the amount of material removed changes across the tool path as shown in Figure 200, the material removal rate (MRR) and spindle torque change in relation to the amount of material removed. This is illustrated in Graph 250. That is, when the amount of material removed is small, as in the leftmost example of the cutting tool 220 in Figure 2A, the MRR and spindle torque are small, as shown at time 252 in Graph 250. The reverse is also true; when the amount of material removed is large, the MRR and spindle torque are also large.

[0020] The constant feed rate used in the machining operation shown in Figure 2A can maintain a spindle torque below a target value, but this constant feed rate does not maximize machine productivity because higher cutting tool speeds can be used at numerous points along the tool path without exceeding the target spindle torque. The technology of this disclosure has been developed to do so using a method that satisfies both spindle torque limiting (and cutting tool force) requirements and machine productivity requirements, does not require external sensors used with the machine tool, and is easy to implement.

[0021] Figure 2B is a figure 260 of a machine tool cutting operation according to an embodiment of the present disclosure, in which the tool moves at a feed rate that varies based on the amount of material being removed. In Figure 2B, the workpiece 210, the cutting tool 220, and the tool path 230 are the same as those in Figure 2A. However, in Figure 2B, the feed rate of the cutting tool 220 changes as the cutting tool 220 moves along the tool path 230. Arrow 270 indicates that the feed rate is reduced when a relatively large amount of material is being removed from the workpiece 210, and arrow 280 indicates that the feed rate is increased when a relatively small amount of material is being removed from the workpiece 210.

[0022] Graph 290 includes a plot of material removal rate (MRR) versus time for the machining operation depicted in Figure 260. Using the techniques of this disclosure described below, the variation in feed rate along the tool path is designed to allow the cutting tool to move faster at shallower depths of cutting, while still satisfying the maximum spindle torque requirement by slowing the cutting tool at deeper depths of cutting. This results in the MRR in Graph 290, which is relatively uniform across the entire machining operation compared to the large variation in MRR in Graph 250. This means that the spindle torque (and therefore the cutter tip load) is also relatively uniform in the variable feed rate machining operation. As a result of the increased feed rate in the area of ​​shallower depths of cutting, the machining operation is completed in a shorter cycle time in the variable feed rate machining operation, which explains why the plotted curve in Graph 290 is shorter (finishes faster) than the curve in Graph 250.

[0023] Figures 2A and 2B and their corresponding graphs are conceptual in nature and are provided to illustrate the conventional constant feed rate and the feed rate optimization concepts of this disclosure. Details of the methods of this disclosure, and the results that depict the same characteristics as those shown in Figures 2A and 2B, are described below.

[0024] The fundamental premise of the feed rate optimization technique described herein is that the cutting torque is substantially proportional to the feed rate of the cutting tool. This means, in other words, T=D / 2(c K tc a sinθ+K te a) (1) This can be understood by considering a cutting torque model expressed as follows: Here, the first term inside the parentheses is the torque due to cutting or shearing of the material from the workpiece, and the second term inside the parentheses is the torque due to tool wear or friction on the workpiece material. In equation (1), T is the cutting torque in the tool, D is the tool diameter, c is the feed distance per tooth, and K tc is a force coefficient associated with cutting / shearing (depending on the workpiece material), a is the axial depth of the cut, θ is the engagement angle of the cutting edge, and K te This is the force coefficient associated with wear / friction.

[0025] Of the two terms in equation (1), the cutting / shearing term is much larger. Therefore, by neglecting the wear / friction term, equation (1) can be simplified as follows:

number

[0026] In equation (2), the only variable on the right side is the tooth-to-tooth feed distance c. All other values ​​are constants relating to a given machining operation (workpiece material, axial depth of cut, and characteristics of the cutting tool—size, shape, and material). Furthermore, for a constant spindle rotational speed, the tooth-to-tooth feed distance c is proportional to the feed rate. Thus, from equation (2), it can be understood that the cutting torque is substantially proportional to the tooth-tooth feed (i.e., T∝c), and therefore, the cutting torque is substantially proportional to the feed rate.

[0027] Based on the relationships described above, the sensorless feed rate optimization technique of this disclosure uses recorded spindle torque command data from a reference machining cycle to calculate the ratio of the target spindle torque to the recorded spindle torque command value at each position step along the tool path, and uses the ratio to calculate a new feed rate to be used at each position step. The new feed rate profile along the tool path estimates the optimal feed rate profile for reasons described above and further explained below.

[0028] A first step in the art of this disclosure is to record time-series spindle torque command data relating to a reference machining cycle. In a preferred embodiment, the machine tool is typically set at a constant feed rate using a pre-programmed NC tool path. As will be understood by those skilled in the art, the tool path may include a number of different cutting steps and "air-cut" steps, in which the cutting tool is repeatedly positioned, cuts material from the workpiece, repositioned, performs the next cut, and so on, until the machining operation is complete. Typically, larger air-cut steps are programmed to be completed using the maximum machine speed and acceleration, so there is no need to modify such air-cut steps for time optimization. Feed rate optimization is performed only on the cutting steps and smaller air-cut steps that exist within the programming of the cutting steps.

[0029] To understand the reference machining cycle, we consider the machining operation depicted in Figure 2A, with a constant feed rate along the tool path 230. At each incremental position of the tool center point along the tool path 230, the spindle torque is recorded along with the tool center point position. This results in a time-series dataset with the spindle torque and tool center point position recorded at each time step. The feed rate along the tool path 230 for the reference machining cycle is also known. The increments of the time steps (i.e., the amount of time and distance between each data point in the time series) may be selected to suit the requirements of the application.

[0030] After the reference machining cycle is completed, the time-series data may be evaluated to determine a maximum value of the spindle torque command over the entire reference machining cycle. This maximum value may be set as the target spindle torque command SPTCMD TARG . Alternatively, the target spindle torque command SPTCMD TARG may be determined based on machine requirements, such as a value equal to 20% of the maximum machine spindle torque. Since the constant feed rate of the reference machining cycle is selected to cause the maximum spindle torque to equal a value based on machine requirements, any of these techniques should result in a similar value for SPTCMD TARG .

[0031] According to the method of the present disclosure, a new feed rate profile is then determined by comparing the spindle torque command value at each point in the reference machining cycle to the target spindle torque command SPTCMD TARG . The new feed rate profile is calculated as follows. [Formula] Here, for each time-series data point (i=1, n) in the reference machining cycle, the new feed rate Feed optim、i is calculated from the ratio of the (known and constant) original feed rate Feed orig and SPTCMD TARG / SPTCMD i . In formula (3), SPTCMD i is the recorded spindle torque command value for the specific time-series data point i. In the new feed rate profile, the new feed rate Feed optim、i is used at the tool center point position (pos i ) corresponding to time step i. This results in a new feed rate profile where an optimal feed rate is defined for each tool center point position along the tool path.

[0032] It should be noted that the new feed rate profile completes the machining cycle in a shorter amount of time than the standard machining cycle (as shown in the graphs in Figures 2A and 2B). Therefore, the new feed rate profile includes a set of positions (along the tool path) and corresponding feed rates. The feed rate profile with positions and feed rates can be converted into a time-step-based operation program, where each time step has a corresponding position and corresponding feed rate in the tool path trajectory.

[0033] Referring again to Figure 2B, it can be understood how equation (3) provides a new feed rate profile that is much more optimal in time than the original constant feed rate, while also ensuring that the commanded spindle torque does not exceed the target maximum value. In the case of the cutting tool 220 immediately after arrow 270, the maximum amount of material has been removed from the workpiece 210. Therefore, the value of the spindle torque command at that point (SPTCMD) i ) is the spindle torque command target value SPTCMD TARG This should be very close, meaning that the feed rate at this point in the new feed rate profile is approximately equal to the original constant feed rate of the reference machining cycle.

[0034] Conversely, at the location of arrow 280, the smallest amount of material is removed from workpiece 210. Therefore, the value of the spindle torque command at that point (SPTCMD) i ) is the spindle torque command target value SPTCMD TARG This is significantly lower, meaning that the feed rate at this point in the new feed rate profile is much higher than the original constant feed rate of the reference machining cycle. The increased feed rate in areas with less material removal allows machining operations to be completed more quickly using the new optimal feed rate profile.

[0035] When calculating a new speed profile using equation (3), certain control parameters may be defined to limit the amount of increase or decrease in feed rate. For example, the feed rate at each point i in the new speed profile may be constrained to a range of 0.8 to 3.0 times the original feed rate. In other words, the new feed rate may be 0.8 times slower than the original feed rate, or it may be up to 3.0 times faster than the original feed rate. These values ​​are merely examples, and any feed rate limit may be chosen to suit a particular application.

[0036] It is emphasized that recorded spindle torque command data for a reference machining cycle does not require a torque sensor in the machine tool or cutting tool. Since the controller monitors the spindle rotation speed and provides torque commands (via motor current) to the spindle motor designed to maintain a target spindle speed, the spindle torque command data is known to the machine controller. Therefore, the above technique is sensorless and utilizes the built-in functions of the machine tool (measuring position and speed), as well as parameter data known to the machine controller.

[0037] In some embodiments, the time-series data of spindle torque commands for a reference machining cycle may be pre-processed to remove the torque of air cut. This is because the sum of the spindle torques is equal to the cutting torque plus the friction torque in the machine tool itself. That is, T total =T cut +T friction Friction torque T friction This is the amount of torque required to rotate the spindle at the cutting speed without any tool-workpiece contact. Therefore, friction torque may be determined during the air-cut step.

[0038] Furthermore, in this embodiment in which the air cut torque is eliminated, the air cut (friction) torque is the target spindle torque command SPTCMD TARGIt is removed. Although the air cut (friction) torque is small compared to the actual cutting torque, removing the friction torque from the calculation of the optimal speed profile may provide slightly better results in some applications.

[0039] After the new feed rate profile is calculated as described above, the new motion program for the machining operation may be prepared by using the original tool path geometry and inserting new feed rate commands for each position point along the tool path. As a result, the motion program will estimate the optimal feed rate profile according to the original tool path, with the feed rate along the tool path being the optimal feed rate profile. The new motion program may acquire the feed rate profile (containing [position, feed rate] data pairs) and convert it into time-series data points for the new motion program using a method known in the art. This new motion program is then used by the machine controller in production machining operations.

[0040] Figure 3 includes a spindle torque versus time graph 300 and a feed rate versus time graph 350 according to an embodiment of the present disclosure, each graph including a data trace for a conventional constant feed rate and a data trace where the feed rate changes based on the amount of material being removed. Graphs 300 and 350 represent results from an experimental implementation of a sinusoidal cutting path operation of the type depicted in Figure 2 using the feed rate optimization technique described above.

[0041] In graph 300, data trace 310 plots spindle torque data for a conventional constant feed rate machining cycle, e.g., the one used for the reference machining cycle in the above description. Data trace 320 plots spindle torque data for a machining cycle created using the feed rate optimization technique described above in the operating program. Line 330 is the target spindle torque command SPTCMD TARG It represents.

[0042] In Graph 300, it can be observed that the spindle torque (data trace 310) for a constant feed rate machining cycle oscillates between a maximum and a minimum value because the amount of material removed from the workpiece changes. The maximum spindle torque value in data trace 310 is, as expected, equal to the target spindle torque. This is the behavior shown in the graph of Figure 2A and described above. In contrast, the spindle torque (data trace 320) for an optimized feed rate machining cycle has peaks and valleys, but the peaks spend more time at the target spindle torque value, and the valleys do not decrease as sharply as those in the constant feed rate data traces.

[0043] In Graph 350, the constant feed rate is shown in data trace 360. In contrast, the feed rate for the optimized feed rate machining cycle (data trace 370) dramatically increases in some machining cycles where the amount of material removed is small, and then decreases back to the feed rate of the conventional constant feed rate machining cycle in some machining cycles where the amount of material removed is large. This is expected behavior considering the feed rate optimization calculation technique described above.

[0044] In the experimental implementation shown in Graphs 300 and 350, the change in maximum feed rate for the optimized machining cycle was set to three times the constant feed rate of the reference machining cycle. This was achieved by applying the feed rate limit as previously described. A threefold increase in feed rate can be observed in data trace 370 compared to data trace 360 ​​in Graph 350. If the feed rate is increased even further (more than three times), this reduces the size of the valley in data trace 320 in Graph 320. However, at some point, excessive feed rate increases result in unacceptably abrupt tool movement (sharper peaks in data trace 370) and / or exceed the limits of the machine's acceleration and jerk.

[0045] The results depicted in Figure 3 clearly demonstrate that the feed rate optimization technique of this disclosure generates machining cycles with significantly less spindle torque variation than those in conventional constant feed rate machining cycles, while still ensuring that the maximum spindle torque does not exceed the target spindle torque value. Most importantly, machining cycles with feed rate optimization are completed in significantly less time than conventional constant feed rate machining cycles. In this experimental implementation, machining cycle time was reduced by more than 40% using the feed rate optimization method of this disclosure. This reduction in cycle time can be seen in both graphs in Figure 3.

[0046] In another experimental implementation, the machining operation machined a workpiece from a solid block of material, the workpiece containing numerous bosses, ribs, and cavities of varying height and thickness, resulting in complex tool-workpiece engagement both inside and outside the workpiece. In this experimental implementation, the maximum feed rate increase for the optimized machining cycle was set to twice the original feed rate. In this example, the machining cycle time was reduced by approximately 20% compared to a constant feed rate machining cycle using the sensorless feed rate optimization method of this disclosure.

[0047] The feed rate optimization technique described above may also be considered a type of feedforward control, where the feed rate change is made in anticipation of an imminent change in the amount of material removed from the workpiece. In another embodiment, the feedforward control using feed rate optimization may be combined with real-time feedback control to provide more robust and effective feed rate management.

[0048] In known feedback control systems for machine tools, spindle torque data, along with other parameters such as spindle temperature, is monitored in real time by the machine controller. When the spindle torque value reaches a predetermined threshold (similar to the target spindle torque command described above), the machine controller reduces the feed rate as needed to lower the spindle torque value below the threshold. Due to the nature of feedback control, some overshoot of the threshold spindle torque value may be unavoidable, especially in high-speed machining operations. The best characteristics of both control strategies may be achieved by combining feedforward and feedback control of the machining operation.

[0049] Figure 4 is a block diagram of a system 400 for sensorless feed optimization with auxiliary feedback control, which includes providing a reference feed rate from an optimized feed rate profile and adjusting the reference feed rate using feedback control, according to an embodiment of the present disclosure. In system 400, a machine controller 410 controls the machine tool 470 in the manner shown in Figure 1 and described above.

[0050] In the feed rate optimization technique of the present disclosure, an operation program including the optimal feed rate for all position points along the tool path is provided to the controller 410 in accordance with the sensorless feed rate optimization of the present disclosure as described in detail above. Then, at all time steps during the actual execution of the machining cycle in the production part, the tool center point position from block 420 is provided to operation program block 430, which determines the optimal feed rate based on the tool center point position along the tool path. In a basic implementation of the feed rate optimization method, the optimal feed rate from block 430 is provided to the machine tool 470, which uses the feed rate as instructed.

[0051] In more advanced implementations, feedback control may be employed by adding elements inside the dashed box 412. In this embodiment, the optimal feed rate from block 430 is provided to the feedback control block 440 as the reference feed rate. The feedback control block 440 calculates the adjusted feed rate based on the difference between the reference spindle torque command value for the current time step (the torque value that the feedback control is targeting, provided from block 450) and the actual torque command from the previous time step (provided as feedback from the machine tool 470 in line 460). The feedback control block 440 may employ PID control or some other feedback control algorithm known in the art.

[0052] If the actual spindle torque command is greater than the reference torque command, the feedback control block 440 calculates an adjusted feed rate that is less than the optimal feed rate from block 430. Conversely, if the actual spindle torque command is less than the reference torque command, the feedback control block 440 calculates an adjusted feed rate that is higher than the optimal feed rate from block 430. In the PID control system, the feed rate adjustment calculation is performed not only on a simple difference but also using proportional, integral, and differential logic. The output from the feedback control block 440 is the adjusted feed rate, which is provided as a command to the machine tool 470. A spindle torque command, designed to keep the spindle rotating at a target rotational speed, is also provided to the machine tool 470.

[0053] The combination of pre-preparation of an optimal feed rate profile, as depicted in Figure 4, and feedback control of the cutting tool feed rate can offer the advantages of both feedforward and feedback control strategies. The feedforward reference rate provides the theoretically optimal feed rate for all points along the tool path, while real-time feedback control makes smaller scale adjustments as needed to prevent cutting torque overshoot, while also allowing feed rate increases where possible. Typically very small adjustments, made by the feedback control module 440, may be required due to reasons such as misalignment of the actual workpiece position from the nominal workpiece position defined in the machine tool motion program. Experimental implementations of this combined control strategy demonstrated a highly effective reduction of spindle torque command overshoot.

[0054] Figure 5 is a flowchart 500 of a sensorless feed rate optimization method according to an embodiment of the present disclosure, which includes calculating an optimal feed rate profile based on spindle torque data from a reference machining cycle.

[0055] In box 502, time-series data relating to a reference machining cycle is collected from the machine tool controller. The time-series data includes, for each time step, the tool center point position along a defined tool path and the corresponding spindle torque command value. In a preferred embodiment, the reference machining cycle is performed using an operation program (e.g., from an NC or CNC) that applies a constant feed rate of the cutting tool along the tool path. If a variable feed rate is used in the reference machining cycle, the feed rate for each time-series data point is recorded along with the tool center point position and spindle torque command.

[0056] In box 504, the spindle torque command target value (SPTCMD) TARGThe spindle torque command target value is determined. The spindle torque command target value may be defined as the maximum value of the spindle torque command over the entire reference machining cycle. Alternatively, the spindle torque command target value may be defined based on machine requirements, such as a value equal to 20% of the maximum machine spindle torque. Either method is used in SPTCMD. TARG This should yield similar values.

[0057] In box 506, the optimal feed rate profile for the tool path is calculated. As previously mentioned, for each time step i in the reference machining cycle dataset, the new optimal feed rate is calculated using equation (3), where the new feed rate is the original feed rate for the time step multiplied by SPTCMD. TARG / SPTCMD i This is equal to the ratio obtained by multiplying by the ratio, where SPTCMD i This is the recorded spindle torque command value for a specific time series data point i. In the new optimal feed rate profile, the new feed rate Feed optim、i This is the tool center point position (pos) corresponding to time step i. i This is used in [the process]. As a result, a new feed rate profile is obtained, where the optimal feed rate is determined at each tool center point position along the tool path.

[0058] As previously mentioned, when calculating the optimal feed rate profile in Box 506, the limiting factors may be applied to increases or decreases in feed rate calculated from Equation (3). For example, a maximum increase or decrease in feed rate of two or three times may be specified. The rate increase factor may differ from the rate decrease factor, and these factors may be selected to suit the requirements of the application.

[0059] As mentioned earlier, when calculating the optimal feed rate profile in box 506, (SPTCMD TARG and SPTCMD iThe spindle torque values ​​(for both) may be pre-treated to subtract the air cut (friction) torque, and as a result, the optimal feed rate calculation is performed directly against the torque associated with material cutting, without including parasitic friction torque.

[0060] In box 508, the motion program is created using the optimal feed rate profile and is used in the machine controller for production machining operations. The motion program used in box 508 includes the optimal feed rates for all position points along the tool path, as calculated in box 506. As a result, the motion program estimates the optimal feed rate profile based on the feed rate along the tool path, according to the original tool path.

[0061] In an alternative embodiment, the machine controller performing the production machining operation in box 508 uses a new operation program with an optimal feed rate profile and also employs a feedback control block, as depicted in Figure 4, to adjust the commanded feed rate based on the actual spindle torque. The use of feedback control enables automatic, real-time fine-tuning of the feed rate to account for unexpected overshoot or undershoot of the target spindle torque, in addition to the optimal feed rate feedforward command.

[0062] The sensorless feed rate optimization technique described above has been shown to be highly effective in maintaining the cutting tool load below a target value while reducing cycle time compared to constant-speed machining. Sensorless techniques are applicable to machining operations where the amount of material removed from the workpiece is sufficient to result in significant fluctuations in spindle torque along the machining trajectory of the tool path. However, in other applications, such as machining small components of electronic devices, the combination of small cutting tool diameters and low material removal rates results in slight fluctuations in spindle torque throughout the machining operation, thus rendering sensorless spindle torque command-based techniques ineffective. In such applications, an accelerometer may be added to the machine tool, and the accelerometer signal is analyzed for the purpose of feed rate optimization. This technique is described below.

[0063] The fundamental premise of sensor-based feed rate optimization technology is that both lateral and axial forces in a cutting tool are generally proportional to the tool feed rate. Even when the cutting tool is moving perpendicular to the tool axis, the cutting action of the tool generates a partially axial force (in addition to the lateral force) because the cutting tool has a helical cutting edge profile (see cutting tool 120 in the inset in Figure 1). Furthermore, if the cutting tool has an axial motion element (such as when descending into the workpiece material), this also generates an axial force that depends on the feed rate.

[0064] The above explanation can be expressed mathematically as follows:

number

[0065] It has been demonstrated that, rather than directly measuring the axial force in the cutting tool, time-series axial acceleration data can be processed and analyzed to detect changes in the axial force in the cutting tool. Therefore, the Z-axis acceleration signal can be used as surrogate data for the axial force in the tool, which then relates to the cutting force in the lateral (X and Y) directions. The variation in cutting force, as determined from the axial acceleration signal, can be used to calculate the optimal feed rate profile.

[0066] Referring to Figure 1, the sensor 150 is mounted on the machine tool 110. In a preferred embodiment, the sensor 150 is an acceleration sensor, more specifically, an acceleration sensor configured to measure time-series acceleration data in the axial direction (parallel to the spindle axis, also known as the Z direction, as indicated by the arrow). The sensor 150 collects time-series data during the machining operation and provides the acceleration data to the controller 140, where the collected data (e.g., Z-axis acceleration data) is correlated with the location of the tool center point along the tool path during the machining operation, and the sensor data is then analyzed to calculate an optimal feed rate profile in a manner similar to the sensorless technology described earlier.

[0067] For the purposes of the following explanation, the acceleration parameter X used in the feed rate optimization calculation is defined. In one embodiment, time-series data from an axial acceleration sensor is used as time-series data X. origThe data is double-integrated and processed with a high-pass filter to generate the X-axis acceleration data. Double integration converts the axial acceleration data into the equivalent axial displacement data that changes with the axial force. High-pass filtering removes drift from the time-series data. Double integration and high-pass filtering convert the raw time-series axial acceleration data into X-axis acceleration data. orig This is merely one example of a data processing technique for generating data. Other techniques may be used as deemed preferable.

[0068] In some preferred embodiments, the acceleration sensor 150 provides data measurements at a rate faster than the time step increments used by the controller 140 to control the machine tool 110. Thus, for every time step of the controller, multiple axial acceleration data points are available, which helps prevent sudden rises and falls in the acceleration data from artificially influencing the acceleration parameter X.

[0069] Next, the moving average or other low-pass filter is applied as follows, i.e., X(t) = movavg(|X orig (t)|) (5) To generate time-series data with respect to the acceleration parameter X, as shown above, X orig This is applied to time-series data. Here, X orig (t) is the time-series acceleration data after the double integration and high-pass filtering (or other data processing) described above, and X(t) is the time-series acceleration parameter data used in the feed rate optimization calculation.

[0070] Similar to the sensorless feed rate optimization technique described earlier, the sensor-based method begins with recording time-series data related to a reference machining cycle, in this case, axial acceleration data for each point along the tool path. In a preferred embodiment, the machine tool is typically set to a constant feed rate using a pre-programmed NC tool path. During the reference machining cycle, at each incremental position of the tool center point along the tool path 230 (Figure 2), the axial acceleration is recorded along with the tool center point position. This results in a time-series dataset having the axial acceleration and tool center point position recorded at each time step. The feed rate along the tool path 230 for the reference machining cycle is also known. The increments of the time steps (i.e., the amount of time and distance between each time-series data point) may be chosen to suit the requirements of the application.

[0071] After the reference machining cycle is completed, time-series data may be evaluated to determine the maximum value of the acceleration parameter X (calculated from axial acceleration data) over the entire reference machining cycle. This maximum value is the target acceleration amplitude value X. TARG It may be defined as such.

[0072] According to the sensor-based method of this disclosure, the new feed rate profile is then calculated by comparing the axial acceleration amplitude value at each point in the reference machining cycle with the target acceleration amplitude value X. TARG It is determined by comparison. The new feed rate profile is calculated as follows:

number

[0073] X i <X TARG At points along the tool path, the optimal feed rate can be increased beyond the original feed rate to improve cycle time. i >X TARG At points along the tool path, the optimal feed rate is reduced from the original feed rate to prevent excessive load on the cutting tool. The limit may be set in the amount of increase or decrease in feed rate observed in the optimal feed rate profile, as previously stated in relation to equation (3).

[0074] Sensor-based feed rate optimization works in the same way and for the same reasons as previously described with respect to Figure 2. That is, in portions of the tool path where the material removal rate is low, the feed rate may be increased relative to the nominal value, and vice versa. When applied to the same type of sinusoidal tool path as depicted in Figure 2, the result of the sensor-based solution is an optimized feed rate profile similar to that depicted in Figure 3 and previously described, where a large portion of the machining cycle operates at a feed rate significantly higher than the nominal constant feed rate. It is again emphasized that this feed rate optimization is achieved using sensor-based technology even without the use of spindle torque command data, since the machine controller cannot detect spindle torque fluctuations at all.

[0075] It should be noted again that the new feed rate profile completes the machining cycle in a shorter amount of time than the standard machining cycle (as shown in the graphs in Figures 2A and 2B and as previously mentioned). In the example described above, the sensor-based technology generated a feed rate profile that completed the machining operation in approximately 32% less time than a machining cycle at a constant feed rate. The new feed rate profile includes a set of positions (along the tool path) and corresponding feed rates that can be used by the controller in any preferred manner to control the machine tool movement according to the feed rate profile.

[0076] The advantages of combining feed rate feedback control with feedforward control were previously discussed in relation to sensorless feed rate optimization technology, and a block diagram of such a system is shown in Figure 4. This same technique may also be employed in sensor-based feed rate optimization. Note that in this case, instead of using actual torque commands as feedback in line 460 (which are known data to the machine controller), it is necessary to use real-time axial acceleration signals from sensor 150 for feedback.

[0077] In some embodiments of sensor-based feed rate optimization techniques, additional features may be added to address the problem of cross-coupling of direction-dependent vibrations in the machine tool. Consider a machine coordinate system in which the Z-axis is parallel to the spindle axis (e.g., vertical) as described above, and the X and Y axes are oriented in the "forward" and "lateral" cutting directions (e.g., in the horizontal plane). Considerations in machine tool design stipulate that the structure and mechanism for tool movement in a certain direction (e.g., X) differ from those for tool movement in a perpendicular direction (e.g., Y). This means that the stiffness / flexibility characteristics differ in the X and Y directions, causing direction-dependent vibrations in the machine tool. These vibrations may also have a cross-coupling effect, which also depends on the machine tool design. In other words, vibrations in the X direction may feel less pronounced in the Y and Z directions, and vice versa. Furthermore, vibrations in the X direction result in a different vibration signature in the Z direction than those produced by vibrations in the Y direction.

[0078] In other words, the acceleration in the Z direction resulting from cutting in the Y direction can be very different from the acceleration in the Z direction resulting from cutting in the X direction. Since sensor-based feed rate optimization techniques are based on the magnitude of acceleration in the Z direction, it may be advantageous to consider the cutting direction when processing acceleration data. This involves dividing the complete tool path trajectory into feed angle groups and considering the X for each feed angle group. TARG This can be done by using the intrinsic values ​​of the object.

[0079] Figure 6 is a plan view of the tool feed angle workspace in an implementation of sensor-based feed rate optimization according to an embodiment of the present disclosure, where a plurality of sectors are defined, and a target value is selected at each time step based on which sector the tool feed angle vector falls into. Figure 6 shows the X-axis direction 610 and the Y-axis direction 620 of the tool coordinate system. In the tool coordinate system, the Z-axis is parallel to the axis of the cutting tool and points downward. These definitions are consistent with the coordinate directions used throughout the present disclosure. The X and Y directions in Figure 6 relate to tool velocity, not position, as described below.

[0080] Quadrant 630 is defined for a portion of the machine tool feed angle workspace, where the cutting tool feed angle has a positive X component and a positive Y component. The feed angle is the speed or direction of the cutting tool's movement at any specific point along the tool path. Quadrant 630 is divided into three sectors: sector 640 depicts the cutting tool feed angle from 0° to 30° from the X axis, sector 650 depicts the feed angle from 30° to 60° from the X axis, and sector 660 depicts the feed angle from 60° to 90° from the X axis. During the reference machining cycle, the feed angle at each time step is recorded along with the corresponding axial acceleration data. TARG Different values ​​are calculated for sectors 640, 650, and 660, respectively. That is, the target value

number

[0081] Next, in order to calculate the optimal feed rate profile, equation (6) is used for each point i as described above, but X TARGThe value of is selected for an appropriate set of feed angles with respect to point i. For example, for a point in a machining cycle trajectory where the cutting tool has a feed angle vector 642, the time-series acceleration data parameter X i This is the target value for sector 640 corresponding to the feed angle vector 642.

number

[0082] The method for determining the feed angle group described above for quadrant 630 may, of course, be applied similarly to feed angles in other quadrants (negative X and / or Y velocity directions). The other quadrants may be symmetrical to quadrant 630 or may be uniquely defined. Furthermore, the 30° sector is merely a non-limiting example, and more or fewer than three sectors may be included in each quadrant, and the sectors do not all have to be the same size. The sector sizes for the feed angle group may be determined to meet the requirements of the application and may be based on the machine tool characteristics.

[0083] Figure 7 is a flowchart 700 of a sensor-based feed rate optimization method according to an embodiment of the present disclosure, which includes calculating an optimal feed rate profile based on axial acceleration data from a reference machining cycle.

[0084] In box 702, time-series data relating to a reference machining cycle is collected from the machine tool controller, including axial acceleration data from sensors attached to the machine tool. The time-series data includes, for each time step, the tool center position along a defined tool path and the corresponding axial acceleration value. In a preferred embodiment, the reference machining cycle is performed using an operation program (e.g., from an NC or CNC) that applies a constant feed rate of the cutting tool along the tool path. If a variable feed rate is used in the reference machining cycle, the feed rate for each time-series data point is recorded along with the tool center position and axial acceleration value.

[0085] Axial acceleration data from the sensor may be provided at a higher frequency than the controller's control cycle frequency. In other words, for every time step of the controller controlling the machine tool in the tool path, multiple acceleration data points may be received and recorded from the sensor, resulting in averaging or other data processing techniques having more data to consider and therefore being less affected by individual spikes in the acceleration data.

[0086] In box 704, the acceleration-related parameter X is calculated from raw time-series data of axial acceleration. As previously stated, the acceleration parameter X has values ​​at each time-series data point, which may be the peak value of the axial acceleration or the double integral of the acceleration signal. Other data processing techniques may be applied to the raw time-series acceleration data to provide an acceleration parameter X intended to represent the amplitude of the axial acceleration (and thus the force at the cutting tool) at each time step in the machining cycle.

[0087] In box 706, the target value for the acceleration parameter X is determined. The target value is X TARGIt may be specified as the maximum value of the acceleration parameter X over the entire reference machining cycle. In some embodiments, each path point in the machining cycle is assigned to a feed angle group based on the direction of the tool velocity vector, and the target value X TARG This is calculated for each of the feed angle groups.

[0088] In box 708, the optimal feed rate profile for the tool path is calculated. As previously mentioned, for each time step i in the reference machining cycle dataset, the new optimal feed rate is calculated using equation (6), where the new feed rate is the original feed rate for the time step multiplied by X. TARG / X i This is equal to the product of the ratios, where X i This is the value of the acceleration parameter X for a specific time series data point i. In the new optimal feed rate profile, the new feed rate Feed optim、i This is the tool center point position (pos) corresponding to time step i. i This is used in [the process]. As a result, a new feed rate profile is obtained, where the optimal feed rate is determined at each tool center point position along the tool path.

[0089] As previously mentioned, when calculating the optimal feed rate profile in Box 708, the limiting factors may be applied to increases or decreases in feed rate calculated from Equation (6). For example, a maximum increase or decrease in feed rate of two or three times may be specified. The rate increase factor may differ from the rate decrease factor, and these factors may be selected to suit the requirements of the application.

[0090] As mentioned earlier, when calculating the optimal feed rate profile in box 708, each time-series data point may first be assigned to a feed angle group, and each feed angle group is used in equation (6) X TARGEach feed angle group has its own unique value. The processing of the feed angle groups cancels out the effects of cross-coupling of direction-dependent vibrations in the machine tool and can provide improved results in certain applications, such as those in which the machining cycle involves a wide range of feed angles and the machine tool has dramatically different stiffness characteristics in the longitudinal direction compared to the lateral direction.

[0091] In Box 710, the operation program is created using the optimal feed rate profile and used in the machine controller for production machining operations. The operation program used in Box 710 includes the optimal feed rate for all position points along the tool path, as calculated in Box 708. As a result, the operation program estimates the optimal feed rate profile based on the feed rate along the tool path, following the original tool path. In sensor-based solutions, the optimal feed rate profile is generated without processing spindle torque command data, which is essential in applications such as machining electronic equipment where spindle torque fluctuations are too small to be detected.

[0092] In an alternative embodiment, the machine controller performing production machining operations in box 708 uses a new operation program with an optimal feed rate profile and, as previously mentioned, also employs a feedback control block to adjust the commanded feed rate based on the actual axial acceleration values ​​measured in real time. The use of feedback control enables automatic, real-time fine-tuning of the feed rate to account for unexpected overshoot or undershoot of cutting forces, in addition to the optimal feed rate feedforward command.

[0093] The sensorless and sensor-based feed rate optimization techniques described above provide an effective function for creating machining feed rate profiles that minimize machining cycle time while avoiding excessive spindle torque or material removal rates. Another important factor in machining operations is the surface roughness of the finished workpiece. Techniques for predicting the nominal surface roughness in the finished operation are described below.

[0094] Figure 8 is a diagram of a machining operation provided to illustrate how tool motion creates a scalloped shape on the surface of a workpiece and to illustrate the concepts used in the surface roughness prediction technique of this disclosure. Figure 8 shows the same type of machining operation as those depicted in Figures 1 and 2. A machine tool (not shown) moves in the direction indicated by the feed arrow 810. The machine tool has a cutting tool 820 (e.g., an end mill) on its spindle, which is rotating at a spindle speed S and moving at a linear feed speed F. The spindle rotates about the Z axis, and the machine tool moves in a direction perpendicular to the spindle axis, in this case the X direction, as shown by the coordinate system depicted in Figure 8.

[0095] The workpiece 830 is fixed in place and machined by the cutting tool 820. With each rotation of the cutting tool 820, the groove 822 cuts and removes a portion of the material 832 from the workpiece 830. The size and shape of the portion 832 depends on the spindle speed S and feed rate F, along with the diameter of the cutting tool 820 and the number of grooves in the cutting tool 820 (which will be described further below). If the feed rate F is negligibly small, the cutting tool 820 leaves a smooth surface on the workpiece 830. However, because the groove 822 moves in the X direction from one rotation to the next, the cutting tool 820 actually leaves a "scalloped" shape on the machined surface of the workpiece 830, particularly at the bottom of the machined surface indicated by 834.

[0096] Figure 9 shows an ideal cutting tool with a perfect shape and a real-world cutting tool with runout, illustrating how tool runout affects the scallop shape of a workpiece after machining and how this effect can be simulated in embodiments of the present disclosure. The cutting tool 900 on the left has a rotating axis 910. The cutting tool 900 has a perfectly axially symmetric shape, and as a result, each of the grooves 920 has an equal tip diameter, which means that under constant spindle speed and feed rate conditions, each of the grooves 920 cuts the same amount of material from the workpiece with each rotation of the tool. While the cutting tool 900 embodies the idealized tool, in reality, there is always some asymmetry with respect to the cutting tool.

[0097] The cutting tool 930 on the right has a rotation axis 940. The cutting tool 940 embodies a real-world example of a cutting tool with runout. Runout is a term used to describe a state in which the pattern of the cutting grooves is offset from the rotation axis of the cutting tool. The cutting tool 930 has a groove 950 (shown by a solid contour) that is laterally offset to the right from the idealized pattern of groove 960 (shown by a dotted contour). This state is independent of the feed rate or direction and is a characteristic of the cutting tool 930 itself. The implication of tool runout is that a particular groove (952) aligned in the direction of the runout cuts most of the material from the workpiece in each rotation of the cutting tool 930. A technique for simulating the surface roughness of a workpiece considering a set of machining operation parameters is described below, where optionally, the technique may be configured to take into account the effect of tool runout.

[0098] The surface roughness of a workpiece resulting from machining operations can be simulated based on parameters including the cutting tool diameter, feed rate, and spindle speed. This simulation is explained in relation to the following figure.

[0099] Figure 10 is a diagram illustrating how the surface roughness of a workpiece is simulated in embodiments of the present disclosure, showing the paths of the tips of the grooves of a cutting tool in the operating plane and the corresponding surface profiles. A cutting tool having two grooves, such as the one shown in Figure 8, is modeled in graph 1000. The cutting tool moves in the XY plane (the operating plane of the cutting tool) perpendicular to the Z-axis, which is the spindle rotation axis, according to the same rules used previously. One of the grooves has a tip, and the path of this tip is represented by point 1010. As the cutting tool rotates and moves in the indicated feed direction, point 1010 follows path 1020. The groove on the opposite side of the cutting tool follows a similar path to path 1020, but with a half-rotation phase difference.

[0100] Graph 1000 was created using the following technique. The tip of each cutting tool groove is given an initial coordinate in the XY plane. For example, the tip of the cutting tool groove represented by point 1010 may be given an initial coordinate corresponding to the position of the cutting tool at the "equator," following in the feed direction. Then, the groove on the opposite side is given an initial coordinate relating to the position of the cutting tool at the equator, leading in the feed direction. Based on the feed rate and spindle speed, it can be determined how far each groove tip advances with each rotation of the cutting tool in the feed direction. For example, with a spindle speed of 1000 rpm and a feed rate of 100 mm / min, the cutting tool advances 0.1 mm / revolution. The spatial motion of point 1010 can then be calculated at discrete points for each simulation step. That is, if the simulation uses a step of 1° spindle rotation, from one step to the next, point 1010 rotates 1° around the spindle axis (providing new X and Y coordinates) and translates in the X direction by an amount determined by ΔX = (0.1 mm / rotation) * (1 / 360 rotation). Using the new X and Y coordinates due to the tool rotation and translation ΔX, the position of point 1010 can be calculated at each simulation step. If the X and Y coordinates of point 1010 are plotted for all simulation steps, the result is path 1020.

[0101] Graph 1030 shows the surface profile of the workpiece after cutting by the groove of the cutting tool moving along path 1020 and the groove on the opposite side. Graph 1030 is greatly magnified with vertical axis (Y) units in micrometers compared to tool path trace graph 1000, which has vertical axis units in millimeters. In Graph 1030, the surface shape created by path 1020 is shown in profile 1040 (in a dark line font), while the surface shape created by the groove on the opposite side is shown in profile 1050 (in a light line font). From profiles 1040 and 1050, the surface roughness of the workpiece may be calculated in any preferred manner. Embodiments of surface roughness indices from the simulation include the absolute mean of the surface profile, the root mean square (RMS) of the surface profile, and the height from peak to valley. Other indices may be used as needed.

[0102] The surface roughness simulation techniques described above can be modified to simulate cutting tools with more than two (e.g., four) grooves, and / or to accommodate asymmetric cutting tool morphologies, such as unequal pitch (grooves not equally spaced) and runout (as described above, where one groove performs most of the cutting). In all cases, the simulation provides an estimated surface roughness index for a given set of feed rate and spindle speed conditions.

[0103] Typically, parts machined by the types of machining operations described in this disclosure have surface roughness tolerances that must be met. Therefore, the surface roughness prediction calculated as shown in Figure 10 may be compared to the part tolerance to determine whether the machining operation parameters result in an acceptable finish / roughness of the workpiece surface. It should be recalled that the objective of the sensorless and sensor-based feed rate optimization techniques mentioned earlier is to increase the feed rate as much as possible in the machining cycle when the material removal rate is small. The resulting rate increase from feed rate optimization can result in unacceptably high surface roughness; therefore, it is desirable to evaluate the surface roughness after performing feed rate optimization calculations before implementing the optimized feed rate profile.

[0104] Figure 11 is a flowchart 1100 of a method for calculating a machining feed rate profile according to an embodiment of the present disclosure, which includes first performing sensorless or sensor-based feed rate optimization, and then, if necessary, evaluating surface roughness and adjusting the feed rate profile.

[0105] In box 1102, the optimal feed rate profile is calculated using either the sensorless or sensor-based feed rate optimization techniques described in detail above. As previously explained, this results in a feed rate profile, where the optimal feed rate is determined at each tool center point position along the tool path. In many cases, the optimal feed rate profile includes higher feed rates than those in the original (e.g., constant rate) profile.

[0106] In box 1104, the surface roughness prediction simulation is depicted in Figure 10 and performed using the techniques described above. That is, at least the fastest feed rate in the optimal feed rate profile is identified, and the surface roughness prediction is performed using that fastest feed rate and other relevant machining parameters (e.g., spindle speed, tool diameter, etc.). In determination diamond 1106, it is determined whether the surface roughness predicted in box 1104 is acceptable. This may be done by a human evaluation of the roughness prediction, or it may be done programmatically by comparing the predicted roughness with a threshold or specification value.

[0107] If the predicted surface roughness is unacceptable, the process moves from determination diamond 1106 to box 1108 where a modified feed rate profile is calculated. This involves reducing a portion of the feed rate profile that has the highest feed rate. The amount of reduction may be determined by any preferred method, e.g., by a fixed percentage (e.g., 5%) or by a percentage selected based on the required reduction in the surface roughness index. Smoothing or blending techniques may be used to blend the feed rate changes of the portion of the profile where the rate is truncated to the new maximum value. The result in box 1108 is a modified feed rate profile in which the maximum feed rate is lower than the maximum feed rate that was in the optimal feed rate profile from box 1102.

[0108] The process returns to box 1104 to predict surface roughness using the modified feed rate profile. In determination diamond 1106, it is again determined (here based on the modified feed rate profile) whether the surface roughness is acceptable. If it is not acceptable, in box 1108, another modified feed rate profile is calculated with a further reduced maximum feed rate. The loop of boxes 1108 and 1104 may be repeated multiple times to achieve the desired surface roughness index. If the predicted surface roughness is acceptable in determination diamond 1106, the process moves to box 1110, where the operation program with the final modified feed rate profile is used by the machine controller for the actual workpiece machining operation.

[0109] Techniques for optimizing machining feed rates using sensorless or sensor-based methods and evaluating the surface roughness of the workpiece for the optimized feed rate profile before implementing the optimized feed rate profile for production operations are disclosed above. All of these calculations and evaluations can be advantageously incorporated within a software application having a graphical user interface (GUI) that guides the user through the setup of machining operations.

[0110] Figure 12 shows a graphical user interface (GUI) screen 1200 of a software application configured to optimize the feed rate of a machining operation according to an embodiment of the present disclosure. The GUI screen 1200 is designed to guide the user through the feed rate optimization process in a highly automated manner.

[0111] In section 1210 at the top of the GUI screen 1200, the user selects two input files. The first input file contains the NC program that defines the operation for machining the workpiece / part. The second input file contains data from the previously described reference machining cycle, namely tool position and motor torque data (in the case of a sensorless embodiment) collected from the reference machining cycle and used to calculate the optimal feed rate profile.

[0112] Optimization settings and configuration options are defined in section 1220 on the left side of screen 1200. The settings typically include defining the time range over which optimization is performed, which may be the entire machining cycle. The settings also include selecting a target load level, i.e., a defined percentage of maximum machine torque used to scale the optimal feed rate profile, as previously mentioned. The target load level may be set to the default value of 100%, but may also be set higher or lower, where a lower target may be chosen, for example, to extend tool life.

[0113] Furthermore, increasing or improving the maximum feed rate is selectable via a slider bar in section 1220. The option to increase the maximum feed rate (for example, to three times the constant feed rate used in the machining program) was mentioned earlier. A set of checkboxes defining other configuration settings is shown in section 1230 in the lower left. The first two, namely the removal of friction torque during feed rate optimization calculations and the permission for feed rate reduction as the rate increases, were mentioned earlier. Another option labeled “Advanced Fine Tuning” relates to features that will be discussed in conjunction with later figures.

[0114] After the configuration settings have been determined as desired, the user can run the feed rate optimization calculation by clicking the Run Optimization button 1240. The graph section 1250 is located on the right side of the GUI screen 1200. Until button 1240 is clicked to run the feed rate optimization, the graph section 1250 contains a graph that displays spindle torque data from a reference machining cycle in curve 1260 and the target spindle torque in line 1270. The Y-axis line is labeled on the left side along with the units of the percentage of the maximum spindle torque.

[0115] Figure 13 shows the graphical user interface (GUI) screen 1200 of Figure 12 after a feed rate optimization calculation has been performed according to an embodiment of the present disclosure. When the user clicks the Run Optimization button 1240 as described above, the software application performs the feed rate optimization calculation and displays the results (the calculation is performed almost instantaneously). At this point, graph section 1250 is reformatted as graph section 1250A, where the graph still includes spindle torque data from a reference machining cycle at curve 1260 and the target spindle torque at line 1270, and further includes a feed rate curve 1330. The feed rate curve 1330 plots the optimal feed rate as a percentage of the original constant feed rate, as indicated by the label on the Y-axis line added to the right. It can be seen that if the spindle torque of the reference machining cycle is lower, the feed rate curve 1330 is higher, and vice versa. This is the effect shown in the previous figure and described in detail above.

[0116] After the feed rate optimization calculation is performed, the save program button 1350 becomes active, allowing the user to save a new machining program containing the optimized feed rate profile. The new machining program is then available for use in production machining operations. If the user wishes to try additional configuration settings, the buttons and other controls in section 1220 may be adjusted, and at this point, the run optimization button 1240 becomes active again.

[0117] The aforementioned software application and GUI screen 1200 are configured, in particular, to optimize the feed rate of a machining operation using sensorless technology based on spindle torque data relating to a reference machining cycle. The same application and a similar GUI screen may be provided to optimize the feed rate of a machining operation using sensor-based technology that utilizes vibration data relating to a reference machining cycle.

[0118] Another feature relating to the feed rate optimization technique of this disclosure is shown in the lower left of Figure 12 and relates to the checkbox for advanced fine-tuning briefly mentioned above. This feature allows calculations not only to optimize the feed rate for a given machining program but also to add new command lines to the program for finer adjustments of the feed rate. This feature is described below.

[0119] Figure 14 shows a workpiece machined by a toolpath defined by a program including a command line with sparse control point spacing, and a workpiece having a toolpath again defined with increased control point density, according to an embodiment of the present disclosure.

[0120] In Figure 1400 on the left, the workpiece 1410 has a wavy shape along its left edge, as shown. The workpiece 1410 is machined by a cutting tool 1420 following a tool path 1430 (dashed line) to have a flat surface along its left edge. The tool path 1430 is defined in the machining program by a set of command line control points 1432, 1434, 1436, 1438, etc., which are fairly widely spaced, but sufficient to define a tool path (which is a straight line in this case). In the machining program, a specific feed rate can be defined for each command line containing each control point. However, if feed rate optimization calculations are performed on the tool path 1430, little improvement in feed rate is achieved. This is because each toolpath segment (for example, from control point 1432 to control point 1434) includes sections with high material removal rates and sections with low (or zero) material removal rates, and each toolpath segment may use only a single feed rate.

[0121] The solution to the above problem is shown in Figure 1450 on the right. The workpiece 1410 is the same as described above. However, using the technique of this disclosure, a new machining program is defined that includes additional command-line control points, enabling a much better application of feed rate optimization calculations. The cutting tool 1420 follows the same trajectory as the tool path 1430, but is defined by a different set of command-line control points, so the cutting tool 1420 follows a tool path identified here as 1430A. In addition to the original control points 1432, 1434, etc., the tool path 1430A also includes the additional control points 1462, 1464, 1466, etc. Now, when the feed rate optimization calculation is performed on the tool path 1430A, the feed rate can be adjusted much better to the actual cutting conditions in each section of the tool path. For example, the feed rate can be set to the maximum in the tool path section from control point 1462 to 1464, since little or no material is being cut from the workpiece 1410. Conversely, in the tool path section from control point 1434 to 1466, where high material removal is involved, the feed rate is kept almost at the original feed rate to avoid exceeding the target spindle load.

[0122] The function described above in relation to Figure 14, which involves adding command lines to a machining program with finer control point spacing to optimize the feed rate in a new machining program, is provided using the advanced fine-tuning checkbox shown in Figures 12 and 13. When this box is checked, the user inputs a number of control points to be added to the machining program. After performing feed rate optimization with a first number of additional control points, the user can increase the number of additional control points as needed and rerun the optimization calculation until a satisfactory result is obtained. Similarly, fine-tuning of the control point spacing can also be automated. For example, when advanced fine-tuning is selected, if the spindle load varies by a specified rate between existing command points, and / or if the optimal feed rate changes by a specified rate between command points, the program may automatically insert additional command points. This control point spacing feature can be configured in the GUI programming and the basic algorithms of the GUI, and / or by user-input parameters.

[0123] In other embodiments, the surface roughness prediction calculation and flowchart shown in Figure 11 may be added to a software application to provide the user with all the functionality in a single interface. This is described below.

[0124] Figure 15 shows a GUI screen 1500 of a software application configured to optimize feed rates and predict surface roughness for machining operations according to an embodiment of the present disclosure. GUI screen 1500 includes all the features of the feed rate optimization GUI screen 1200 described above, including a configuration setting section 1220 and a graph section 1250A. In addition, GUI screen 1500 includes a surface roughness prediction screen 1510, where the user can view the surface roughness prediction results for the optimized feed rate profile and modify the feed rate profile if desired to obtain the required surface roughness characteristics for the workpiece.

[0125] After optimizing the feed rate and viewing the results in sections 1220 and 1250A, the user may perform a surface roughness prediction simulation on the optimized feed rate profile by clicking button 1520. The results of this surface roughness prediction simulation are displayed in Table 1530, which includes various surface roughness indices such as maximum, average, and RMS surface roughness in one non-limiting embodiment.

[0126] If the user wishes to improve the surface roughness from the optimized feed profile, this can be done on the right side of section 1510. In box 1540, the user may enter a target surface roughness index, or instead, a percentage reduction in the maximum feed rate in the optimized profile. In either case, when the user clicks button 1550, the steps in the flowchart diagram in Figure 11 are performed by the software application to calculate a new feed rate profile with a lower maximum feed rate (e.g., a rate increase of less than 300% as previously mentioned in the description of Figure 12) and simulate the surface roughness for the new feed rate profile. The predicted surface roughness results for the modified feed rate profile are displayed in Table 1560. If the user is satisfied with the results, the machining program with the modified feed rate profile can be saved by clicking button 1570.

[0127] The software application with GUI screen 1500 provides the user with all the features of the feed rate optimization techniques of this disclosure, including feed rate optimization for both sensorless and sensor-based use cases, flexibility in determining the amount of feed rate improvement, convenient configuration options, the ability to add command-line control points to the machining program to better optimize speed in local sections of the machining operation, and surface roughness prediction, which is built in along with its own configurability and target alignment. An optimized motion profile using these features can dramatically improve the efficiency of the machine tool operation.

[0128] Throughout the above description, various computers and controllers are described and suggested. It should be understood that the software applications and modules of such computers and controllers run on one or more electronic computing devices having a processor and memory modules. In particular, this includes the machine controller 140 in Figure 1 and the controller 410 in Figure 4. In addition, some or all of the feed rate optimization calculations and surface roughness predictions may be performed by separate computing devices communicating with the machine controllers. Specifically, the processors in the controllers 140 / 410 and the separate computing devices are configured to perform the sensorless and / or sensor-based feed rate optimization described above, along with the control of the machine tool itself, including the method steps in Figures 5, 7, and / or 11, calculations using equations (1) to (6) and other techniques described above, and the execution of the software applications and GUIs in Figures 12, 13, and 15.

[0129] Numerous preferred embodiments and models of methods for sensorless and sensor-based feed rate optimization have been described above, but those skilled in the art will recognize modifications, rearrangements, additions, and secondary combinations thereof. Accordingly, the appended claims below and the claims incorporated herein are intended to be construed as including all such modifications, rearrangements, additions, and secondary combinations as they are in their true spirit and scope.

[0130] With regard to the above embodiments and modifications, the following additional information is disclosed. (Note 1) In a feed rate optimization method, The data collection process includes collecting time-series data by a machine controller for a reference machining cycle performed by a machine tool, wherein the data includes, for multiple time steps, the position of the tool center point along the tool path and axial acceleration data from an acceleration sensor attached to the machine tool. moreover, A time series value calculation step, which calculates time series values ​​relating to acceleration parameters from the acceleration data in the axial direction of the time series, A determination step of determining a target value for the acceleration parameter from the time-series values ​​of the acceleration parameter, The process includes: calculating an optimal feed rate profile, wherein the optimal feed rate profile includes: for each time step in the time series data, calculating a new feed rate by multiplying the original feed rate from the reference machining cycle by the ratio of the target value of the acceleration parameter to the acceleration parameter with respect to the time step; and pairing the new feed rate with the tool center point position in the optimal feed rate profile. Furthermore, a feed rate optimization method comprising a step of using the machine controller to control the machine tool that performs production machining operations using an operation program created using the optimal feed rate profile. (Note 2) The feed rate optimization method according to Appendix 1, wherein the acceleration sensor is configured to measure axial acceleration in a direction parallel to the rotation axis of the spindle of the machine tool. (Note 3) The feed rate optimization method described in Appendix 1, wherein the aforementioned reference machining cycle is performed using a predetermined operating program that includes a constant feed rate. (Note 4) The feed rate optimization method according to Appendix 1, wherein calculating the new feed rate includes limiting the new feed rate by a maximum increase factor and a maximum decrease factor relative to the original feed rate. (Note 5) The feed rate optimization method described in Appendix 1, wherein the operation program is created by pairing each tool center point position in the optimal feed rate profile with a new feed rate corresponding to each tool center point position, and defining a machine tool operation command that includes all of the pairings. (Note 6) The feed rate optimization method according to Appendix 1, wherein the time series value calculation step for calculating the time series value relating to the acceleration parameter includes generating intermediate time series data by performing a double integral of the axial acceleration data of the time series, and then calculating a moving average of the intermediate time series data to generate the time series value relating to the acceleration parameter. (Note 7) The feed rate optimization method according to Appendix 1, wherein the determination step for determining the target value of the acceleration parameter includes selecting the maximum value from the time-series values ​​of the acceleration parameter over the reference machining cycle. (Note 8) The feed rate optimization method according to Appendix 1, further comprising using a feedback control algorithm together with the optimal feed rate profile to control the machine tool performing the production machining operation using the machine controller. (Note 9) The feed rate optimization method according to Appendix 1, further comprising the step of using a feed angle group when performing the feed rate optimization, wherein each time step is assigned to the feed angle group based on the tool center point velocity vector, a determination step of determining the target value of the acceleration parameter is performed for each feed angle group, and the calculation of the new feed rate for each time step is performed using the target value of the acceleration parameter for the feed angle group to which the time step belongs. (Note 10) The feed rate optimization method according to Appendix 1, further comprising performing a predictive simulation of the surface roughness of a workpiece using the feed rate data in the optimal feed rate profile, along with the spindle speed and the radius and number of grooves in the cutting tool. (Note 11) The feed rate optimization method described in Appendix 10, wherein the prediction simulation of the surface roughness of the workpiece is calculated based on the distance the cutting tool advances in the feed direction between cutting of one of the grooves of the cutting tool, thereby determining the shape of the machined surface of the workpiece. (Note 12) The feed rate optimization method according to Appendix 1, further comprising the use of a software application having a graphical user interface (GUI) that defines the configuration settings for feed rate optimization and refers to the graphical results of the feed rate optimization. (Note 13) The feed rate optimization method according to Appendix 12, wherein the GUI and the software application include user-selectable options, a number of additional command-line control points are added to the operation program, and the optimal feed rate profile is calculated for the operation program having the additional command-line control points. (Note 14) The feed rate optimization method according to Appendix 12, which includes a GUI and a software application, which includes a surface roughness prediction simulation performed on the optimal feed rate profile, which includes the calculation of a modified feed rate profile that satisfies a user-defined surface roughness specification. (Note 15) The feed rate optimization method according to Appendix 1, wherein the machine tool is a multi-axis machine tool or an industrial robot having a machining tool head mounted as an end tool of the arm. (Note 16) In a feed rate optimization method, The process includes a data collection step in which a machine controller collects time-series data for a reference machining cycle performed by a machine tool, wherein the data includes, for multiple time steps, the position of the tool center point along the tool path and axial acceleration data from an acceleration sensor attached to the machine tool, the acceleration sensor being configured to measure axial acceleration in a direction parallel to the rotation axis of the spindle of the machine tool. moreover, The process includes a time series value calculation step for calculating time series values ​​relating to acceleration parameters from the axial acceleration data of the time series, the time series value calculation step includes generating intermediate time series data by performing a double integral of the axial acceleration data of the time series, and then calculating a moving average of the intermediate time series data to generate the time series values ​​relating to the acceleration parameters. moreover, A determination step of determining the target value of the acceleration parameter as the maximum value from the time-series values ​​of the acceleration parameter over the aforementioned reference machining cycle, The process includes: calculating an optimal feed rate profile, wherein the optimal feed rate profile includes: for each time step in the time series data, calculating a new feed rate by multiplying the original feed rate from the reference machining cycle by the ratio of the target value of the acceleration parameter to the acceleration parameter with respect to the time step; and pairing the new feed rate with the tool center point position in the optimal feed rate profile. moreover, A feed rate optimization method, comprising the step of using the machine controller to control the machine tool that performs production machining operations using an operation program created using the optimal feed rate profile. (Note 17) A feed rate optimization system for machine tools, A machine tool configured to perform an action on a workpiece, An acceleration sensor is attached to the machine tool and configured to measure axial acceleration in a direction parallel to the rotation axis of the spindle of the machine tool. A computing device that communicates with the aforementioned machine tool and the aforementioned acceleration sensor, The computing device is configured to calculate and use the optimal feed rate profile by performing the process, The above process is, The data collection process includes collecting time-series data for a reference machining cycle performed by the aforementioned machine tool, wherein the data includes, for multiple time steps, the position of the tool center point along the tool path and axial acceleration data from the acceleration sensor. moreover, A time series value calculation step, which calculates time series values ​​relating to acceleration parameters from the acceleration data in the axial direction of the time series, A determination step of determining a target value for the acceleration parameter from the time-series values ​​of the acceleration parameter, The process includes an optimal feed rate profile calculation step for calculating an optimal feed rate profile, wherein the optimal feed rate profile includes, for each time step in the time series data, calculating a new feed rate by multiplying the original feed rate from the reference machining cycle by the ratio of the target value of the acceleration parameter to the acceleration parameter with respect to the time step, and pairing the new feed rate with the tool center point position in the optimal feed rate profile. moreover, A feed rate optimization system, which includes a usage process that controls the machine tool performing production machining operations using an operation program created using the optimal feed rate profile. (Note 18) The feed rate optimization system described in Appendix 17, wherein the aforementioned reference machining cycle is performed using a predetermined operating program that includes a constant feed rate. (Note 19) The feed rate optimization system according to Appendix 17, wherein calculating the new feed rate includes limiting the new feed rate by a maximum increase factor and a maximum decrease factor relative to the original feed rate. (Note 20) The feed rate optimization system described in Appendix 17 is created by pairing each tool center point position in the optimal feed rate profile with a corresponding new feed rate for each tool center point position, and defining a machine tool operation command that includes all of the pairings. (Note 21) The feed rate optimization system according to Appendix 17, wherein the time series value calculation step for calculating time series values ​​relating to acceleration parameters includes: performing a double integral of the axial acceleration data of the time series to generate intermediate time series data; and then calculating a moving average of the intermediate time series data to generate the time series values ​​relating to the acceleration parameters. (Note 22) The feed rate optimization system according to Appendix 17, wherein the determination step for determining the target value of the acceleration parameter includes selecting the maximum value from the time-series values ​​of the acceleration parameter over the reference machining cycle. (Note 23) The feed rate optimization system according to Appendix 17, further comprising controlling the machine tool performing the production machining operation using a feedback control algorithm together with the optimal feed rate profile via a machine controller. (Note 24) The feed rate optimization system according to Appendix 17, further comprising using feed angle groups when calculating the optimal speed profile, wherein each time step is assigned to a feed angle group based on the tool center point velocity vector, a determination step of determining an acceleration parameter target value is performed for each feed angle group, and the calculation of a new feed rate for each time step is performed using the acceleration parameter target value for the feed angle group to which the time step belongs. (Note 25) The feed rate optimization system as described in Appendix 17, wherein the computing device is further configured to perform a predictive simulation of the surface roughness of a workpiece using the feed rate data in the optimal feed rate profile, along with the spindle speed and the radius and number of grooves in the cutting tool. (Note 26) The feed rate optimization system described in Appendix 25 predicts the surface roughness of the workpiece based on the distance the cutting tool travels in the feed direction between cuts of one of the grooves of the cutting tool, thereby calculating the shape of the machined surface of the workpiece. (Note 27) The feed rate optimization system according to Appendix 17, wherein the computing device runs a software application having a graphical user interface (GUI) that allows the user to define configuration settings for feed rate optimization and refer to the graphical results of the feed rate optimization. (Note 28) The feed rate optimization system according to Appendix 27, wherein the GUI and the software application include user-selectable options, a number of additional command-line control points are added to the operation program, and the optimal feed rate profile is calculated for the operation program having the additional command-line control points. (Note 29) The feed rate optimization system described in Appendix 27 includes a GUI and a software application, which include a surface roughness prediction simulation performed on the optimal feed rate profile, which includes the calculation of a modified feed rate profile that satisfies a user-defined surface roughness specification. (Note 30) The feed rate optimization system according to Appendix 17, wherein the machine tool is a multi-axis machine tool or industrial robot having a machining tool head mounted as an arm end tool. [Explanation of symbols]

[0131] 100 Systems 110 Machine tools 112 spindles 120 Cutting tools 122 Tip 130 Workpieces 140 controllers 150 sensors 210 workpieces 220 Cutting tools 230 Tool paths

Claims

1. In a feed rate optimization method, The data collection process includes collecting time-series data by a machine controller for a reference machining cycle performed by a machine tool, wherein the data includes, for multiple time steps, the position of the tool center point along the tool path and axial acceleration data from an acceleration sensor attached to the machine tool. moreover, A time series value calculation step, which calculates time series values ​​relating to acceleration parameters from the acceleration data in the axial direction of the time series, A determination step of determining a target value for the acceleration parameter from the time-series value of the acceleration parameter, The process includes: calculating an optimal feed rate profile, wherein the optimal feed rate profile includes: for each time step in the time series data, calculating a new feed rate by multiplying the original feed rate from the reference machining cycle by the ratio of the target value of the acceleration parameter to the acceleration parameter with respect to the time step; and pairing the new feed rate with the tool center point position in the optimal feed rate profile. Furthermore, a feed rate optimization method comprising a step of using the machine controller to control the machine tool that performs production machining operations using an operation program created using the optimal feed rate profile.

2. The feed rate optimization method according to claim 1, wherein the acceleration sensor is configured to measure axial acceleration in a direction parallel to the rotation axis of the spindle of the machine tool.

3. The feed rate optimization method according to claim 1, wherein the reference machining cycle is performed using a predetermined operating program that includes a constant feed rate.

4. The feed rate optimization method according to claim 1, wherein calculating the new feed rate includes limiting the new feed rate by a maximum increase factor and a maximum decrease factor relative to the original feed rate.

5. The feed rate optimization method according to claim 1, wherein the operation program is created by pairing each tool center point position in the optimal feed rate profile with a new feed rate corresponding to each tool center point position, and defining a machine tool operation command that includes all of the pairings.

6. The feed rate optimization method according to claim 1, wherein the time series value calculation step for calculating the time series value relating to the acceleration parameter includes: performing a double integral of the axial acceleration data of the time series to generate intermediate time series data; and then calculating a moving average of the intermediate time series data to generate the time series value relating to the acceleration parameter.

7. The feed rate optimization method according to claim 1, wherein the determination step for determining the target value of the acceleration parameter includes selecting the maximum value from the time-series values ​​relating to the acceleration parameter over the reference machining cycle.

8. The feed rate optimization method according to claim 1, further comprising using a feedback control algorithm together with the optimal feed rate profile to control the machine tool performing the production machining operation using the machine controller.

9. The feed rate optimization method according to claim 1, further comprising the step of using a feed angle group when performing the feed rate optimization, wherein each time step is assigned to the feed angle group based on the tool center point velocity vector, a determination step of determining an acceleration parameter target value is performed for each feed angle group, and the calculation of a new feed rate for each time step is performed using the acceleration parameter target value for the feed angle group to which the time step belongs.

10. The feed rate optimization method according to claim 1, further comprising performing a predictive simulation of the surface roughness of a workpiece using the feed rate data in the optimal feed rate profile, along with the spindle speed and the radius and number of grooves in the cutting tool.

11. The feed rate optimization method according to claim 10, wherein the prediction simulation of the surface roughness of the workpiece is calculated based on the distance the cutting tool advances in the feed direction between cutting of one of the grooves of the cutting tool to determine the shape of the machined surface of the workpiece.

12. The feed rate optimization method according to claim 1, further comprising a user using a software application having a graphical user interface (GUI) that defines the configuration settings for feed rate optimization and refers to the graphical results of the feed rate optimization.

13. The feed rate optimization method according to claim 12, wherein the GUI and the software application include user-selectable options, a number of additional command-line control points are added to the operation program, and the optimal feed rate profile is calculated for the operation program having the additional command-line control points.

14. The feed rate optimization method according to claim 12, wherein the GUI and the software application include a surface roughness prediction simulation performed on the optimal feed rate profile, which includes the calculation of a modified feed rate profile that satisfies a user-defined surface roughness specification.

15. The feed rate optimization method according to claim 1, wherein the machine tool is a multi-axis machine tool or an industrial robot having a machining tool head mounted as an arm end tool.

16. In a feed rate optimization method, The process includes a data collection step in which a machine controller collects time-series data for a reference machining cycle performed by a machine tool, wherein the data includes, for multiple time steps, the position of the tool center point along the tool path and axial acceleration data from an acceleration sensor attached to the machine tool, the acceleration sensor being configured to measure axial acceleration in a direction parallel to the rotation axis of the spindle of the machine tool. moreover, The process includes a time series value calculation step for calculating time series values ​​relating to acceleration parameters from the axial acceleration data of the time series, the time series value calculation step includes generating intermediate time series data by performing a double integral of the axial acceleration data of the time series, and then calculating a moving average of the intermediate time series data to generate the time series values ​​relating to the acceleration parameters. moreover, A determination step of determining the target value of the acceleration parameter as the maximum value from the time-series values ​​of the acceleration parameter over the aforementioned reference machining cycle, The process includes: calculating an optimal feed rate profile, wherein the optimal feed rate profile includes: for each time step in the time series data, calculating a new feed rate by multiplying the original feed rate from the reference machining cycle by the ratio of the target value of the acceleration parameter to the acceleration parameter with respect to the time step; and pairing the new feed rate with the tool center point position in the optimal feed rate profile. moreover, A feed rate optimization method, comprising the step of using the machine controller to control the machine tool that performs production machining operations using an operation program created using the optimal feed rate profile.

17. A feed rate optimization system for machine tools, A machine tool configured to perform an action on a workpiece, An acceleration sensor is attached to the machine tool and configured to measure axial acceleration in a direction parallel to the rotation axis of the spindle of the machine tool. A computing device that communicates with the aforementioned machine tool and the aforementioned acceleration sensor, The computing device is configured to calculate and use the optimal feed rate profile by performing the process, The above process is, The data collection process includes collecting time-series data for a reference machining cycle performed by the aforementioned machine tool, wherein the data includes, for multiple time steps, the position of the tool center point along the tool path and axial acceleration data from the acceleration sensor. moreover, A time series value calculation step, which calculates time series values ​​relating to acceleration parameters from the acceleration data in the axial direction of the time series, A determination step of determining a target value for the acceleration parameter from the time-series value of the acceleration parameter, The process includes an optimal feed rate profile calculation step for calculating an optimal feed rate profile, wherein the optimal feed rate profile includes, for each time step in the time series data, calculating a new feed rate by multiplying the original feed rate from the reference machining cycle by the ratio of the target value of the acceleration parameter to the acceleration parameter with respect to the time step, and pairing the new feed rate with the tool center point position in the optimal feed rate profile. moreover, A feed rate optimization system, which includes a usage process that controls the machine tool performing production machining operations using an operation program created using the optimal feed rate profile.

18. The feed rate optimization system according to claim 17, wherein the reference machining cycle is performed using a predetermined operating program that includes a constant feed rate.

19. The feed rate optimization system according to claim 17, wherein calculating a new feed rate includes limiting the new feed rate by a maximum increase factor and a maximum decrease factor relative to the original feed rate.

20. The feed rate optimization system according to claim 17, wherein the operation program is created by pairing each tool center point position in the optimal feed rate profile with a new feed rate corresponding to each tool center point position, and defining a machine tool operation command that includes all of the pairings.

21. The feed rate optimization system according to claim 17, wherein the time series value calculation step for calculating time series values ​​relating to acceleration parameters includes: performing a double integral of the axial acceleration data of the time series to generate intermediate time series data; and then calculating a moving average of the intermediate time series data to generate the time series values ​​relating to acceleration parameters.

22. The feed rate optimization system according to claim 17, wherein the determination step for determining an acceleration parameter target value includes selecting a maximum value from the time-series values ​​relating to the acceleration parameter over the reference machining cycle.

23. The feed rate optimization system according to claim 17, further comprising controlling the machine tool performing the production machining operation using a feedback control algorithm together with the optimal feed rate profile via a machine controller.

24. The feed rate optimization system according to claim 17, further comprising using a feed angle group when calculating the optimal speed profile, wherein each time step is assigned to a feed angle group based on the tool center point velocity vector, a determination step of determining an acceleration parameter target value is performed for each feed angle group, and the calculation of a new feed rate for each time step is performed using the acceleration parameter target value for the feed angle group to which the time step belongs.

25. The feed rate optimization system according to claim 17, wherein the computing device is further configured to perform a predictive simulation of the surface roughness of a workpiece using the feed rate data in the optimal feed rate profile, along with the spindle speed and the radius and number of grooves in the cutting tool.

26. The feed rate optimization system according to claim 25, wherein the prediction simulation of the surface roughness of the workpiece is calculated based on the distance the cutting tool advances in the feed direction between cutting of one of the grooves of the cutting tool to determine the shape of the machined surface of the workpiece.

27. The feed rate optimization system according to claim 17, wherein the computing device runs a software application having a graphical user interface (GUI) that allows the user to define configuration settings for feed rate optimization and refer to the graphical results of the feed rate optimization.

28. The feed rate optimization system according to claim 27, wherein the GUI and the software application include user-selectable options, a number of additional command-line control points are added to the operation program, and the optimal feed rate profile is calculated for the operation program having the additional command-line control points.

29. The feed rate optimization system according to claim 27, wherein the GUI and the software application include a surface roughness prediction simulation performed on the optimal feed rate profile, which includes the calculation of a modified feed rate profile that satisfies a user-defined surface roughness specification.

30. The feed rate optimization system according to claim 17, wherein the machine tool is a multi-axis machine tool or an industrial robot having a machining tool head mounted as an arm end tool.

Images