Dynamic response measurement device and evaluation method of electric spindle under multiple working conditions

Abstract

This invention provides a dynamic response measurement device and evaluation method for electric spindles under multiple operating conditions, relating to the field of dynamic performance evaluation technology for electric spindles. The method includes: simulating multiple preset operating conditions using the multi-condition dynamic response measurement device to obtain electromagnetic force measured by a force sensor and displacement measured by a displacement sensor under these conditions; determining the dynamic stiffness of the electric spindle under these preset operating conditions based on the electromagnetic force and displacement; modeling the dynamic stiffness of the electric spindle under these preset operating conditions using the Kriging response surface method to obtain the dynamic stiffness of the electric spindle under all operating conditions; repeating the simulation and modeling N times to obtain the dynamic stiffness under all operating conditions after N modeling iterations; and determining the performance margin of the dynamic stiffness of the electric spindle under the tested operating condition based on the dynamic stiffness under all operating conditions after N modeling iterations and uncertainty theory. This invention achieves a quantitative evaluation of the dynamic response of an electric spindle under different operating conditions.

Description

Technical Field

[0001] This invention relates to the field of dynamic performance evaluation technology for electric spindles, and in particular to a dynamic response measurement device and evaluation method for electric spindles under multiple operating conditions. Background Technology

[0002] Machine tools are essential basic mechanical equipment and mother machines in the manufacturing industry. As the core component of CNC machine tools, the precision spindle directly contacts the workpiece being machined, so its performance directly affects the machining efficiency of the machine tool, as well as the accuracy and reliability of the machined workpiece.

[0003] The dynamic performance of a machine tool system is crucial for achieving the required quality and productivity during machining. During machining, the forces generated by material removal exert dynamic excitation on the machine tool spindle, causing relative vibrations between the workpiece and the tool tip. These vibrations affect workpiece quality (surface roughness, dimensional accuracy) and productivity (machining instability, catastrophic failures). Therefore, it is essential to characterize the dynamic performance of the spindle under high-speed rotation, particularly the dynamic behavior of the tool tip position. However, due to the nonlinear characteristics of the electric spindle structure, and the influence of factors such as mechanical backlash, local contact stress, and lubrication on the bearings inside the electric spindle during machining, the dynamic behavior of the spindle varies significantly under different operating conditions (i.e., workload). Furthermore, due to variations in machining parameters and inconsistencies in tools and materials, the workload during machining is not a fixed value, complicating the measurement of the spindle tool tip dynamic response and thus posing challenges for engineers' decision-making. Summary of the Invention

[0004] This invention provides a device and method for measuring and evaluating the dynamic response of an electric spindle under multiple operating conditions, so as to measure the dynamic response of the electric spindle under multiple operating conditions.

[0005] In a first aspect, embodiments of the present invention provide a dynamic response measurement device for an electric spindle under multiple working conditions, comprising: an electric spindle, a virtual tool, an electromagnetic excitation module, a force sensor, and a displacement sensor; wherein, the virtual tool is connected to the electric spindle, and the axis of the electromagnetic excitation module coincides with the axis of the virtual tool; the electromagnetic excitation module is used to provide electromagnetic force to the virtual tool, and the virtual tool is used to simulate different working conditions based on the electromagnetic force provided by the electromagnetic excitation module; the force sensor is connected to the electromagnetic excitation module and is used to measure the electromagnetic force applied by the electromagnetic excitation module; the displacement sensor is used to measure the displacement of the virtual tool under the action of the electromagnetic force.

[0006] In some embodiments, the virtual tool includes a rotor core, a connecting rod, and a pressure block; wherein the connecting rod is used to connect to the electric spindle, the rotor core is the operating position of the electromagnetic excitation module, and the pressure block is used to fix the rotor core; the rotor core, the connecting rod, and the pressure block are connected by a press-fitting method.

[0007] In some embodiments, the rotor core is made of silicon steel sheets.

[0008] In some embodiments, the device further includes a spindle seat, a base plate, a displacement sensor support, and an integrated bracket; wherein the electric spindle is fixed on the spindle seat, and the spindle seat is fixed on the base plate; the electromagnetic excitation module and the force sensor are both fixed on the base plate, the displacement sensor support is connected to the integrated bracket and then fixed on the base plate, and the displacement sensor is fixed on the integrated bracket.

[0009] In some embodiments, the displacement sensor is a non-contact eddy current displacement sensor, and the displacement sensor is installed in a split configuration.

[0010] This invention discloses a dynamic response measurement device for electric spindles under multiple operating conditions. A virtual tool is connected to the electric spindle, and the axis of the electromagnetic excitation module coincides with the axis of the virtual tool. The electromagnetic excitation module provides electromagnetic force to the virtual tool, which simulates different operating conditions based on this force. A force sensor is connected to the electromagnetic excitation module to measure the electromagnetic force applied by the module. A displacement sensor measures the displacement of the virtual tool under the electromagnetic force. This invention can more realistically simulate the operation of the electric spindle under different operating conditions based on the electromagnetic excitation module and the virtual tool, and uses the force and displacement sensors to measure the dynamic response of the electric spindle under different operating conditions.

[0011] Secondly, embodiments of the present invention provide a method for evaluating the dynamic response of an electric spindle under multiple operating conditions, comprising: simulating multiple preset operating conditions based on the electric spindle dynamic response measurement device under multiple operating conditions described in the first aspect, and obtaining the electromagnetic force measured by a force sensor and the displacement measured by a displacement sensor under the multiple preset operating conditions; determining the dynamic stiffness of the electric spindle under the multiple preset operating conditions based on the electromagnetic force and the displacement; modeling the dynamic stiffness of the electric spindle under the multiple preset operating conditions based on the Kriging response surface method, and obtaining the dynamic stiffness of the electric spindle under all operating conditions; repeating the simulation and modeling N times to obtain the dynamic stiffness under all operating conditions after N modeling, and determining the performance margin of the dynamic stiffness of the electric spindle under the operating condition to be tested based on the dynamic stiffness under all operating conditions after N modeling and uncertainty theory.

[0012] In some embodiments, determining the performance margin of the dynamic stiffness of the electric spindle under the test condition based on the dynamic stiffness and uncertainty theory under all operating conditions after N modeling includes: determining a first cumulative distribution function of the dynamic stiffness of the electric spindle under the test condition based on the dynamic stiffness under all operating conditions after N modeling; determining multiple related operating conditions corresponding to the test condition based on preset speed uncertainty range and force uncertainty range; determining a second cumulative distribution function of the dynamic stiffness of the electric spindle under each of the related operating conditions based on the dynamic stiffness under all operating conditions after N modeling; and determining the performance margin of the dynamic stiffness of the electric spindle under the test condition based on the first cumulative distribution function and multiple second cumulative distribution functions.

[0013] In some embodiments, determining the performance margin of the dynamic stiffness of the electric spindle under the test condition based on the first cumulative distribution function and a plurality of second cumulative distribution functions includes: determining, from the first cumulative distribution function and the plurality of second cumulative distribution functions, a first target cumulative distribution function with the largest dynamic stiffness value and a second target cumulative distribution function with the smallest dynamic stiffness value; determining the dynamic stiffness margin of the electric spindle under the test condition based on a dynamic stiffness threshold and the first target cumulative distribution function; determining the uncertainty value of the electric spindle under the test condition based on the first target cumulative distribution function and the second target cumulative distribution function; and determining the performance margin of the dynamic stiffness of the electric spindle under the test condition based on the dynamic stiffness margin and the uncertainty value.

[0014] As an example, determining the dynamic stiffness margin of the electric spindle under the test condition based on the dynamic stiffness threshold and the first target cumulative distribution function includes: determining the first dynamic stiffness corresponding to the first probability value in the first target cumulative distribution function; and determining the difference between the dynamic stiffness threshold and the first dynamic stiffness as the dynamic stiffness margin.

[0015] As an example, determining the uncertainty value of the electric spindle under the test condition based on the first target cumulative distribution function and the second target cumulative distribution function includes: determining the second dynamic stiffness corresponding to the first probability value in the second target cumulative distribution function; determining the third dynamic stiffness corresponding to the second probability value in the first target cumulative distribution function; the second probability value is greater than the first probability value; and determining the difference between the third dynamic stiffness and the second dynamic stiffness as the uncertainty value.

[0016] The present invention discloses a method for evaluating the dynamic response of an electric spindle under multiple operating conditions. This method simulates multiple preset operating conditions using a dynamic response measurement device for electric spindles under multiple operating conditions, acquiring electromagnetic forces measured by force sensors and displacements measured by displacement sensors under these conditions. Based on the electromagnetic forces and displacements, the dynamic stiffness of the electric spindle under these preset operating conditions is determined. Using the Kriging response surface methodology, the dynamic stiffness of the electric spindle under these preset conditions is modeled to obtain the dynamic stiffness under all operating conditions. This simulation and modeling process is repeated N times to obtain the dynamic stiffness under all operating conditions after N modeling iterations. Based on the dynamic stiffness under all operating conditions after N modeling iterations and uncertainty theory, the performance margin of the dynamic stiffness of the electric spindle under the tested operating condition is determined. This invention simultaneously considers the combined influence of cutting force and rotational speed on the dynamic response of the electric spindle, enabling a more realistic reflection of the dynamic performance of the machine tool during actual machining. Furthermore, by combining the Kriging response surface methodology and uncertainty theory to theoretically calculate the performance margin of dynamic compliance under different operating conditions, a quantitative evaluation of the dynamic response of the electric spindle under different operating conditions is achieved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of the dynamic response measurement device for electric spindle under multiple working conditions provided in an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of the virtual tool in an embodiment of the present invention;

[0020] Figure 3 This is one of the flowcharts illustrating the dynamic response evaluation method for electric spindles under multiple operating conditions provided in this embodiment of the invention.

[0021] Figure 4 This is the second flowchart illustrating the dynamic response evaluation method for electric spindles under multiple operating conditions provided in this embodiment of the invention.

[0022] Figure 5 This is the third flowchart illustrating the dynamic response evaluation method for electric spindles under multiple operating conditions provided in this embodiment of the invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] Machine tools are essential basic mechanical equipment and mother machines in the manufacturing industry. As a core component of high-end CNC machine tools, the precision spindle directly contacts the workpiece being machined; therefore, its performance directly affects the machining efficiency, as well as the accuracy and reliability of the machined workpiece. Consequently, further improving the dynamic performance of precision spindles has become one of the main bottlenecks restricting the improvement of core performance indicators of high-end CNC machine tools.

[0025] The dynamic performance of a machine tool system (including spindle, tool, workpiece, and motion table) is crucial for achieving the required quality and productivity during machining. During machining, the forces generated by material removal exert dynamic excitation on the machine tool spindle, causing relative vibrations between the workpiece and the tool tip. These vibrations affect workpiece quality (surface roughness, dimensional accuracy) and productivity (machining instability, catastrophic failures). Therefore, it is essential to characterize and evaluate the dynamic performance of the spindle under high-speed rotation, particularly the dynamic behavior of the tool tip position. However, due to the nonlinear characteristics of the electric spindle structure and the influence of factors such as mechanical backlash, local contact stress, and lubrication on the bearings inside the electric spindle during machining, the dynamic behavior of the spindle varies significantly under different operating conditions (i.e., workload). Furthermore, due to variations in machining parameters and inconsistencies in tools and materials, the workload during machining is not a fixed value but rather falls within a certain range; this uncertainty is called workload uncertainty. These uncertainties in the machining process complicate the evaluation of the spindle and tool tip dynamic response, thus hindering engineers' decision-making.

[0026] To address the aforementioned problems, this invention provides a dynamic response measurement device and evaluation method for electric spindles under multiple operating conditions.

[0027] Figure 1 This is a schematic diagram of the structure of the dynamic response measurement device for electric spindles under multiple operating conditions provided in an embodiment of the present invention. Figure 1As shown, the device includes: an electric spindle 1, a virtual tool 2, an electromagnetic excitation module 3, a force sensor 4, and a displacement sensor 5. The virtual tool 2 is connected to the electric spindle 1, and the axis of the electromagnetic excitation module 3 coincides with the axis of the virtual tool 2. The electromagnetic excitation module 3 provides electromagnetic force to the virtual tool 2, which simulates different working conditions based on the electromagnetic force provided by the module 3. The force sensor 4 is connected to the electromagnetic excitation module 3 and measures the electromagnetic force applied by the module 3. The displacement sensor 5 measures the displacement of the virtual tool 2 under the action of the electromagnetic force.

[0028] The virtual tool 2 is connected to the electric spindle 1 via a spindle thread interface, and the electromagnetic excitation module 3 is connected to the force sensor 4 via bolts.

[0029] In some embodiments, the electromagnetic force applied by the electromagnetic excitation module includes a static component and a dynamic component. The electromagnetic force control process includes setting the current in the two pairs of opposite magnetic poles to... and Bias current A constant DC signal controls the current. The simulation of milling force is achieved by accumulating a constant DC signal with a sinusoidal dynamic signal of a certain frequency and amplitude. The direction of the electromagnetic force can be perpendicular to the axial direction, such as the horizontal or vertical direction of the spindle.

[0030] It should be noted that the working conditions in the embodiments of the present invention refer to cutting machining conditions, and each working condition corresponds to a specific spindle speed and cutting force. The spindle speed can be directly controlled by the spindle frequency converter, and the cutting force can be simulated by the electromagnetic force applied to the virtual tool by the electromagnetic excitation module to realize the simulation of different working conditions.

[0031] In some embodiments, such as Figure 1 As shown, the device also includes a spindle seat 6, a base plate 7, a displacement sensor support 8, and an integrated bracket 9. The electric spindle 1 is fixed to the spindle seat 6, which is fixed to the base plate 7. The electromagnetic excitation module 3 and the force sensor 4 are both fixed to the base plate 7. The displacement sensor support 8 is connected to the integrated bracket 9 and fixed to the base plate 7. The displacement sensor 5 is fixed to the integrated bracket 9. The displacement sensor support 8 is used to connect to the integrated bracket 9 to fix the integrated bracket 9 to the base plate 7.

[0032] In one possible implementation, the electric spindle 1 is bolted to the spindle seat 6, which is bolted to the base plate 7. The electromagnetic excitation module 3 and the force sensor 4 are bolted together and then bolted to the base plate 7. The displacement sensor support 8 and the integrated bracket 9 are bolted together and then bolted to the base plate 7. The displacement sensor 5 is bolted to the integrated bracket 9. The integrated bracket 9 has mounting holes for displacement sensors on its top and both sides, for mounting displacement sensors in the horizontal and vertical directions of the spindle, thus enabling displacement measurement in both directions.

[0033] In some embodiments, the displacement sensor 5 is a non-contact eddy current displacement sensor. The displacement sensor 5 is installed separately to avoid inaccurate measurements caused by housing vibration when the sensor is installed on the electromagnetic excitation module housing.

[0034] In some embodiments, such as Figure 2 As shown, the virtual tool includes a connecting rod 10, a rotor core 11, and a pressure block 12. The connecting rod 10 is used to connect to the electric spindle 1, the rotor core 11 is the operating position of the electromagnetic excitation module 3, and the pressure block 12 is used to fix the rotor core 11. The rotor core 11, connecting rod 10, and pressure block 12 are connected by press-fitting to form an integral structure. The rotor core 11 can be made of silicon steel sheet. The press-fitting mechanism between the rotor core 11, connecting rod 10, and pressure block 12 forms a stable mechanical connection, enabling the virtual tool to possess efficient mechanical properties and stability during operation.

[0035] This invention discloses a dynamic response measurement device for electric spindles under multiple operating conditions. A virtual tool is connected to the electric spindle, and the axis of the electromagnetic excitation module coincides with the axis of the virtual tool. The electromagnetic excitation module provides electromagnetic force to the virtual tool, which simulates different operating conditions based on this force. A force sensor is connected to the electromagnetic excitation module to measure the electromagnetic force applied by the module. A displacement sensor measures the displacement of the virtual tool under the electromagnetic force. This invention can more realistically simulate the operation of the electric spindle under different operating conditions based on the electromagnetic excitation module and the virtual tool, and uses the force and displacement sensors to measure the dynamic response of the electric spindle under different operating conditions.

[0036] Figure 3 This is one of the flowcharts illustrating the dynamic response evaluation method for electric spindles under multiple operating conditions provided in this embodiment of the invention. It should be noted that this method requires the use of the dynamic response measurement device for electric spindles under multiple operating conditions described in the above embodiment to simulate different operating conditions, and to perform evaluation based on measurement data from force sensors and displacement sensors. Figure 3 As shown, the method may include the following steps.

[0037] Step 301: Simulate multiple preset working conditions based on the dynamic response measurement device under multiple working conditions of the electric spindle, and obtain the electromagnetic force measured by the force sensor and the displacement measured by the displacement sensor under multiple preset working conditions.

[0038] It should be noted that the preset working conditions in the embodiments of the present invention can be typical cutting machining conditions in the art, or conditions set based on actual needs. Each working condition corresponds to a specific electric spindle speed and milling force.

[0039] In some embodiments, the required current signal can be set by the current signal setting module on the device according to the dynamic force requirements under each preset working condition. Then, the converted voltage signal is output to two pulse width modulation amplifiers by the acquisition card. The amplifier converts the output voltage into an output current according to a specific amplification factor and applies it to the electromagnetic excitation module of the dynamic response measurement device under multiple working conditions of the electric spindle, thereby generating radial electromagnetic force. Then, the acquisition card stores the displacement and electromagnetic force collected by the displacement sensor and the force sensor into the target storage area.

[0040] Since the signal collected by the displacement sensor contains unnecessary error signals caused by the rotation of the spindle, the collected signal can be converted to the frequency domain by Fourier transform to identify and determine the unnecessary motion frequency. Then, the frequency can be filtered out by Fourier interpolation to obtain the displacement of the electric spindle under the action of electromagnetic force.

[0041] In some embodiments, before performing step 301, the dynamic response measurement device for multiple operating conditions of the electric spindle can be installed at a fixed position at the front end of the electric spindle under test to ensure the alignment of the electromagnetic excitation module and the electric spindle under test. The spindle frequency converter is started to make the electric spindle rotate at a stable speed, and the displacement sensor and force sensor are zeroed and calibrated to prepare for the operation. During step 301, the magnitude of the static force amplitude of the control current is set to simulate different cutting forces experienced by the electric spindle during machining, and the rotational speed of the electric spindle is adjusted to simulate the machining state of the electric spindle under different preset operating conditions. The electromagnetic force measured by the force sensor and the displacement measured by the displacement sensor are obtained under each preset operating condition.

[0042] Step 302: Determine the dynamic stiffness of the electric spindle under multiple preset working conditions based on electromagnetic force and displacement.

[0043] It can be understood that the electromagnetic force measured by the force sensor under each preset working condition is equivalent to the cutting force experienced by the electric spindle during the cutting process, and the displacement measured by the displacement sensor is equivalent to the radial displacement of the tool tip during the cutting process.

[0044] In some embodiments, for each simulated preset working condition, the electromagnetic force and displacement under that condition can be obtained. For each preset working condition, the ratio of displacement to electromagnetic force under that condition is used as the dynamic stiffness of the electric spindle under that preset working condition.

[0045] Step 303: Based on the Kriging response surface method, model the dynamic stiffness of the electric spindle under multiple preset working conditions to obtain the dynamic stiffness of the electric spindle under all working conditions.

[0046] In some embodiments, the process of modeling the dynamic stiffness of the electric spindle under multiple preset operating conditions based on the Kriging response surface methodology is equivalent to predicting the dynamic stiffness under other operating conditions based on the dynamic stiffness under multiple preset operating conditions using the Kriging response surface methodology. This prediction process is directly implemented through the Kriging response surface model in related technologies. As an example, a dynamic stiffness dataset under different operating conditions can be obtained based on a large amount of simulation data; the Kriging response surface model can be trained based on this dataset to continuously adjust the model parameters, resulting in a trained Kriging response surface model; the dynamic stiffness of the electric spindle under multiple preset operating conditions is input into the trained Kriging response surface model to obtain the dynamic stiffness of the electric spindle under all operating conditions.

[0047] Step 304: Repeat the simulation and modeling N times to obtain the dynamic stiffness under all working conditions after N modeling, and determine the performance margin of the dynamic stiffness of the electric spindle under the working condition to be tested based on the dynamic stiffness under all working conditions after N modeling and the uncertainty theory.

[0048] In other words, steps 301 to 303 are repeated N times, resulting in the dynamic stiffness under all working conditions after N modeling iterations. The dynamic stiffness under all working conditions can be understood as the correspondence between dynamic stiffness and different loads and speeds, and can be expressed as a surface, where each point corresponds to a specific working condition and dynamic stiffness. After N modeling iterations, the dynamic stiffness under all working conditions can be represented by N surfaces, each surface expressing the correspondence between dynamic stiffness and different cutting forces and speeds.

[0049] Due to variations in machining parameters and inconsistencies in cutting tools and materials, the workload during machining is not a fixed value; within a certain range, this uncertainty is called workload uncertainty. Similarly, the spindle speed can exhibit some uncertainty under different machining scenarios. To incorporate these uncertainties and evaluate the dynamic performance of the spindle, such as… Figure 4 As shown, based on the dynamic stiffness and uncertainty theory under all working conditions after N modeling iterations, the process of determining the performance margin of the dynamic stiffness of the electric spindle under the tested working condition can include the following steps:

[0050] Step 401: Based on the dynamic stiffness under all working conditions after N modeling iterations, determine the first cumulative distribution function of the dynamic stiffness of the electric spindle under the working condition to be measured.

[0051] The working condition to be tested refers to the working condition to be evaluated. There can be one or more working conditions to be tested. The working condition to be tested corresponds to a specific cutting force (load) and rotational speed.

[0052] In some embodiments, the dynamic stiffness under all working conditions after N modeling is equivalent to the correspondence between dynamic stiffness and N cutting forces and speeds, i.e., N data surfaces. Therefore, the dynamic stiffness under a test working condition can be determined based on each data surface. Thus, the dynamic stiffness of the electric spindle under the test working condition can have N values. Based on the N dynamic stiffnesses of the electric spindle under the test working condition, the first cumulative distribution function can be obtained.

[0053] In this context, the abscissa of the first cumulative distribution function of the dynamic stiffness of the electric spindle under the test condition is the dynamic stiffness, and the ordinate is the cumulative probability value of the occurrence of the dynamic stiffness.

[0054] Step 402: Based on the preset speed uncertainty range and force uncertainty range, determine multiple related working conditions corresponding to the working condition to be tested.

[0055] The uncertainty ranges for rotational speed and force can be set based on actual needs. For example, the uncertainty range for rotational speed can be set to 5000 rpm, and the uncertainty range for force can be set to 500 N. Relevant operating conditions refer to the operating conditions after introducing uncertainties into the operating condition under test. For example, if the rotational speed under the operating condition under test is 10,000 rpm and the cutting force is 1,500 N, then the relevant operating conditions include: Relevant Operating Condition 1 (rotational speed of 5,000 rpm, cutting force of 1,500 N), Relevant Operating Condition 2 (rotational speed of 5,000 rpm, cutting force of 1,000 N), Relevant Operating Condition 3 (rotational speed of 5,000 rpm, cutting force of 2,000 N), Relevant Operating Condition 4 (rotational speed of 10,000 rpm, cutting force of 1,000 N), Relevant Operating Condition 5 (rotational speed of 10,000 rpm, cutting force of 2,000 N), Relevant Operating Condition 6 (rotational speed of 15,000 rpm, cutting force of 1,500 N), Relevant Operating Condition 7 (rotational speed of 15,000 rpm, cutting force of 1,000 N), and Relevant Operating Condition 8 (rotational speed of 15,000 rpm, cutting force of 2,000 N).

[0056] Step 403: Based on the dynamic stiffness under all working conditions after N modeling iterations, determine the second cumulative distribution function of the dynamic stiffness of the electric spindle under each relevant working condition.

[0057] For each relevant working condition, based on the dynamic stiffness under all working conditions after N modeling, N dynamic stiffnesses of the electric spindle under that relevant working condition can be determined. Based on the N dynamic stiffnesses under the relevant working condition, the second cumulative distribution function of the dynamic stiffness under that relevant working condition can be determined.

[0058] In this context, the horizontal axis of the second cumulative distribution function represents the dynamic stiffness under the relevant working conditions, and the vertical axis represents the cumulative probability value of the occurrence of dynamic stiffness.

[0059] Step 404: Based on the first cumulative distribution function and multiple second cumulative distribution functions, determine the performance margin of the dynamic stiffness of the electric spindle under the test condition.

[0060] Among them, the first cumulative distribution function can characterize the random uncertainty caused by measurement errors in different simulation processes, and the second cumulative distribution function can characterize the cognitive uncertainty. Thus, the performance margin of the obtained dynamic stiffness introduces the uncertainty in the real scenario, making the obtained performance margin more realistically reflect the dynamic performance of the electric spindle in the actual machining process.

[0061] In some embodiments, based on a first cumulative distribution function and multiple second cumulative distribution functions, multiple dynamic stiffnesses with cumulative probabilities equal to a cumulative probability threshold are determined, and the largest dynamic stiffness is determined from these multiple dynamic stiffnesses. The difference between a preset dynamic stiffness threshold and the obtained largest dynamic stiffness is determined as the performance margin of the dynamic stiffness of the electric spindle under the test condition.

[0062] In some embodiments, such as Figure 5 As shown, the method for determining the performance margin of the dynamic stiffness of the electric spindle under the test condition based on the first cumulative distribution function and multiple second cumulative distribution functions may include the following steps.

[0063] Step 501: From the first cumulative distribution function and multiple second cumulative distribution functions, determine the first target cumulative distribution function with the largest dynamic stiffness value and the second target cumulative distribution function with the smallest dynamic stiffness value.

[0064] In some embodiments, the minimum and maximum dynamic stiffness within the dynamic stiffness range corresponding to the first cumulative distribution function and the plurality of second cumulative distribution functions are determined respectively, that is, the minimum and maximum dynamic stiffness corresponding to the first cumulative distribution function and the minimum and maximum dynamic stiffness corresponding to each second cumulative distribution function are obtained, and the cumulative distribution function corresponding to the maximum value among these maximum dynamic stiffnesses is determined as the first target cumulative distribution function, and the cumulative distribution function corresponding to the minimum value among these minimum dynamic stiffnesses is determined as the second target cumulative distribution function.

[0065] Step 502: Based on the dynamic stiffness threshold and the first target cumulative distribution function, determine the dynamic stiffness margin of the electric spindle under the test condition.

[0066] In some embodiments, the process of determining the dynamic stiffness margin of the electric spindle under the test condition based on the dynamic stiffness threshold and the first target cumulative distribution function includes: determining the first dynamic stiffness corresponding to the first probability value in the first target cumulative distribution function; and determining the difference between the dynamic stiffness threshold and the first dynamic stiffness as the dynamic stiffness margin. The first probability value can be 0.5, as shown in equation (1).

[0067] (1);

[0068] in, This represents the dynamic stiffness margin of the electric spindle under the tested operating conditions. The corresponding electric spindle speed under the test condition; This represents the cutting force corresponding to the working condition under test. Let be the first objective cumulative distribution function under the test condition; This is the inverse cumulative distribution function corresponding to the first objective cumulative distribution function; This is the dynamic stiffness threshold.

[0069] Step 503: Based on the first objective cumulative distribution function and the second objective cumulative distribution function, determine the uncertainty value of the electric spindle under the test condition.

[0070] In some embodiments, the process of determining the uncertainty value of the electric spindle under the test condition based on the first target cumulative distribution function and the second target cumulative distribution function may include: determining the second dynamic stiffness corresponding to the first probability value in the second target cumulative distribution function; determining the third dynamic stiffness corresponding to the second probability value in the first target cumulative distribution function; the second probability value being greater than the first probability value; and determining the difference between the third dynamic stiffness and the second dynamic stiffness as the uncertainty value. The first probability value can be 0.5, and the second probability value can be a preset confidence level, such as 0.95, as shown in equation (2) below.

[0071] (2);

[0072] in, The uncertainty value of the electric spindle under the test condition; The corresponding electric spindle speed under the test condition; This represents the cutting force corresponding to the working condition under test. This represents the range of rotational speed uncertainty. For the range of force uncertainty; Let be the first objective cumulative distribution function under the test condition; Let be the cumulative distribution function of the second objective under the test condition; This is the inverse cumulative distribution function corresponding to the first objective cumulative distribution function; This is the inverse cumulative distribution function corresponding to the second objective cumulative distribution function; , where is the confidence level.

[0073] Step 504: Based on the dynamic stiffness margin and uncertainty values, determine the performance margin of the dynamic stiffness of the electric spindle under the test condition.

[0074] In some embodiments, the ratio of dynamic stiffness margin to uncertainty value can be determined as the performance margin of dynamic stiffness of the electric spindle under the test condition, as shown in the following formula (3).

[0075] (3);

[0076] in, This represents the performance margin of the dynamic stiffness of the electric spindle under the tested operating conditions.

[0077] The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to embodiments of the present invention simulates multiple preset operating conditions using a dynamic response measurement device for electric spindles under multiple operating conditions. The electromagnetic force measured by a force sensor and the displacement measured by a displacement sensor are used under these preset operating conditions. Based on the electromagnetic force and displacement, the dynamic stiffness of the electric spindle under these preset operating conditions is determined. Using the Kriging response surface methodology, the dynamic stiffness of the electric spindle under these preset operating conditions is modeled to obtain the dynamic stiffness of the electric spindle under all operating conditions. The simulation and modeling are repeated N times to obtain the dynamic stiffness under all operating conditions after N modeling iterations. Based on the dynamic stiffness under all operating conditions after N modeling iterations and uncertainty theory, the performance margin of the dynamic stiffness of the electric spindle under the tested operating condition is determined. This invention simultaneously considers the combined influence of cutting force and rotational speed on the dynamic response of the electric spindle, enabling a more realistic reflection of the dynamic performance of the machine tool during actual machining. Furthermore, by combining the Kriging response surface methodology and uncertainty theory to theoretically calculate the performance margin of dynamic compliance under different operating conditions, a quantitative evaluation of the dynamic response of the electric spindle under different operating conditions is achieved.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the dynamic response of an electric spindle under multiple operating conditions, characterized in that, include: Based on the dynamic response measurement device under multiple working conditions of the electric spindle, multiple preset working conditions are simulated to obtain the electromagnetic force measured by the force sensor and the displacement measured by the displacement sensor under the multiple preset working conditions. Based on the electromagnetic force and the displacement, the dynamic stiffness of the electric spindle under the multiple preset working conditions is determined; Based on the Kriging response surface method, the dynamic stiffness of the electric spindle under multiple preset working conditions is modeled to obtain the dynamic stiffness of the electric spindle under all working conditions. Repeat the simulation and modeling N times to obtain the dynamic stiffness under all working conditions after N modeling, and determine the performance margin of the dynamic stiffness of the electric spindle under the working condition to be tested based on the dynamic stiffness under all working conditions after N modeling and the uncertainty theory. Based on the dynamic stiffness and uncertainty theory under all working conditions after N modeling iterations, the performance margin of the dynamic stiffness of the electric spindle under the tested working condition is determined, including: Based on the dynamic stiffness under all working conditions after the Nth modeling, the first cumulative distribution function of the dynamic stiffness of the electric spindle under the working condition to be measured is determined. Based on the preset range of rotational speed uncertainty and force uncertainty, multiple related working conditions corresponding to the working condition to be tested are determined; Based on the dynamic stiffness under all working conditions after the Nth modeling, the second cumulative distribution function of the dynamic stiffness of the electric spindle under each relevant working condition is determined. Based on the first cumulative distribution function and multiple second cumulative distribution functions, the performance margin of the dynamic stiffness of the electric spindle under the test condition is determined. Based on the first cumulative distribution function and multiple second cumulative distribution functions, the performance margin of the dynamic stiffness of the electric spindle under the test condition is determined, including: From the first cumulative distribution function and the plurality of second cumulative distribution functions, determine the first target cumulative distribution function with the largest dynamic stiffness value and the second target cumulative distribution function with the smallest dynamic stiffness value; Based on the dynamic stiffness threshold and the first target cumulative distribution function, the dynamic stiffness margin of the electric spindle under the test condition is determined. Based on the first target cumulative distribution function and the second target cumulative distribution function, the uncertainty value of the electric spindle under the test condition is determined; Based on the dynamic stiffness margin and the uncertainty value, the performance margin of the dynamic stiffness of the electric spindle under the test condition is determined. The dynamic response measurement device for electric spindles under multiple operating conditions includes: an electric spindle, a virtual tool, an electromagnetic excitation module, a force sensor, and a displacement sensor; among which, The virtual tool is connected to the electric spindle, and the axis of the electromagnetic excitation module coincides with the axis of the virtual tool. The electromagnetic excitation module is used to provide electromagnetic force to the virtual tool, and the virtual tool is used to simulate different working conditions based on the electromagnetic force provided by the electromagnetic excitation module. The force sensor is connected to the electromagnetic excitation module and is used to measure the electromagnetic force applied by the electromagnetic excitation module. The displacement sensor is used to measure the displacement of the virtual tool under the action of the electromagnetic force.

2. The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to claim 1, characterized in that, The virtual cutting tool includes a rotor core, a connecting rod, and a pressure block; wherein... The connecting rod is used to connect with the electric spindle, the rotor core is the working position of the electromagnetic excitation module, and the pressure block is used to fix the rotor core; The rotor core, the connecting rod, and the pressure block are connected by a press-fitting method.

3. The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to claim 2, characterized in that, The rotor core is made of silicon steel sheets.

4. The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to claim 1, characterized in that, The device also includes a spindle seat, a base plate, a displacement sensor support, and an integrated bracket; wherein, the electric spindle is fixed on the spindle seat, and the spindle seat is fixed on the base plate; the electromagnetic excitation module and the force sensor are both fixed on the base plate, the displacement sensor support is connected to the integrated bracket and then fixed on the base plate, and the displacement sensor is fixed on the integrated bracket.

5. The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to claim 1, characterized in that, The displacement sensor is a non-contact eddy current displacement sensor, and the displacement sensor is installed in a split manner.

6. The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to claim 1, characterized in that, The determination of the dynamic stiffness margin of the electric spindle under the tested operating condition based on the dynamic stiffness threshold and the first target cumulative distribution function includes: Determine the first dynamic stiffness corresponding to the first probability value in the first target cumulative distribution function; The difference between the dynamic stiffness threshold and the first dynamic stiffness is determined as the dynamic stiffness margin.

7. The method for evaluating the dynamic response of an electric spindle under multiple operating conditions according to claim 1, characterized in that, Based on the first target cumulative distribution function and the second target cumulative distribution function, the uncertainty value of the electric spindle under the test condition is determined, including: Determine the second dynamic stiffness corresponding to the first probability value in the second target cumulative distribution function; Determine the third dynamic stiffness corresponding to the second probability value in the first target cumulative distribution function; the second probability value is greater than the first probability value; The difference between the third dynamic stiffness and the second dynamic stiffness is determined as the uncertainty value.

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