An intelligent monitoring method for grinding burn and its grade based on power signal

Abstract

The present invention discloses an intelligent monitoring method for grinding burn and its level based on power signal. The method takes grinding processing conditions as input and grinding burn degree as output, establishes a mapping relationship between grinding processing conditions, grinding temperature and grinding burn degree, and sets corresponding grinding temperature thresholds for different degrees of burn. The relationship between spindle power and grinding temperature is derived, and the grinding temperature threshold is converted into a spindle power threshold. The spindle power threshold is used to determine whether grinding burn occurs and the condition of grinding burn, thereby realizing intelligent monitoring of the grinding process. The present invention solves the problem that the existing technology is difficult to monitor the grinding temperature in real time and can only rely on empirical methods to determine the grinding burn condition, thereby avoiding grinding burn caused by excessively high grinding temperature and resulting in waste of resources.

Description

Technical Field

[0001] The present invention relates to the technical field of processing and manufacturing, and in particular to an intelligent monitoring method for grinding burns and their levels based on power signals. Background Art

[0002] During the grinding process, a large amount of grinding heat is transferred to the workpiece, causing the workpiece surface to heat up rapidly. When the grinding heat transferred to the workpiece exceeds a certain threshold, grinding burns appear on the workpiece surface. Grinding burns seriously restrict the processing quality and production efficiency of the workpiece. Therefore, how to realize intelligent monitoring of the grinding process and avoid grinding burns caused by excessively high grinding temperatures is the key to solving the problem that restricts the grinding quality and efficiency of manufacturing enterprises.

[0003] However, due to the complexity of the grinding process, it is difficult to monitor the grinding temperature of the grinding production line in real time by arranging temperature sensors to identify grinding burns. The surface burns of the workpiece caused by high grinding temperatures are not only related to the material removal rate, but are also affected by the characteristics of the material, the lubrication conditions of the grinding fluid, and the processing parameters. Using a single characteristic parameter as a characterization parameter cannot accurately determine the degree of grinding burns. Currently, grinding production lines still rely on empirical methods to determine grinding burns, and the results often lag behind production practice, resulting in a large amount of waste.

[0004] Patent publication number CN111015370B discloses a grinding monitoring method based on thermomechanical coupling, which is applied to grinding machine tools and includes the following steps: setting thresholds, collecting grinding part information, obtaining grinding part data, analyzing and judging, and analyzing vibration conditions; the patent takes grinding temperature as the monitoring object, but in actual applications, due to the complexity of the grinding processing area, it is difficult to monitor the grinding temperature of the grinding production line processing process in real time by arranging temperature sensors. Summary of the Invention

[0005] In order to solve the problem that the existing technology is difficult to monitor the grinding temperature in real time and can only rely on empirical methods to judge the grinding burn situation, the present invention provides an intelligent monitoring method for grinding burn and its level based on power signals.

[0006] The technical solution adopted in the present invention is:

[0007] An intelligent monitoring method for grinding burn and its grade based on power signal comprises the following steps:

[0008] S1 built a grinding processing platform and carried out grinding processing experiments. A power meter and thermocouple were used to collect power signals and temperature signals during the grinding process.

[0009] S2 classifies the burn degree of the workpiece based on the burn color of the workpiece surface after grinding;

[0010] S3 establishes the mapping relationship between grinding processing conditions, grinding temperature and grinding burn level, and sets the grinding temperature thresholds for different grinding burn levels;

[0011] S4 deduces the correlation model between spindle power and grinding temperature applicable to different grinding processing conditions, and then obtains the corresponding spindle power threshold P:

[0012]

[0013] Where S is the contact area of ​​the grinding zone, C p is the correction factor of the spindle power, C p It can be solved by deep learning method, C safe is the safety factor for spindle power monitoring, h f is the grinding fluid convection heat transfer coefficient, h w is the heat conduction factor into the workpiece, R ws is the heat rate transferred to the workpiece in the workpiece-grinding wheel system, R wch is the heat rate transferred to the workpiece in the workpiece-grinding chip subsystem;

[0014] S5 establishes the mapping relationship between grinding processing conditions, spindle power, and grinding burn level;

[0015] S6 installs voltage and current sensors in the machine tool electrical cabinet. The computer collects the spindle voltage and current through a data acquisition card. The spindle power signal is collected based on the three-phase electric power measurement principle. Based on the collected power signal, signal processing operations such as filtering and noise reduction are performed in the host computer (computer). The maximum grinding power is then obtained by subtracting the idling power during non-machining from the maximum power during machining. This is compared with the corresponding spindle power threshold to determine whether the spindle power threshold is exceeded. Furthermore, whether burns have occurred and the burn level are determined, thus realizing intelligent monitoring of grinding burns.

[0016] In the above power monitoring, the power signal is collected in real time; the maximum power during processing and the idling power when not processing can be identified through intelligent algorithms such as neural networks; the corresponding spindle power threshold should be calculated by substituting the current processing parameters into the formula in S4.

[0017] Furthermore, in step S3, h w The formula is

[0018]

[0019] Where k is the thermal conductivity of the workpiece, ρ is the density of the workpiece, c is the specific heat capacity of the workpiece, and v w is the feed speed of the workpiece, l cis the contact arc length of the grinding zone, and C is a parameter, whose specific value is related to the Paclet number and the average contact angle of the contact zone;

[0020] Furthermore, in step S3, R ws The formula is

[0021]

[0022] Among them, λ g is the thermal conductivity of the abrasive, γ0 is the averaged effective contact radius of the abrasive top surface, v s is the speed of the grinding wheel;

[0023] Furthermore, in step S3, R wch The formula is

[0024]

[0025] Among them, α w is the thermal diffusion coefficient of wear debris, γ is the shear strain in the wear debris formation area, t a is the thickness of deformed wear chips.

[0026] Furthermore, the formula of S in step S4 is

[0027]

[0028] Among them, a p is the cutting depth, d s is the grinding wheel diameter and B is the grinding width.

[0029] Furthermore, the correlation model between spindle power P and grinding temperature T is based on the workpiece surface temperature model established by Jin Tan and David Stephenson, and a conversion model between grinding temperature threshold and spindle power threshold is established;

[0030]

[0031] Q t is the total heat flux density in the grinding area, which can be obtained from the ratio of the actual spindle power consumption to the grinding contact area, Q t The formula is

[0032] Q t =PS

[0033] Substitute the Qt formula into the workpiece surface temperature model to obtain the correlation model between the spindle power P and the grinding temperature T.

[0034] Furthermore, in step S3, the setting of the grinding temperature thresholds for different grinding burn levels takes into account material properties and is set according to the degree of grinding burn of the material at different grinding temperatures, mainly based on the surface color. The corresponding relationship between the grinding temperature and the spindle grinding power corresponding to different degrees of grinding burn is calibrated by experiments, avoiding the influence of changes in processing conditions on the spindle power, and improving the accuracy of spindle power monitoring.

[0035] Furthermore, in step S5, the severity of the grinding burn of the ground specimen is quantified by the grinding power in combination with the early burn grade classification.

[0036] Furthermore, in step S6, the power signal is obtained by numerical integration of voltage and current based on the three-phase electric power measurement principle.

[0037] Furthermore, in step S4, a safety factor C is set. safe It is used to adjust the reliability of power monitoring alarm, with a value between 0 and 1. The safety factor multiplied by the critical power is the warning power, which can achieve early warning and ensure that there is no burn during the grinding process. The safety factor can be flexibly set according to the importance of the workpiece being processed.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] During the grinding process, the spindle power is strongly correlated with the grinding temperature. Therefore, by deriving a correlation model between the spindle power and the grinding temperature, the critical grinding temperature at which grinding burn occurs during each grinding operation can be converted into the critical spindle power. By setting the spindle power threshold accordingly, intelligent monitoring of the grinding process can be achieved.

[0040] The present invention establishes a mapping relationship between grinding processing conditions, spindle power, and grinding burn level by deducing the relationship between spindle power and grinding temperature. Based on the spindle power signal, intelligent detection of grinding burn is achieved to avoid grinding burn caused by excessively high grinding temperature and waste of resources.

[0041] The present invention only monitors spindle power and does not involve monitoring grinding temperature. Grinding temperature is a process parameter set based on the actual monitored object and is obtained from preliminary experiments. In these experiments, different degrees of workpiece grinding burn tests are conducted under the current grinding conditions to obtain the grinding temperature and corresponding power for the corresponding burn degree. Based on the grinding temperature-spindle power conversion model, the grinding temperature-spindle power mapping relationship is obtained, providing basic data support for subsequent grinding process monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of an intelligent monitoring method for grinding burn and its level based on power signals;

[0043] Figure 2 Schematic diagram of grinding burn grades and their corresponding grinding temperature thresholds;

[0044] Figure 3 Schematic diagram of the relationship between grinding burn temperature and spindle power;

[0045] Figure 4 Schematic diagram of grinding burn levels and their corresponding spindle power thresholds;

[0046] Figure 5 Schematic diagram of intelligent prediction results of grinding burn grade based on power signal;

[0047] Figure 6 Schematic diagram of the comparison of intelligent prediction of grinding burn level based on power signal and warning burn grinding power threshold. DETAILED DESCRIPTION

[0048] In order to clearly illustrate the technical features of this solution, the present invention will be described in detail below by way of specific implementation methods and in conjunction with the accompanying drawings. In the following description, many specific details are described to facilitate a full understanding of the present application. However, the present application can also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. Unless otherwise defined, all technical terms used hereinafter have the same meaning as those generally understood by those skilled in the art. The technical terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention. Unless otherwise specified, the various raw materials, reagents, instruments and equipment used in the present invention, etc. can be purchased from the market or can be prepared by existing methods.

[0049] Example 1

[0050] Please refer to Figure 1 As shown, the present invention provides an intelligent monitoring method for grinding burn and its level based on power signal, comprising the following steps:

[0051] S1 built a grinding platform and carried out grinding experiments, using a power meter and thermocouple to collect power and temperature signals during the grinding process.

[0052] S2 classifies the burn degree of the workpiece based on the burn color of the workpiece surface after grinding;

[0053] S3 establishes a mapping relationship between grinding processing conditions, grinding temperature, and grinding burn levels, and sets grinding temperature thresholds for different grinding burn levels. The setting of grinding temperature thresholds for different grinding burn levels takes into account material properties and is set according to the degree of grinding burn of the material at different grinding temperatures, mainly based on surface color. The corresponding relationship between grinding temperature and spindle grinding power corresponding to different degrees of grinding burn is calibrated by experiments, avoiding the influence of changes in processing conditions on spindle power and improving the accuracy of spindle power monitoring.

[0054] S4 deduces the correlation model between spindle power and grinding temperature applicable to different grinding processing conditions, and then obtains the corresponding spindle power threshold:

[0055] The correlation model between spindle power and grinding temperature is based on the workpiece surface temperature model established by Jin Tan and David Stephenson, and a conversion model between the grinding temperature threshold T and the spindle power threshold P is established (1);

[0056]

[0057] Q t is the total heat flux density in the grinding area, which can be obtained from the ratio of the actual spindle power consumption to the grinding contact area, Q t Formula (2) is

[0058] Q t =PS(2)

[0059] Q t Substituting the formula into the workpiece surface temperature model, the correlation model between the spindle power P and the grinding temperature T is obtained (3):

[0060]

[0061] Among them, C p is the correction factor of the spindle power, C p It can be solved by deep learning method, C safe As the safety factor of the spindle power monitoring, a safety factor C is set safe It is used to adjust the reliability of power monitoring alarm. The value is between 0 and 1. The safety factor multiplied by the critical power is the warning power, which can achieve early warning and ensure that there is no burn during the grinding process. The safety factor can be flexibly set according to the importance of the workpiece being processed. f is the grinding fluid convection heat transfer coefficient, h w is the heat conduction factor into the workpiece, R ws is the heat rate transferred to the workpiece in the workpiece-grinding wheel system, R wch is the heat rate transferred to the workpiece in the workpiece-grinding chip subsystem;

[0062] h w Formula (4) is

[0063]

[0064] Where k is the thermal conductivity of the workpiece, ρ is the density of the workpiece, c is the specific heat capacity of the workpiece, and v w is the feed speed of the workpiece, l c is the contact arc length of the grinding zone, and C is a parameter, whose specific value is related to the Paclet number and the average contact angle of the contact zone;

[0065] R ws Formula (5) is

[0066]

[0067] Among them, λ g is the thermal conductivity of the abrasive, γ0 is the averaged effective contact radius of the abrasive top surface, v s is the speed of the grinding wheel;

[0068] R wch Formula (6) is

[0069]

[0070] Among them, α w is the thermal diffusion coefficient of wear debris, γ is the shear strain in the wear debris formation area, t a is the thickness of deformed wear chips.

[0071] Formula (7) for S is

[0072]

[0073] Among them, a p is the cutting depth, d s is the grinding wheel diameter and B is the grinding width.

[0074] S5 establishes the mapping relationship between grinding processing conditions, spindle power, and grinding burn level. Combined with the previous burn level classification, the severity of grinding burn on the ground specimen is quantified by grinding power.

[0075] S6 installs voltage and current sensors in the machine tool electrical cabinet. The computer collects the spindle voltage and current through the data acquisition card. The spindle power signal is collected according to the three-phase electric power measurement principle. Based on the collected power signal, signal processing operations such as filtering and noise reduction are performed in the host computer (computer). The maximum grinding power is then obtained by subtracting the idling power during non-processing from the maximum power during processing. The maximum grinding power is compared with the corresponding spindle power threshold to determine whether the spindle power threshold is exceeded, and then determine whether burns occur and the burn level, thereby realizing intelligent monitoring of grinding burns.

[0076] The power signal is obtained by numerical integration of voltage and current based on the three-phase electric power measurement principle. In the above power monitoring, the power signal is collected in real time. The maximum power during processing and the idling power when not processing can be identified through intelligent algorithms such as neural networks. The corresponding spindle power threshold should be calculated by substituting the current processing parameters into the formula in S4.

[0077] Example 2

[0078] Please refer to Figure 1-5 As shown, an embodiment of the present invention provides an intelligent monitoring method for grinding burn and its level based on power signal, comprising the following steps:

[0079] S1 built a grinding platform and carried out grinding experiments, using a power meter and thermocouple to collect power and temperature signals during the grinding process.

[0080] For example, the workpiece DZ22B, made of directionally solidified material, was ground using the MKL7132X8 / 17 ultra-high-speed CNC high-power forming grinder from Hangzhou Machine Tool Works. The workpiece width was 8 mm, the grinding wheel size was 400×20×207F13A80FF22V, and the coolant concentration was 5% to 8%. The coolant pressure was 0.8 MPa, and single-side follow-up cooling was used. The grinding conditions and their corresponding measured temperatures and spindle power are shown in Table 1.

[0081] Table 1 Grinding processing conditions and their corresponding measured temperatures and spindle powers

[0082]

[0083]

[0084] S2 determines the grinding temperature threshold according to the above-mentioned different grinding processing conditions and the degree of grinding burn of the DZ22B blade material at different grinding temperatures. The main basis is the burn color of the DZ22B blade material, and the grinding burn of the DZ22B blade material is divided into 4 levels.

[0085] Level 1: Light gray with a slight yellowish tint, no obvious vibration marks; slightly burned; (650℃~850℃)

[0086] Level 2: Brown, with many fine vibration marks, moderate burns; (850℃~1050℃)

[0087] Level 3: Deep black, 650 with more fine vibration marks, severe burns; (1050℃~1250℃)

[0088] Level 4: Deep black with dark blue, with many fine vibration marks, grinding cracks visible to the naked eye, and severe burns. (1250℃~1500℃)

[0089] S3 establishes a mapping relationship between grinding processing conditions, grinding temperature, and grinding burn levels, and sets corresponding grinding temperature thresholds for different grinding burn levels.

[0090] Figure 2 is the threshold temperature corresponding to different levels of burn surface, Figure 2 It can be seen that under different grinding conditions, different degrees of burns are caused to the grinding surface, and different degrees of surface burns correspond to different grinding temperatures. Figure 2 , establish the corresponding relationship between grinding temperature and grinding burn grade.

[0091] S4 derives the relationship between the spindle power P and the grinding temperature T, and converts the grinding temperature threshold into the spindle power threshold; according to formulas (1) to (7) in Example 1, the spindle power corresponding to the grinding burn temperature under different grinding processing conditions is calculated, as follows: Figure 3 Indicated by the middle line. Figure 3 The middle dot is the grinding power and corresponding grinding temperature measured in the experiment. When the grinding temperature is 698℃, the burn level is level 1, and the corresponding spindle power is 447W; when the grinding temperature is 936℃, the burn level is level 2, and the corresponding spindle power is 1121W; when the grinding temperature is 1103℃, the burn level is level 3, and the corresponding spindle power is 1553W; when the grinding temperature is 1366℃, the burn level is level 4, and the corresponding spindle power is 2021W. Figure 3 , establish the corresponding relationship between grinding temperature and spindle power.

[0092] S5 establishes a mapping relationship between grinding processing conditions, spindle power, and grinding burn degree, and matches the corresponding spindle power threshold for different degrees of burn. Figure 4 is the grinding burn grade and its corresponding spindle power threshold. Figure 4 It can be seen that under different grinding conditions, different degrees of burns are caused to the grinding surface. Different degrees of burns correspond to different grinding powers. Different grinding burn levels can be predicted based on the spindle power during grinding. Figure 4 , establish the corresponding relationship between spindle power and grinding burn grade, and obtain Figure 5 The critical grinding burn power curve was predicted.

[0093] S6 installs voltage and current sensors in the electrical cabinet of the machine tool. The computer collects the spindle voltage and current through the data acquisition card. The power signal is collected according to the three-phase power measurement principle. Figure 5 The predicted critical grinding burn power curve shown in the figure realizes intelligent monitoring of grinding burn.

[0094] Example 3

[0095] Please refer to Figure 6 As shown, an embodiment of the present invention provides an intelligent monitoring method for grinding burn and its level based on power signal, including all steps and verifications in Example 2:

[0096] Verification of intelligent monitoring method for grinding burn level based on power signal.

[0097] like Figure 6 As shown, the solid line is the critical grinding burn power curve predicted by the method provided by this patent, and the dotted line is the warning burn grinding power threshold (critical grinding burn power value × safety factor, safety factor is 0.9). The dots are the grinding power values ​​under different grinding burn degrees measured through grinding processing experiments (grinding burn surface is shown in Figure 2). Figure 4 ).Depend on Figure 6 The comparison results of the predicted grinding burn power and the measured grinding burn power values ​​show that the measured grinding burn power values ​​are all within the predicted grinding burn power range, indicating that this method can be used to intelligently monitor grinding burn and its level based on power signals.

[0098] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description. It is intended that all variations within the meaning and range of equivalents of the claims be embraced herein, and any reference signs in the claims should not be construed as limiting the claims to which they relate.

Claims

1. An intelligent monitoring method for grinding burn and its grade based on power signal, characterized in that: The following steps are involved: S1 built a grinding platform and carried out grinding experiments, using a power meter and thermocouple to collect power and temperature signals during the grinding process. S2 classifies the burn degree of the workpiece based on the burn color of the workpiece surface after grinding; S3 establishes the mapping relationship between grinding processing conditions, grinding temperature and grinding burn level, and sets the grinding temperature thresholds for different grinding burn levels; S4 deduces the correlation model between spindle power and grinding temperature applicable to different grinding processing conditions, and then obtains the corresponding spindle power threshold P: in, S is the contact area of ​​the grinding zone, is the correction factor of the spindle power, C p It can be solved by deep learning method. is the safety factor for spindle power monitoring, h f is the grinding fluid convective heat transfer coefficient, h w is the heat conduction factor into the workpiece, R ws is the heat rate transferred to the workpiece in the workpiece-grinding wheel system, is the heat rate transferred to the workpiece in the workpiece-grinding chip subsystem; S5 establishes the mapping relationship between grinding processing conditions, spindle power, and grinding burn level; S6 installs voltage and current sensors in the machine tool electrical cabinet. The computer collects the spindle voltage and current through a data acquisition card. The power signal is collected according to the three-phase power measurement principle. Based on the collected power signal, filtering and noise reduction signal processing operations are performed in the host computer. The maximum grinding power is then obtained by subtracting the idling power during non-machining from the maximum power during machining. This is compared with the corresponding spindle power threshold to determine whether the spindle power threshold is exceeded, and then determine whether burns have occurred and the burn level, realizing intelligent monitoring of grinding burns. In step S4 h w The formula is in, k is the thermal conductivity of the workpiece, ρ is the density of the workpiece, c is the specific heat capacity of the workpiece, v w is the feed speed of the workpiece, l c is the contact arc length of the grinding zone, C is a parameter, and its specific value is related to the Parklet number and the average contact angle of the contact area; In step S4 R ws The formula is in, λ g is the thermal conductivity of the abrasive, γ 0 is the effective contact radius of the abrasive top surface after averaging, v s is the speed of the grinding wheel; In step S4 The formula is in, is the thermal diffusivity of the wear debris, is the shear strain in the chip formation zone, is the thickness of deformed wear debris; The formula of S in step S4 is: in, a p For cutting depth, d s is the grinding wheel diameter, B is the grinding width; The correlation model between the spindle power and the grinding temperature in step S4 is based on the workpiece surface temperature model established by Jin Tan and David Stephenson, and a conversion model between the grinding temperature threshold T and the spindle power threshold P is established; Q t is the total heat flux density in the grinding area, which can be obtained from the ratio of the actual spindle power consumption to the grinding contact area. Q t The formula is Substitute the Qt formula into the workpiece surface temperature model to obtain the correlation model between the spindle power P and the grinding temperature T.

2. The intelligent monitoring method for grinding burn and its grade based on power signal according to claim 1, characterized in that: The setting of the grinding temperature thresholds for different grinding burn levels in step S3 takes into account the material properties and is set according to the degree of grinding burn of the material at different grinding temperatures, mainly based on the surface color. The corresponding relationship between the grinding temperature and the spindle grinding power corresponding to different degrees of grinding burn is calibrated by experiments.

3. The intelligent monitoring method for grinding burn and its grade based on power signal according to claim 1, characterized in that: In step S5, the severity of the grinding burn of the ground specimen is quantified by the grinding power in combination with the early burn grade classification.

4. The intelligent monitoring method for grinding burn and its grade based on power signal according to claim 1, characterized in that: In step S6, the power signal is obtained by numerical integration of voltage and current based on the three-phase electric power measurement principle.

5. The intelligent monitoring method for grinding burn and its grade based on power signal according to claim 1, characterized in that: In step S4, a safety factor is set C safe It is used to adjust the reliability of power monitoring alarm, with a value between 0 and 1. The safety factor multiplied by the critical power is the warning power, which can achieve early warning.

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