Universal control system for realizing numerical control machining control of cold punching die

Abstract

The invention discloses a universal control system for realizing numerical control machining control of a cold punching die. The universal control system comprises a multi-sensor data acquisition module, a data analysis module and a machining execution module. The multi-sensor data acquisition module adopts a cutter wear sensor and a cutting temperature sensor to acquire wear vector and temperature vector data at uniform intervals within a preset time period, and transmits the wear vector and temperature vector data to the data analysis module in a wireless mode. A denoising and standardization preprocessing unit is arranged in the data analysis module, fusion analysis is conducted on the collected data, cutting parameters of the cutter in the next time period are obtained according to a temperature and abrasion double-wheel judgment mechanism, and shutdown is triggered or cutting force is automatically adjusted when the cutting parameters are not qualified. And the processing execution module executes processing operation according to the analysis result. The system realizes real-time monitoring of the state of the cutter and intelligent parameter optimization, can effectively improve the machining precision and consistency of the cold punching die, prolongs the service life of the cutter, reduces the cost, and has the advantages of high universality and flexible deployment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cold stamping die manufacturing, and particularly to a universal control system for realizing numerical control machining control of cold stamping dies. BACKGROUND

[0002] Cold stamping dies are important forming tools in manufacturing industry, and are widely used in the fields of automobiles, home appliances, aerospace, etc. The manufacturing process of cold stamping dies is complex, involving multiple procedures, especially the machining of the cavity and surface of the cold stamping dies, which requires extremely high precision and surface quality. Traditional cold stamping die machining mainly relies on manual experience, which is low in efficiency and difficult to ensure the consistency of machining precision.

[0003] In the prior art, although some numerical control machining systems have been applied to the machining of cold stamping dies, there is a common problem that the tool wear and workpiece deformation are difficult to be monitored and compensated in real time, which affects the machining precision.

[0004] Therefore, there is an urgent need for a universal numerical control machining control system for cold stamping dies, which can solve the above problems, improve the machining precision, and reduce the material cost. SUMMARY

[0005] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification of the present application in order to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0006] In view of the above problems existing in the prior art numerical control machining system for cold stamping dies, the present application is proposed.

[0007] Therefore, the technical problem solved by the present application is to solve the problem that the tool wear and workpiece deformation are difficult to be monitored and compensated in real time, which affects the machining precision, in the prior art numerical control machining system for cold stamping dies.

[0008] To solve the above technical problems, the application provides the following technical scheme: a general control system for realizing cold punching die numerical control machining control, comprising the following components: a multi-sensor data acquisition module, which acquires tool state data in a preset time period in real time according to multiple types of sensors arranged and wirelessly transmits the tool state data to a data analysis module; the data analysis module is wirelessly connected with the multi-sensor data acquisition module, receives the acquired tool state data, and performs fusion analysis on the tool state data in the preset time period to obtain tool cutting parameters in the next same interval time period; and a machining execution module, which comprises a numerical control machine tool and an execution mechanism, is connected with the data analysis module and is used for performing machining operation according to the tool cutting parameters in the next same interval time period obtained through analysis.

[0009] As a preferred scheme of the general control system for realizing cold punching die numerical control machining control, the multi-sensor data acquisition module specifically comprises a tool wear sensor and a cutting temperature sensor; and the acquired tool state data specifically comprises a wear vector obtained according to the tool wear sensor and a temperature vector obtained according to the cutting temperature sensor.

[0010] As a preferred scheme of the general control system for realizing cold punching die numerical control machining control, the data analysis module further embeds a data preprocessing unit, performs data preprocessing in advance after receiving the tool state data, and then transmits the tool state data to an analysis unit for data analysis; and the data preprocessing step specifically comprises denoising and standardization processing.

[0011] As a preferred scheme of the general control system for realizing cold punching die numerical control machining control, the data analysis module performs fusion analysis on the tool state data in the preset time period to obtain tool cutting parameters in the next same interval time period, which specifically comprises the following steps: S1: evenly interval collecting temperature vectors and wear vectors of each current point in the preset time period to form a set of temperature vector data and a set of wear vector data in the preset time period; S2: obtaining a variance fluctuation value and an average temperature value of the set of temperature vector data; S3: when the variance fluctuation value and the average temperature value both meet the standard, determining that the current cutting parameter is qualified in one round, the parameter is available, and turning to step S4; when either the variance fluctuation value or the average temperature value does not meet the standard, determining that the current cutting parameter is unqualified in one round, the parameter is unavailable, and determining that the cutting under the current parameter has a risk, i.e., stopping control; S4: obtaining a feature offset value of the set of wear vector data; and S5: when the feature offset value meets the standard, determining that the current cutting parameter is qualified in two rounds, the parameter can be used in the next same interval time period; when the feature offset value does not meet the standard, determining that the current cutting parameter is unqualified in two rounds, and adjusting the current tool cutting parameter according to the feature offset value.

[0012] As a preferred scheme of the general control system for realizing numerical control machining control of cold stamping die, in the step S5, the current tool cutting parameter is adjusted according to the feature offset value, specifically, the tool cutting force is adjusted only when the cutting fluid placement parameter is unchanged.

[0013] As a preferred scheme of the general control system for realizing numerical control machining control of cold stamping die, in the step S5, the current tool cutting parameter is adjusted according to the feature offset value, specifically, the tool cutting force is adjusted only when the cutting fluid placement parameter is unchanged.

[0014] Wherein, ε2 is the adjusted tool cutting force, unit is N; ε1 is the unadjusted tool cutting force, unit is N; η is the feature offset value.

[0015] As a preferred scheme of the general control system for realizing numerical control machining control of cold stamping die, in the step S5, the current tool cutting parameter is adjusted according to the feature offset value, specifically, the tool cutting force is adjusted only when the cutting fluid placement parameter is unchanged.

[0016] The general control system for realizing numerical control machining control of cold stamping die has the following beneficial effects:

[0017] 1. Improve the machining precision and consistency: through the real-time collection of tool wear vectors and cutting temperature vectors by multiple types of sensors, and the fusion analysis in the data analysis module, the tool machining state can be mastered in real time, the machining parameters can be scientifically evaluated, and timely adjustment can be made, so that the machining error can be effectively reduced, and the consistency of the size precision and surface quality of the cold stamping die can be improved.

[0018] 2. Realize the automatic optimization of machining parameters: the system adopts a double-wheel judgment mechanism to judge the eligibility of temperature parameters and wear parameters in stages, and automatically adjusts the cutting force when the standard is not met, realizes the intelligent optimization of machining parameters, reduces the dependence on manual experience, and improves the machining efficiency.

[0019] 3. Effectively prevent machining risks: when the temperature fluctuation value, average temperature value or wear feature offset value exceeds the set standard, the system can immediately trigger the stop control or parameter adjustment, prevent the workpiece from being scrapped or the equipment from being damaged due to tool abnormalities, and ensure the machining safety and stability.

[0020] 4. Prolong the tool life and reduce the cost: through real-time monitoring and dynamic adjustment of the cutting force, the excessive wear of the tool caused by overload or overheating is reduced, the tool life is prolonged, the tool replacement frequency and related downtime losses are reduced, and thus the machining cost is reduced.

[0021] 5. Strong generality and deployment flexibility: the system structure has generality and can be adapted to various cold stamping die machining equipment. Wireless communication is adopted between the sensor and the data analysis module, reducing wiring restrictions and facilitating rapid deployment and use in different machine tools and processing environments.

[0022] 6. High data processing accuracy and strong anti-interference capability: the data analysis module has a built-in denoising and standardized data preprocessing unit, which can improve the accuracy and stability of the collected data. Meanwhile, the scheme of uniformly spaced 15 times of collection within a preset time period ensures the representativeness of the data and the reliability of the analysis, effectively reducing the impact of occasional interference on processing control. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0024] Figure 1 The system module diagram of the general control system for realizing the numerical control machining control of cold stamping die provided by the present application.

[0025] Figure 2 The overall method flowchart of the data analysis module provided by the present application for fusion analysis of the tool state data within a preset time period to obtain the tool cutting parameter in the next uniformly spaced time period.

[0026] Figure 3 It is an average temperature change trend chart.

[0027] Figure 4 It is a wear offset value change trend chart.

[0028] Figure 5 It is a machining size deviation change trend chart. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0030] In the prior art, although some numerical control machining systems have been applied to the machining of cold stamping dies, there is a common problem that tool wear and workpiece deformation are difficult to monitor and compensate in real time, affecting the machining accuracy.

[0031] Therefore, the scheme of the present application provides a general control system for realizing numerical control machining control of cold punching die, refer to the following examples:

[0032] Example 1

[0033] Referring to Figure 1 , the present application provides a general control system for realizing numerical control machining control of cold punching die, comprising the following components:

[0034] The multi-sensor data acquisition module 100 acquires the tool state data in a preset time period in real time according to the multi-type sensors arranged, and wirelessly transmits the tool state data to the data analysis module 200;

[0035] The data analysis module 200 is wirelessly connected with the multi-sensor data acquisition module 100, receives the collected tool state data, and performs fusion analysis on the tool state data in the preset time period to obtain the tool cutting parameters in the next same interval time period;

[0036] The machining execution module 300 comprises a numerical control machine tool and an execution mechanism, and is connected with the data analysis module 200 for data, and is used for executing machining operation according to the tool cutting parameters in the next same interval time period obtained by analysis.

[0037] Specifically, the multi-sensor data acquisition module 100 specifically comprises: a tool wear sensor and a cutting temperature sensor;

[0038] The collected tool state data specifically comprises: a wear vector obtained according to the tool wear sensor; and a temperature vector obtained according to the cutting temperature sensor.

[0039] It should be noted that the tool wear sensor and the cutting temperature sensor involved in the present application are all selected from existing sensors, and are summarized as follows:

[0040] (1) Tool wear sensor

[0041] ① Function: Real-time monitoring of tool wear state in the machining process.

[0042] ② Working principle: Common implementation methods include:

[0043] Visual detection type (obtaining tool image through high-resolution camera / microscopic imaging, and calculating cutting edge wear width or morphology change through image processing algorithm);

[0044] Acoustic emission detection type (using sensors to collect high-frequency acoustic wave characteristics released during metal cutting to indirectly calculate tool wear degree);

[0045] Electric signal monitoring type (using cutting current, resistance or vibration signal change to judge wear state);

[0046] ③Output data: Wear vector

[0047] "Vector" means: An array that contains multiple dimensions of wear characteristics, such as: wear amount of tool rake face and flank face; wear change rate in different directions; unevenness of wear distribution;

[0048] This not only can determine how much the tool is worn, but also know which direction is the most serious wear, which is crucial for subsequent cutting parameter adjustment.

[0049] Here, considering the computing power, the wear degree in a single plane and a single direction can be used as a generalized wear vector;

[0050] (2) Cutting temperature sensor

[0051] ①Function: Monitor the instantaneous temperature of the tool-workpiece contact area during cutting.

[0052] ②Common techniques:

[0053] Thermocouple type: Embedded thermocouple in tool or tool holder, directly measure temperature through thermoelectric effect.

[0054] Infrared temperature measurement type: Use infrared radiation detector (such as infrared optical fiber or high-precision infrared camera) to non-contact measure cutting area temperature.

[0055] Optical fiber temperature sensor: Strong anti-interference, can be used in high electromagnetic noise environment.

[0056] ③Output data: Temperature vector

[0057] Represents the temperature data set at different times or different collection points within a collection period.

[0058] For example, collecting multiple temperature values at fixed time intervals within a preset time period can form a temperature change curve, and then analyze temperature stability and fluctuation range.

[0059] It should be noted that the meanings of "wear vector" and "temperature vector" are as follows:

[0060] In this invention, these two vectors belong to multi-dimensional time series data, they are not only a instantaneous parameter, but also a data set collected at uniform intervals within a preset time period.

[0061] Wear vector: Reflects the dimensional change of tool wear within a period of time, which can be used to predict the life stage of the tool (initial stage-stable stage-rapid wear stage).

[0062] Temperature vector: Reflects the trend and stability of temperature changes during cutting. High temperature fluctuations may indicate cutting force fluctuations, material hard spots, or poor lubrication.

[0063] The roles of the "wear vector" and "temperature vector" in the system of the present application are summarized as follows:

[0064] Real-time state awareness: These two vectors are one of the core indicators for determining the working state of the tool.

[0065] Data fusion analysis: The data analysis module will comprehensively calculate the temperature vector and wear vector (such as variance, average value, and characteristic deviation value) within a preset time period and use it to determine whether the cutting parameters are qualified.

[0066] Risk warning: If the temperature fluctuation is too large or the wear characteristic deviation value exceeds the standard, the system will immediately shut down or adjust the cutting parameters to prevent the spread of abnormalities.

[0067] Automatic optimization: Based on the analysis results, the system can automatically adjust the tool cutting force to achieve dynamic optimization of the machining process.

[0068] Further, the data analysis module 200 also has a data preprocessing unit embedded therein, which, after receiving the state data of the tool, pre-processes the data and then transmits it to the analysis unit for data analysis.

[0069] Specifically, the data preprocessing steps are denoising and standardization processing.

[0070] It should be noted that the original data collected by the sensor, such as the tool wear vector and cutting temperature vector, often has noise and scale differences. If it is directly sent to the analysis unit, it may lead to misjudgment (such as sudden temperature changes caused by short-term interference) and parameter distortion (different dimensions of data have inappropriate weights when analyzed). Therefore, denoising and standardization processing are required.

[0071] Specifically:

[0072] ① Denoising processing

[0073] Denoising is to remove or suppress non-real abnormal fluctuations in the original data.

[0074] Possible sources of noise: machine vibration interference signals, sensor instantaneous abnormal readings caused by electromagnetic interference, and bit errors during data transmission.

[0075] Common denoising methods:

[0076] Moving Average Filter

[0077] The mean value of a time window of data is taken to smooth short-term fluctuations.

[0078] Median Filter

[0079] It can effectively suppress short-time spike noise, especially suitable for the case of sensor burst outliers.

[0080] Butterworth / Chebyshev, etc.

[0081] It retains low-frequency effective signals and removes high-frequency interference components.

[0082] Wavelet denoising

[0083] It decomposes and removes noise layers for non-stationary signals (such as tool temperature changes), and retains useful frequency components.

[0084] ② Standardization

[0085] The purpose of standardization is to eliminate the influence of different data dimensions and numerical ranges, so that each feature has comparability and balance in fusion analysis.

[0086] Problem:

[0087] Wear vector in units of μm (microns), the value is very small;

[0088] Temperature vector in units of ℃, the value is relatively large;

[0089] If not handled, the change of temperature value will dominate in the calculation of variance or feature distance;

[0090] Common standardization methods: Z-score standardization and Min-Max normalization;

[0091] Map the data to the [0, 1] interval, which is convenient for fusion calculation.

[0092] The benefits in the present invention:

[0093] The numerical scales of the temperature and wear data sets are unified;

[0094] Improve the accuracy of decision-making in fusion analysis;

[0095] Avoid temperature / wear single indicator "domination";

[0096] Signal quality improvement: remove invalid signals and interference in the acquisition process;

[0097] Feature analysis accuracy is improved: avoid decision bias caused by unit and magnitude differences;

[0098] System robustness is enhanced: maintain consistent decision-making in different environments, different machine tools, and different batches of processing;

[0099] To lay the foundation for the subsequent "two-wheel judgment mechanism", ensure that the variance fluctuation value, average temperature value, characteristic offset in steps S2, S4 are reliable.

[0100] Further, referring to Figure 2 , the data analysis module 200 fuses and analyzes the tool state data in the preset time period to obtain the tool cutting parameters in the next uniform interval time period, which specifically includes the following steps:

[0101] S1: In the preset time period, the temperature vector and wear vector of each current point are collected uniformly and interval by interval to form a set of temperature vector set and a set of wear vector set in the preset time period;

[0102] It should be noted that: in the preset time period, the temperature vector and wear vector of each current point are collected uniformly and interval by interval for 15 times.

[0103] A fixed monitoring time period is preset, for example, 1 minute, 5 minutes, etc. (depending on the processing rhythm and process requirements).

[0104] In this time period, the sensor data is collected uniformly and interval by interval for 15 times:

[0105] Temperature vector: record the current tool cutting zone temperature value (which can be multi-point temperature measurement multi-dimensional data) each time

[0106] Wear vector: record the multi-dimensional value of tool wear state (which can include the wear amount of different edge regions, wear inclination angle, etc.) each time

[0107] In this way, we will get:

[0108] A temperature vector set T = {T1, T2, …, T 15}

[0109] A wear vector set W = {W1, W2, …, W 15}

[0110] Key technical details

[0111] Uniform interval sampling: ensure that the data covers the whole process of the time period and does not miss the processing trend.

[0112] 15 times of collection: this number is a balance point selected through processing stability analysis - enough to reflect the trend, and will not produce too much data redundancy.

[0113] Vector meaning: each T i or W i can be a multi-dimensional value, for example, the temperature of different contact areas of the tool forms a temperature vector.

[0114] S2: Obtain the variance fluctuation value and average temperature value of a set of temperature vector set data;

[0115] Average temperature value:

[0116]

[0117] Reflect the overall thermal load level during processing.

[0118] Variance fluctuation value:

[0119]

[0120] Measure the stability of temperature in this time period, the lower the fluctuation, the more stable the cutting process.

[0121] Temperature is a key factor affecting tool life and workpiece accuracy in cutting process, high fluctuation often means unstable cutting force or material hard point problem.

[0122] When calculating, the temperature vector set needs to be denoised and standardized first to ensure the accuracy of the fluctuation analysis.

[0123] S3: When the variance fluctuation value and average temperature value meet the standard, it is determined that the current cutting parameter is qualified for one round, the parameter is available, and the S4 step is entered; when either the variance fluctuation value or the average temperature value does not meet the standard, it is determined that the current cutting parameter is not qualified for one round, the parameter is not available, and it is determined that the cutting under the current parameter is at risk, i.e. stop control;

[0124] Determination rule

[0125] Standard threshold setting:

[0126] Set a maximum allowed temperature fluctuation variance threshold Var max ;

[0127] Set a safe average temperature range [T min , T max ];

[0128] Determination condition:

[0129] If Var(T)≤Var max and T min ≤T≤T max → one round is qualified;

[0130] Otherwise → one round is not qualified;

[0131] Immediately determine that the current cutting is at risk;

[0132] Start stop control (pause processing to prevent tool or workpiece damage);

[0133] The dual-index determination advantage: single temperature mean may mask high volatility risk, single volatility value may mask sustained high temperature risk, and the combination of the two is safer.

[0134] Stop control strategy: there are two implementation methods:

[0135] Send an emergency stop signal to the numerical control system;

[0136] The spindle speed and feed rate are quickly reduced to a safe value and an alarm is reported.

[0137] It should be noted that: when the user uses, can according to the actual situation and the expected to reach the cutting judgment target to Var max and [T min , T max ] are set separately.

[0138] S4: Obtain a set of feature offset values of the wear vector set data;

[0139] S5: When the feature offset value meets the standard, it is determined that the current cutting parameter is qualified for two rounds, and the parameter can be used to the next interval period; when the feature offset value does not meet the standard, it is determined that the current cutting parameter is unqualified for two rounds, and the current tool cutting parameter is adjusted according to the feature offset value.

[0140] Determination rule

[0141] If the feature offset value ≤ set threshold η max → two rounds of qualified (tool state healthy, current cutting parameter can continue to be used in the next cycle);

[0142] If the offset value > set threshold → two rounds of unqualified (tool wear enters risk state), then:

[0143] Keep the cutting fluid parameters unchanged, and adjust the tool cutting force according to the following model: ε2=ε1×f(η), where ε1 is the cutting force before adjustment, η is the feature offset value, and the function f reduces the cutting force according to the offset size.

[0144] It should be noted that in step S5, the adjustment of the current tool cutting parameter according to the feature offset value is: under the condition that the cutting fluid placement parameter is unchanged, only the tool cutting force is adjusted.

[0145] Only adjust the cutting force and not the cutting fluid: avoid the interference of additional introduction of liquid flow / viscosity change on temperature.

[0146] Force adjustment method: reduce spindle cutting power, reduce feed speed or cutting depth, thereby reducing cutting force.

[0147] It should be noted that: when the user uses, can according to the actual situation and the expected to reach the cutting judgment target to ηmax The single setting is performed.

[0148] Further, the tool cutting force is adjusted according to the following model:

[0149]

[0150] Wherein, ε2 is the adjusted tool cutting force, unit is N; ε1 is the tool cutting force before adjustment, unit is N; η is the characteristic offset value (i.e. variance value).

[0151] It should be noted that: the above characteristic offset value is selected as the variance value of the wear vector set data.

[0152] In addition, it should be noted that:

[0153] 1. Temperature determination criteria in S2 / S3

[0154] Average temperature value range:

[0155] Reference value: 200℃ ~ 450℃

[0156] Reason:

[0157] For high speed steel or carbide tool, cutting temperature below 200℃ is generally in low load state, and the machining efficiency is low;

[0158] Higher than 450℃ is easy to cause tool edge annealing or coating failure, and increase the risk of wear.

[0159] Variance fluctuation threshold:

[0160] Reference value: not more than (15℃)² ≈ 225 (°C²);

[0161] Indicates that the temperature standard deviation is less than or equal to 15℃;

[0162] Reason:

[0163] In practice, the temperature change range during stable cutting is generally ±10~15℃, and if it exceeds this value, it means that the cutting force or material condition fluctuation is large.

[0164] Determination rule example:

[0165] If it meets:

[0166]

[0167] Where: TmT min =200℃, TmT max =450℃, VaVar max =225°C 2 → One round is qualified, otherwise one round is unqualified.

[0168] 2. Standard determination value of wear index in S4 / S5 (variance type)

[0169] Wear vector set W = {W1, W2,..., W 15} represents the amount of tool wear in a preset time period (may include the wear depth or edge width of multiple measurement points). Characteristic offset value: reflects the uniformity and stability of wear in the cycle.

[0170] Reference standard value

[0171] Index Determination condition Reference value Unit Explanation Wear variance Var(W) Upper limit of qualification ≤ 9 μm² Corresponding standard deviation ≤ 3 μm, indicating that the tool wear is stable within the monitoring period Average wear increment (optional) Monitoring trend ≤ 5 μm / period Exceeding, indicating that the tool enters the rapid wear stage (can be used as an additional warning)

[0172] Determination logic

[0173] Var(W) ≤ 9 μm² → two rounds of qualified, the current cutting parameters can be continued to the next cycle;

[0174] Otherwise → two rounds of unqualified, adjust the tool cutting force according to the wear fluctuation degree.

[0175] 3. Comprehensive determination table (temperature + wear)

[0176] Step Data source Determination index Qualification standard Unit Determination result S2 / S3 round Temperature vector set T Average temperature value 200~450 ℃ Otherwise stop Temperature variance ≤ 225 ℃² Otherwise stop S4 / S5 round Wear vector set W Wear variance ≤ 9 μm² Otherwise adjust the cutting force (Optionally) Average wear increment ≤ 5 μm / period Otherwise as a warning

[0177] Example 2

[0178] Reference Figure 1 The application provides a general control system for realizing numerical control machining control of cold stamping dies, comprising the following components:

[0179] The multi-sensor data acquisition module 100 acquires tool state data in a preset time period in real time according to a plurality of types of sensors arranged, and wirelessly transmits the tool state data to the data analysis module 200;

[0180] The data analysis module 200 is wirelessly connected with the multi-sensor data acquisition module 100, receives the acquired tool state data, and performs fusion analysis on the tool state data in the preset time period to obtain tool cutting parameters in the next same interval time period;

[0181] The machining execution module 300 comprises a numerical control machine tool and an execution mechanism, is connected with the data analysis module 200, and is used for executing machining operation according to the tool cutting parameters in the next same interval time period obtained by analysis.

[0182] Specifically, the multi-sensor data acquisition module 100 specifically comprises a tool wear sensor and a cutting temperature sensor.

[0183] The acquired tool state data specifically comprises wear vectors obtained according to the tool wear sensor and temperature vectors obtained according to the cutting temperature sensor.

[0184] Further, the data analysis module 200 also embeds a data preprocessing unit, after receiving the state data of the tool, the data is preprocessed, and then transmitted to the analysis unit for data analysis;

[0185] The data preprocessing step is specifically: denoising and standardization.

[0186] Further, the data analysis module 200 fuses and analyzes the tool state data in the preset time period to obtain the tool cutting parameters in the next same interval time period, which includes the following steps:

[0187] S1: In the preset time period, the temperature vector and the wear vector of each current point are collected in uniform intervals, and a set of temperature vector set and a set of wear vector set in the preset time period are formed;

[0188] S2: Obtain the variance fluctuation value and the average temperature value of the set of temperature vector set data;

[0189] S3: When the variance fluctuation value and the average temperature value meet the standard, it is determined that the current cutting parameter is qualified for one round, and the parameter is available, and step S4 is entered; when any of the variance fluctuation value and the average temperature value does not meet the standard, it is determined that the current cutting parameter is not qualified for one round, and the parameter is not available, and it is determined that the cutting under the current parameter exists risk, that is, stop control;

[0190] S4: Obtain the feature offset value of the set of wear vector set data;

[0191] S5: When the feature offset value meets the standard, it is determined that the current cutting parameter is qualified for two rounds, and the parameter can be used to the next same interval time period; when the feature offset value does not meet the standard, it is determined that the current cutting parameter is not qualified for two rounds, and the current tool cutting parameter is adjusted according to the feature offset value.

[0192] Further, in step S5, the adjustment of the current tool cutting parameter according to the feature offset value is specifically: under the condition that the cutting fluid placement parameter is unchanged, only the tool cutting force is adjusted.

[0193] Further, the tool cutting force is adjusted according to the following model:

[0194]

[0195] Wherein, ε2 is the adjusted tool cutting force, unit is N; ε1 is the tool cutting force before adjustment, unit is N; η is the feature offset value; k is the adjustment proportion coefficient (recommended 0.1~0.2, to avoid the influence of the processing efficiency caused by the too large reduction).

[0196] The more η exceeds the standard, the greater the force reduction ratio will be, but it will not exceed 20%.

[0197] Specifically, within a preset time period, the temperature vector and the wear vector of each current point are collected 15 times at even intervals.

[0198] It should be noted that, in this embodiment, the feature offset value is expressed as a two-norm.

[0199] That is: S4 / S5 wear characteristic offset value judgment standard (based on the second norm)

[0200] Data: Wear vector set

[0201] W={W1, W2, ..., W 15}

[0202] Where W i The wear amount obtained from one collection (can be in μm).

[0203] Feature offset calculation method (L2 Norm):

[0204]

[0205] n=15 (number of sampling times in the preset time period)

[0206] If W i If it is a multi-dimensional value (such as wear at multiple measuring points), first sum the squares of the values ​​at each measuring point, and then take the square root of the accumulated value of the 15 sampling points.

[0207] Reference threshold:

[0208] Carbide die tools:

[0209] η max =35 μm;

[0210] Reason: This value roughly corresponds to the critical state where the tool changes from stable wear to rapid wear.

[0211] Decision logic:

[0212] η≤35 μm → Second round qualified, maintain cutting parameters;

[0213] η>35 μm → Second round failed, perform cutting force adjustment;

[0214] Comprehensive judgment standard table

[0215] Step Data source Determination index Qualification standard Unit Unqualified treatment S2 / S3 Temperature vector set T Average temperature 200~450 ℃ Stop Temperature variance ≤ 225 ℃² Stop S4 / S5 Wear vector set W Feature offset value (L2 Norm) ≤ 35 μm Reduce the cutting force

[0216] The present invention provides a universal control system for realizing CNC machining control of cold stamping dies, which has the following beneficial effects:

[0217] 1. Improve processing precision and consistency: Through real-time collection of tool wear vectors and cutting temperature vectors by multiple types of sensors, and fusion analysis in the data analysis module, the tool processing state can be mastered in real time, the processing parameters can be scientifically evaluated, and timely adjustment can be made, thereby effectively reducing processing errors and improving the consistency of cold stamping die size precision and surface quality.

[0218] 2. Realize automatic optimization of processing parameters: The system uses a double-wheel judgment mechanism to judge the eligibility of temperature parameters and wear parameters in stages, and automatically adjusts the cutting force when it does not meet the standard, realizes intelligent optimization of processing parameters, reduces the dependence on artificial experience, and improves processing efficiency.

[0219] 3. Effectively prevent processing risks: When the temperature fluctuation value, average temperature value or wear characteristic offset value exceeds the set standard, the system can immediately trigger stop control or parameter adjustment to prevent tool abnormalities from causing workpiece scrap or equipment damage, and ensure processing safety and stability.

[0220] 4. Prolong tool life and reduce costs: By real-time monitoring and dynamic adjustment of cutting force, excessive wear of the tool due to overload or overheating is reduced, the tool life is prolonged, the tool replacement frequency and related downtime losses are reduced, thereby reducing processing costs.

[0221] 5. Strong versatility and deployment flexibility: The system structure has versatility and can adapt to various cold stamping die processing equipment, and wireless communication is used between the sensor and the data analysis module to reduce wiring restrictions and facilitate rapid deployment and use in different machine tools and processing environments.

[0222] 6. High data processing accuracy and strong anti-interference ability: The data analysis module has a built-in denoising and standardized data preprocessing unit that can improve the accuracy and stability of collected data, while the evenly spaced 15 times collection scheme within a preset time period ensures the representativeness of the data and the reliability of the analysis, effectively reducing the impact of occasional interference on processing control.

[0223] In order to verify the beneficial effects of the present application, the following experiments are carried out:

[0224] I. Purpose of the test

[0225] The technical advantages of the cold stamping die numerical control processing control system of the present application in the actual processing process are verified, mainly from the following aspects:

[0226] Whether it can monitor the tool state in real time and judge the processing risk.

[0227] Whether the double-wheel judgment mechanism (temperature + wear) can significantly improve the processing precision and consistency.

[0228] Whether the automatic adjustment of cutting force can prolong tool life and reduce processing cost.

[0229] Whether there is a significant improvement in machining precision, tool life, and machining efficiency compared with traditional machining methods.

[0230] II. Test conditions

[0231] Item Parameter Workpiece being machined Cold stamping die steel Cr12MoV, hardness HRC58 Machining equipment Numerical control milling machine + wireless data acquisition module Tool type Hard alloy flat tool Φ12 mm Initial cutting force of tool 150 N Cutting fluid flow Fixed at 3 L / min (not adjusted during the test) Monitoring sensor Tool wear sensor, infrared cutting temperature sensor Sampling frequency Uniformly collect temperature and wear data 15 times per preset period Machining parameter Spindle speed 8000 rpm; feed speed 0.08 mm / rev; cutting depth 0.5 mm Determination standard Average temperature 200-450 ℃; temperature variance ≤ 225 ℃²; wear offset value ≤ 35 μm

[0232] III. Test methods and steps

[0233] Control group: traditional numerical control machining method is adopted without real-time monitoring and automatic adjustment.

[0234] Experimental group: the universal control system of the present application is adopted to realize double-wheel judgment and dynamic adjustment of cutting force.

[0235] The same batch of 20 cold stamping die parts is machined, and the following data is counted:

[0236] Mean and variance of temperature per cycle

[0237] Wear characteristic offset value per cycle

[0238] Cutting force adjustment

[0239] Machining size deviation

[0240] Tool life (cumulative number of machined parts until replacement)

[0241] The data is statistically analyzed and compared.

[0242] IV. Data record

[0243] 4.1 Experimental group running data (system of the present application)

[0244] Period Average temperature (℃) Temperature variance (℃²) Wear offset value (μm) Determination result Cutting force adjustment (N) Machining size deviation (μm) 1 320 180 18 Qualified 150 (unchanged) ±3 2 335 200 20 Qualified 150 (unchanged) ±3 3 340 198 28 Qualified 150 (unchanged) ±3 4 345 210 36 Two-round unqualified 140 (decrease by 6.7%) ±4 5 338 190 30 Qualified 140 (unchanged) ±4 6 350 205 38 Two-round unqualified 125 (decrease by 10.7%) ±5 7 342 215 32 Qualified 125 (unchanged) ±5

[0245] 4.2 Control group running data (traditional method)

[0246] Period Average temperature (℃) Temperature variance (℃²) Wear offset value (μm) Determination result Cutting force adjustment (N) Machining size deviation (μm) 1 330 190 18 — 150 (unchanged) ±4 2 350 260 30 — 150 (unchanged) ±6 3 370 280 40 — 150 (unchanged) ±8 4 385 300 45 — 150 (unchanged) ±9 5 400 320 50 — 150 (unchanged) ±10

[0247] 4.3 Tool life comparison

[0248] Group Number of machined pieces of tool Tool replacement frequency Average machining size deviation (μm) Total number of machining stoppages Experimental group (system of the application) 35 pieces Replace every 35 pieces ±4 1 time (temperature overrun stop) Control group (traditional method) 20 pieces Replace every 20 pieces ±7 3 times (tool collapse stop)

[0249] V. Data analysis

[0250] Machining precision:

[0251] The machining size deviation of the experimental group fluctuates stably at ±310 μm.

[0252] The system effectively reduces dimensional drift by monitoring and adjusting cutting force in real time.

[0253] Cutting force dynamic adjustment effect:

[0254] When the wear offset value of the experimental group exceeds the standard, the cutting force is reduced by 6.7%~10.7%, preventing rapid tool wear and delaying the time of entering the failure stage.

[0255] The control group has rapid temperature rise and wear surge due to unadjusted cutting force.

[0256] Tool life:

[0257] The tool life of the experimental group is improved by 75% (35 pieces vs 20 pieces).

[0258] Tool replacement frequency is reduced, and downtime is reduced.

[0259] Processing efficiency:

[0260] Although the experimental group has an increase of about 5% in individual cycle processing time due to the adjustment of cutting force, the overall productivity is higher than that of the control group because of the reduction of tool replacement and low downtime.

[0261] Six, test conclusion

[0262] Additional reference Figure 3 (Average temperature trend), 4 (Wear offset value trend), 5 (Processing dimensional deviation trend);

[0263] Figure 3 :

[0264] The temperature of the experimental group is stable in the interval of 320~350 ℃, and the temperature variance is lower than 225 ℃².

[0265] The temperature of the control group rises rapidly, and the temperature fluctuation is significantly above the safety variance threshold, which has the risk of tool annealing.

[0266] Figure 4 :

[0267] After the cutting force is reduced in the experimental group after detecting the exceeding standard in cycle 4 and 6, the subsequent wear value decreases to within the threshold.

[0268] The control group does not adjust the cutting force, and the wear continues to rise, leading to rapid tool entering the failure stage.

[0269] Figure 5 (The absolute value of the deviation is used as the curve standard):

[0270] The dimensional deviation of the experimental group is stable at ±3~±5 μm, fully meeting the high precision requirements of cold stamping dies.

[0271] The deviation of the control group increases cycle by cycle, and finally reaches ±10 mu m, and the mold precision decreases obviously.

[0272] The universal control system of the application can realize real-time monitoring and risk prediction through the temperature and wear double-wheel judgment mechanism, automatically adjust the cutting parameters and trigger the shutdown protection when the temperature fluctuates or the wear exceeds the standard.

[0273] The system significantly prolongs the tool life while ensuring the machining precision, reduces the replacement frequency, and reduces the comprehensive machining cost by about 20%.

[0274] The verification results prove that the scheme of the application is superior to the traditional machining method, and is especially suitable for high-precision and long-period cold stamping die machining tasks.

[0275] It should be noted that the above examples are only used to illustrate the technical solutions of the application and are not limiting. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and they should be covered in the scope of the claims of the application.

Claims

1. A universal control system for realizing CNC machining control of cold stamping dies, characterized in that: Includes the following components: A multi-sensor data acquisition module (100) collects tool status data within a preset time period in real time based on the layout of multiple types of sensors, and wirelessly transmits the data to a data analysis module (200); The data analysis module (200) is wirelessly connected to the multi-sensor data acquisition module (100), receives the acquired tool status data, and performs a fusion analysis on the tool status data within a preset time period to obtain tool cutting parameters within the next interval time period; A processing execution module (300), comprising a numerically controlled machine tool and an execution mechanism, is data-connected to the data analysis module (200) and is used to execute a processing operation according to the tool cutting parameters within the next same interval time period obtained through analysis; Wherein, the multi-sensor data acquisition module (100) specifically includes: a tool wear sensor and a cutting temperature sensor; The tool status data collected specifically include: a wear vector obtained by a tool wear sensor; a temperature vector obtained by a cutting temperature sensor; The data analysis module (200) is further embedded with a data pre-processing unit, which pre-processes the data after receiving the status data of the tool, and then transmits the data to the analysis unit for data analysis; Among them, the data preprocessing steps are specifically: denoising and standardization; The data analysis module (200) performs fusion analysis on the tool status data within a preset time period, and obtains the tool cutting parameters within the next same interval time period, specifically including the following steps: S1: Within a preset time period, the temperature vector and wear vector of each current point are collected at even intervals to form a set of temperature vectors and a set of wear vectors within the preset time period; S2: Obtain the variance fluctuation value and average temperature value of a set of temperature vector data; S3: When both the variance fluctuation value and the average temperature value meet the standards, the current cutting parameters are determined to be qualified for one round and the parameters are usable, and the process proceeds to step S4; when either the variance fluctuation value or the average temperature value does not meet the standards, the current cutting parameters are determined to be unqualified for one round and the parameters are unusable, and it is determined that there is a risk in cutting under the current parameters, and the control is stopped; S4: obtaining a set of characteristic offset values ​​of wear vector set data; S5: When the characteristic offset value meets the standard, it is determined that the current cutting parameters are qualified for the second round, and the parameters can be used until the next interval time period; when the characteristic offset value does not meet the standard, it is determined that the current cutting parameters are unqualified for the second round, and the current tool cutting parameters are adjusted according to the characteristic offset value.

2. The universal control system for realizing CNC machining control of cold stamping dies according to claim 1, characterized in that: In step S5, the current tool cutting parameters are adjusted according to the characteristic offset value. Specifically, when the cutting fluid injection parameters remain unchanged, only the tool cutting force is adjusted.

3. The universal control system for realizing CNC machining control of cold stamping dies according to claim 2, characterized in that: The tool cutting forces are adjusted according to the following model: ， Among them, ε2 is the tool cutting force after adjustment, in N; ε1 is the tool cutting force before adjustment, in N; η is the characteristic offset value.

4. The universal control system for realizing CNC machining of cold stamping dies according to claim 3, characterized in that: Within the preset time period, the temperature vector and wear vector of each current point are collected 15 times at even intervals.

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