High-precision lithium battery slitting cutter machining system

Abstract

The invention belongs to the technical field of tool forging, particularly relates to a high-precision lithium battery slitting tool machining system, and solves the problems of inconsistent grain size and non-uniform internal stress distribution caused by improper cooling rate in a traditional machining method by introducing a mechanism for dynamically adjusting the cooling rate. Therefore, the cutting precision of the cutter is remarkably improved, the conditions of rough edges and uneven notches are reduced, the service life of the cutter is prolonged, and the risk of forming cracks in materials is reduced. Besides, by means of the optimized cooling process, production efficiency is improved, production interruption and reworking conditions are reduced, energy consumption and resource consumption are reduced, and unnecessary energy waste and raw material loss are avoided. The system flexibly adapts to different material characteristics and processing requirements and is suitable for production of lithium battery slitting cutters of various specifications and types, the efficiency, stability and environmental protection performance of the manufacturing process are integrally improved, and an efficient, reliable and economical solution is provided for the lithium battery manufacturing industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of tool forging, and particularly relates to a high-precision processing system for lithium battery slitting tools. Background Art

[0002] During the manufacturing process of lithium batteries, the quality of slitting tools directly affects the cutting accuracy and production efficiency of battery electrodes. In the prior art, high-precision lithium battery slitting tools are usually processed using alloy steel as the base material and through a series of heat treatment processes to enhance their hardness and wear resistance. However, in the traditional process flow, especially in the forging and cooling stages, due to the lack of effective control of the cooling rate, problems such as inconsistent grain size and uneven internal stress distribution often occur. These problems not only affect the final cutting accuracy of the tool but also shorten the service life of the tool.

[0003] Specifically, the traditional processing method fails to precisely control the cooling process of the material after forging, resulting in too fast or too slow cooling rates, thus causing the above problems. For example, in some solutions, only fixed cooling parameters are relied on, without dynamically adjusting the cooling rate according to real-time temperature feedback, resulting in significant fluctuations in the quality of tools in different batches or even within the same batch. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-precision processing system for lithium battery slitting tools, which not only solves the problems of inconsistent grain size and uneven internal stress distribution caused by improper cooling rates but also significantly improves the cutting accuracy and service life of the tools.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A high-precision processing system for lithium battery slitting tools, comprising:

[0006] A feeding module for providing alloy steel material; a heating module for heating the material provided by the feeding module; a forging module for receiving the material from the heating module and performing isothermal forging; a cooling rate control module for adjusting the cooling speed according to the working state of the forging module to ensure uniform cooling; a temperature monitoring module for detecting and feeding back temperature data to the cooling rate control module in real time; a stress analysis module for evaluating the internal stress distribution according to the data of the temperature monitoring module; a grain optimization module for adjusting process parameters using the results of the stress analysis module to obtain a consistent grain size; a cutting module for cutting based on the optimal parameters determined by the grain optimization module; an inspection module for verifying whether the cutting accuracy and service life of the tools produced by the cutting module meet the standards.

[0007] Preferably, the feeding module includes:

[0008] A raw material preparation sub-module for selecting alloy steel material;

[0009] A size adjustment sub-module adjusts the material to a predetermined size by calculating L = (W * H) / D according to the material size provided by the raw material preparation sub-module, where L is the length, W is the width, H is the height, and D is the density;

[0010] A weight calibration sub-module measures the weight of the material processed by the size adjustment sub-module using the formula M = V * P, where M represents mass, V represents volume, and P is the density;

[0011] A position fixing sub-module stably places the material at the starting position of processing according to the result of the weight calibration sub-module.

[0012] Preferably, the heating module includes:

[0013] A temperature setting sub-module determines the target heating temperature T based on the material stably placed by the position fixing sub-module according to the material type, and calculates it using the formula T = (Cm * Mc) + B, where Cm is the specific heat capacity of the material, Mc is the mass of the material, and B is the basic heating value;

[0014] A heating execution sub-module heats up the material provided by the feeding module according to the determined target heating temperature T;

[0015] A temperature monitoring sub-module continuously monitors the temperature of the material in the heating execution sub-module, calculates the difference Dt = T - Ta by comparing the actual temperature Ta with the target temperature T, where Ta is the actually measured temperature, and adjusts the heating rate to maintain a constant heating rate;

[0016] A status confirmation sub-module verifies whether the material is uniformly heated to the target temperature based on the data of the temperature monitoring sub-module.

[0017] Preferably, the forging module includes:

[0018] A material introduction sub-module receives the material that has been uniformly heated to the target temperature by the status confirmation sub-module and introduces it into the forging position;

[0019] A pressure application sub-module calculates the required pressure Pr based on the material introduced by the material introduction sub-module according to the material size and the target density, and uses the formula Pr = (Dr * Vr) / Ar, where Dr is the material density, Vr is the volume, and Ar is the pressure-receiving area;

[0020] A temperature maintenance sub-module works synchronously with the pressure application sub-module to ensure that the material temperature remains constant during the forging process. It corrects by monitoring and adjusting the heating amount Ht according to the difference Tm = T - Ta, where T is the target temperature and Ta is the actually measured temperature;

[0021] The structure optimization sub-module adjusts the forging parameters to optimize the internal structure of the material according to the temperature data provided by the temperature maintenance sub-module and the pressure application of the pressure application sub-module.

[0022] Preferably, the cooling rate control module includes:

[0023] The initial temperature detection sub-module is used to receive the material processed by the structure optimization sub-module and measure its initial temperature Ti.

[0024] The cooling parameter setting sub-module calculates the ideal cooling rate Rc based on the temperature Ti measured by the initial temperature detection sub-module, using the formula Rc = (Ti - Tf) / Td, where Tf is the target final temperature and Td is the desired temperature reduction time.

[0025] The temperature regulation sub-module adjusts the flow rate Vs and temperature Ta of the cooling medium according to the determined ideal cooling rate Rc, and matches the optimal cooling conditions through the formula Vs = K * Rc, while monitoring and maintaining Ta within the set range, where K is a proportionality constant.

[0026] The temperature verification sub-module measures the material temperature Tf again based on the operation result of the temperature regulation sub-module to confirm whether the expected temperature reduction target is achieved.

[0027] Preferably, the temperature monitoring module includes:

[0028] The temperature acquisition sub-module is used to collect the temperature data Tc of the material in real time during the cooling process and compare it with the material temperature Tf confirmed by the temperature verification sub-module.

[0029] The data processing sub-module calculates the actual temperature reduction rate Ra based on the acquired temperature data Tc, using the formula Ra = (Tf - Tc) / t, where t is the time interval, to evaluate the current cooling effect.

[0030] The feedback adjustment sub-module adjusts the cooling parameters according to the calculated actual temperature reduction rate Ra; if Ra deviates from the ideal cooling rate Rc, it is corrected by changing the flow rate Vs of the cooling medium, according to the formula Vs = Vs0 + K * (Rc - Ra), where Vs0 is the initial flow rate and K is the adjustment coefficient.

[0031] The status synchronization sub-module synchronizes the result of the feedback adjustment sub-module to the cooling rate control module to ensure that the cooling rate control module can perform optimization adjustment according to the latest temperature data Tc and the adjusted flow rate Vs of the cooling medium.

[0032] Preferably, the stress analysis module includes:

[0033] The stress parameter calculation sub-module calculates the preliminary stress value S0 based on the latest temperature data Tc provided by the state synchronization sub-module and in combination with the material property coefficient A, using the formula S0 = A * (Tf - Tc), where Tf is the final target temperature;

[0034] The internal stress evaluation sub-module evaluates the internal stress distribution according to the preliminary stress value S0; by introducing the position factor Lp, the actual stress value S at each point is calculated using the formula S = S0 + Lp * Bs, where Bs is the adjustment constant;

[0035] The stress distribution mapping sub-module generates the internal stress distribution map of the material using the calculated actual stress value S;

[0036] The adjustment suggestion sub-module proposes adjustment suggestions for the cooling rate or temperature control based on the stress distribution map and calculates the adjustment coefficient Cc, using the formula Cc = (Smax - Smin) / Vm, where Smax and Smin are the maximum and minimum stress values respectively, and Vm is the material volume.

[0037] Preferably, the grain optimization module includes:

[0038] The grain size calculation sub-module calculates the ideal grain size G based on the stress distribution data provided by the adjustment suggestion sub-module and in combination with the basic grain size G0 of the material, using the formula G = G0 + Kg * (Smax - Smin), where Kg is the proportionality coefficient;

[0039] The process parameter adjustment sub-module adjusts the process parameters according to the ideal grain size G; the grain growth conditions are optimized by changing the cooling rate Rc or the temperature T, using the formula Rc = Rc0 - Lr * (G - G0), where Rc0 is the initial cooling rate and Lr is the adjustment factor;

[0040] The grain uniformity evaluation sub-module evaluates the grain uniformity using the parameters adjusted by the process parameter adjustment sub-module; the actual grain size is compared with the ideal grain size G, and the consistency of the grains is measured by calculating the deviation Dg = |G - Ga| / Ga, where Ga is the average grain size measured actually;

[0041] The optimization feedback sub-module provides feedback to optimize the process parameters based on the results of the grain uniformity evaluation sub-module; if the deviation Dg exceeds the set range, the cooling rate or temperature control strategy is adjusted according to the formula Cf = Dg * Fg, where Fg is the correction coefficient.

[0042] Preferably, the cutting module includes:

[0043] The cutting parameter setting sub-module determines the optimal cutting parameters according to the finally adjusted cooling rate Rc or temperature T provided by the optimization feedback sub-module, and calculates the cutting pressure Pc using the formula Pc = Kpc * G + Bpc, where Kpc is the proportionality coefficient, G is the ideal grain size, and Bpc is the basic cutting pressure;

[0044] The material positioning sub-module accurately positions the material that has undergone grain optimization processing to the cutting position based on the optimal cutting parameters;

[0045] The precision cutting execution sub-module cuts the material with the calculated cutting pressure Pc according to the position data of the material positioning sub-module, and simultaneously monitors the cutting speed Vs. The formula is Vs = Vso - Dr * (Pc - Pno), where Vso is the initial cutting speed, Dr is the adjustment factor, and Pno is the standard cutting pressure;

[0046] The quality inspection sub-module inspects the quality of the cutting finished products completed by the precision cutting execution sub-module; by measuring the cutting edge roughness Ra, using the formula Ra = Rao + Er * (Vs - Vopt), where Rao is the reference roughness, Er is the error amplification coefficient, and Vopt is the optimal cutting speed, to evaluate whether the cutting quality meets the requirements.

[0047] Preferably, the inspection module includes:

[0048] The cutting accuracy detection sub-module is used to measure the actual cutting accuracy Pa of the tool produced by the precision cutting execution sub-module. Based on the cutting path design value Pd, the formula is Pa = Pd - Ep * (Vs - Vopt), where Ep is the error coefficient, Vs is the actual cutting speed, and Vopt is the optimal cutting speed;

[0049] The service life prediction sub-module calculates the predicted service life Lt of the tool according to the cutting edge roughness Ra and in combination with the material wear rate Wr, using the formula Lt = Lo / (Ra * Wr), where Lo is the basic service life;

[0050] The performance verification sub-module verifies the overall performance of the tool using the actual cutting accuracy Pa and the predicted service life Lt; by comparing Pa with the preset accuracy standard Ps and Lt with the expected service life Le, using the formula Dv = (Ps - Pa) + (Le - Lt), where Dv is the performance deviation value, to determine whether the tool meets the standard;

[0051] The data recording sub-module records the performance deviation value Dv to provide a basis for analysis and improvement; at the same time, it feeds back the results to the system to adjust the process parameters.

[0052] Technical effects and advantages of the present invention: A high-precision lithium battery slitting tool processing system proposed by the present invention has the following advantages compared with the prior art:

[0053] By introducing a mechanism for dynamically adjusting the cooling rate, the present invention solves the problems of inconsistent grain size and uneven internal stress distribution caused by improper cooling rate in traditional processing methods, thereby significantly improving the cutting accuracy of the tool, reducing burrs and uneven cuts, extending the service life of the tool, and reducing the risk of internal crack formation in the material. In addition, the optimized cooling process not only improves production efficiency, reduces production interruptions and rework, but also reduces energy consumption and resource consumption, avoiding unnecessary energy waste and raw material loss. The system can flexibly adapt to different material characteristics and processing requirements, is applicable to the production of lithium battery slitting tools of various specifications and types, and overall improves the efficiency, stability and environmental protection of the manufacturing process, providing an efficient, reliable and economical solution for the lithium battery manufacturing industry. Brief Description of the Drawings

[0054] Figure 1 It is a module diagram of a high-precision lithium battery slitting tool processing system of the present invention. Detailed Embodiments

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0056] The present invention provides a high-precision lithium battery slitting tool processing system as shown in Figure 1 which includes a feeding module, a heating module, a forging module, a cooling rate control module, a temperature monitoring module, a stress analysis module, a grain optimization module, a cutting module, and an inspection module. By real-time monitoring the temperature and feeding it back to the cooling rate control module to dynamically adjust the cooling speed, uniform cooling is ensured. This not only solves the problems of inconsistent grain size and uneven internal stress distribution caused by improper cooling rate, but also significantly improves the cutting accuracy and service life of the tool, providing a more stable and efficient slitting solution for lithium battery manufacturing.

[0057] Exemplarily, the feeding module is used to provide alloy steel materials; specifically including:

[0058] The raw material preparation sub-module is responsible for selecting suitable alloy steel materials for processing from the inventory. First, the materials are cleaned to remove surface impurities and contaminants, ensuring that no unnecessary defects are introduced during subsequent processing or affecting the quality of the final product.

[0059] The size adjustment sub-module adjusts the material to a predetermined size according to the size of the material provided by the raw material preparation sub-module by calculating L = (W * H) / D, where L is the length, W is the width, H is the height, and D is the density; this formula is used to estimate the target length to be adjusted based on the basic geometric parameters (width and height) of the material and its density. It helps determine the ideal length to be achieved while keeping other dimensions unchanged to meet specific processing requirements.

[0060] The weight calibration sub-module measures the weight of the material processed by the size adjustment sub-module using the formula M = V * P, where M represents mass, V represents volume, and P is density; this formula is based on the basic mass calculation principle in physics, that is, mass equals volume multiplied by density. It is used to accurately measure the actual mass of each piece of material to facilitate controlling the consistency of processing conditions.

[0061] The position fixing sub-module firmly places the material at the starting position of processing according to the result of the weight calibration sub-module. This step ensures the stability of the material in subsequent processes, preventing displacement during handling or preliminary heating and affecting processing accuracy.

[0062] Example 1

[0063] Suppose a piece of alloy steel material needs to be processed, with an initial width W of 0.5 meters, a height H of 0.3 meters, and a density D of 7850 kg / m³.

[0064] It is desired to adjust it to a specific length L. According to the formula L = (W * H) / D, it is calculated that L = (0.5 * 0.3) / 7850 = 1.91e-5 cubic meters (note the unit conversion).

[0065] Next, measure the volume V of this material, assumed to be 0.045 cubic meters, and calculate its mass M = 0.045 * 7850 = 353.25 kg using the formula M = V * P. Finally, firmly place this piece of material at the starting position of processing and prepare to enter the next step - heat treatment.

[0066] Exemplarily, the heating module is used to perform a temperature-raising treatment on the material provided by the feeding module; specifically including:

[0067] Temperature setting sub-module, based on the material stably placed by the position fixing sub-module, determines the target heating temperature T according to the material type, and calculates it using the formula T = (Cm * Mc) + B, where Cm is the specific heat capacity of the material, Mc is the mass of the material, and B is the basic heating value; this formula is used to determine the target temperature to be reached according to the physical properties (specific heat capacity and mass) of the material and the preset basic heating value. The specific heat capacity represents the heat required for a unit mass of a substance to increase or decrease by one degree, and through this parameter, the energy required for heating can be estimated.

[0068] Heating execution sub-module, according to the determined target heating temperature T, performs a heating process on the material provided by the feeding module; this step involves heating the material to the pre-calculated target temperature to ensure that all material blocks are evenly heated.

[0069] Temperature monitoring sub-module, monitors the temperature of the material in the heating execution sub-module in real time, calculates the difference Dt between the actual temperature Ta and the target temperature T by comparing them, Dt = T - Ta, where Ta is the actual measured temperature, and adjusts the heating rate to maintain a constant temperature rise; the difference Dt reflects the gap between the current material temperature and the target temperature, and by continuously adjusting the heating rate, this gap is narrowed until they are equal. This method ensures the stability and consistency of the heating process.

[0070] Status confirmation sub-module, based on the data of the temperature monitoring sub-module, verifies whether the material is evenly heated to the target temperature. By verifying whether the material is evenly heated to the target temperature, quality problems caused by local temperature differences can be reduced, and the success rate of subsequent forging processes and product quality can be guaranteed.

[0071] Embodiment 2

[0072] Suppose there is a piece of alloy steel material with a mass Mc of 353.25 kg (from the previous example), a specific heat capacity Cm of 480 J / (kg·°C), and a basic heating value B of 800 °C. According to the formula T = (Cm * Mc) + B, the target heating temperature T = (480 * 353.25) + 800 = 170,640 J / kg + 800 °C = 800 °C (Note: Here the calculation is simplified. In fact, multiplying the specific heat capacity by the mass gives the total heat requirement rather than directly adding it to the temperature). For the sake of simplicity in explanation, assume that the material needs to be heated to 800 °C as the target temperature.

[0073] During the heating process, if the actual measured temperature Ta at a certain moment is 750 degrees Celsius, then the difference Dt = T - Ta is calculated according to the formula Dt = 800 - 750 = 50 degrees Celsius. This means that heating needs to continue until the temperature reaches the target value of 800 degrees Celsius. As the heating process progresses, the system will continuously adjust the heating rate to ensure uniform heating of the material until the entire material block reaches the target temperature.

[0074] Exemplarily, the forging module receives the material from the heating module and performs isothermal forging; specifically including:

[0075] The material import sub-module receives the material that has been uniformly heated to the target temperature from the status confirmation sub-module and imports it to the forging position; ensuring that each piece of material reaches the ideal preheating temperature when entering the forging process to obtain the best plastic deformation conditions.

[0076] The pressure application sub-module, based on the material imported by the material import sub-module, calculates the required pressure Pr according to the material size and the target density, using the formula Pr = (Dr * Vr) / Ar, where Dr is the material density, Vr is the volume, and Ar is the compressed area; this formula is used to calculate the pressure applied to the material, based on the mass of the material (calculated by density and volume) and the compressed area. It helps to determine the minimum pressure value required to achieve a specific deformation effect, thereby optimizing the energy consumption and equipment load during forging.

[0077] The temperature maintenance sub-module works synchronously with the pressure application sub-module to ensure that the material temperature remains constant during forging by monitoring and adjusting the heating amount Ht, corrected according to the difference Tm = T - Ta, where T is the target temperature and Ta is the actual measured temperature; the difference Tm reflects the difference between the current material temperature and the target temperature. By monitoring and adjusting the heating amount in real time, temperature fluctuations can be dynamically compensated to ensure that the material is always within the ideal working temperature range.

[0078] The structure optimization sub-module adjusts the forging parameters to optimize the internal structure of the material according to the temperature data provided by the temperature maintenance sub-module and the pressure application situation of the pressure application sub-module. By finely controlling the forging parameters, the internal grain structure of the material can be significantly improved, residual stress can be reduced, the overall strength and toughness of the material can be increased, and the tool service life can be extended.

[0079] Example Three

[0080] Suppose there is an alloy steel material with a density Dr of 7,850 kg / m³, a volume Vr of 0.045 m³, and a compression area Ar of 0.01 m². According to the formula Pr = (Dr * Vr) / Ar, the required pressure Pr = (7,850 * 0.045) / 0.01 = 35,325 Pascals (i.e., 35.325 MPa). This means that in order to achieve the ideal forging effect, a pressure of approximately 35.325 MPa needs to be applied to the material.

[0081] During the forging process, if the actual measured temperature Ta at a certain moment is 790 degrees Celsius and the target temperature T is set at 800 degrees Celsius, then the temperature difference Tm = T - Ta is calculated as Tm = 800 - 790 = 10 degrees Celsius according to the formula. This indicates that additional heating is required to make up for the 10-degree temperature difference and bring the material temperature back up to the target value of 800 degrees Celsius.

[0082] At the same time, the structure optimization sub-module adjusts the forging parameters based on the real-time feedback data (such as the actual application conditions of temperature Tm and pressure Pr), for example, appropriately extending the pressure holding time or finely tuning the pressure magnitude, to ensure that the internal structure of the material reaches the optimal state.

[0083] Exemplarily, the cooling rate control module adjusts the cooling rate according to the working state of the forging module to ensure uniform cooling; specifically including:

[0084] The initial temperature detection sub-module is used to receive the material processed by the structure optimization sub-module and measure its initial temperature Ti; accurate measurement of the initial temperature provides the basic data for subsequent cooling parameter setting, ensuring that the cooling process is adjusted based on the actual conditions from the very beginning and avoiding improper cooling rates caused by inaccurate initial temperature estimation.

[0085] The cooling parameter setting sub-module calculates the ideal cooling rate Rc based on the temperature Ti measured by the initial temperature detection sub-module, using the formula Rc = (Ti - Tf) / Td, where Tf is the target final temperature and Td is the desired cooling time; by calculating the difference between the initial temperature and the target temperature and dividing it by the desired cooling time, the temperature value that should be reduced per unit time is obtained. This helps to formulate a reasonable cooling rate that neither increases the internal stress of the material too quickly nor affects the production efficiency too slowly.

[0086] The temperature regulation sub-module adjusts the flow rate Vs and temperature Ta of the cooling medium according to the determined ideal cooling rate Rc, using the formula Vs = K * Rc to match the optimal cooling conditions, while monitoring and maintaining Ta within the set range, where K is a proportionality constant. This method can flexibly adjust the supply speed of the cooling medium according to the specific cooling requirements of the material to ensure that the material cools uniformly at the predetermined cooling rate.

[0087] The temperature verification submodule measures the material temperature Tf again based on the operating results of the temperature control submodule to confirm whether the expected cooling target is achieved. Through the final temperature verification, any substandard cooling conditions can be discovered and corrected in a timely manner, ensuring that each batch of materials has the same physical properties, improving the yield rate and product quality.

[0088] Embodiment 4

[0089] Assume that there is a piece of alloy steel material, whose initial temperature Ti is 800 degrees Celsius, the target final temperature Tf is set to 200 degrees Celsius, and the expected cooling time is 3600 seconds (1 hour). According to the formula Rc = (Ti-Tf) / Td, the ideal cooling rate can be calculated to be Rc = (800-200) / 3600 = 0.167 degrees Celsius / second.

[0090] In order to achieve this cooling rate, the flow rate Vs of the cooling medium needs to be adjusted. Assuming the proportional constant K is 50, according to the formula Vs = K*Rc, it is calculated that Vs = 50*0.167 = 8.35 liters / minute. This means that the flow rate of the cooling medium needs to be set to about 8.35 liters / minute to ensure that the material can be cooled evenly at the predetermined rate.

[0091] During the entire cooling process, the temperature control submodule will continuously monitor the actual temperature Ta of the cooling medium and make fine adjustments as needed to keep it within the set range. For example, if the actual temperature Ta deviates from the target temperature trajectory at a certain moment, the system will automatically adjust the flow rate or temperature of the cooling medium to compensate for the deviation.

[0092] Finally, when the cooling process is nearing the end, the temperature verification submodule will measure the final temperature Tf of the material again to ensure that it has reached the expected 200 degrees Celsius. If the measurement results show that the material temperature is in line with expectations, it means that the cooling process has been successfully completed and the material has the ideal microstructure and mechanical properties.

[0093] Exemplarily, the temperature monitoring module is used to detect in real time and feed back temperature data to the cooling rate control module; specifically includes:

[0094] The temperature acquisition submodule is used to collect the temperature data Tc of the material during the cooling process in real time, and compare it with the material temperature Tf confirmed by the temperature verification submodule; real-time collection of temperature data enables the system to dynamically monitor the cooling process, promptly detect and correct any deviation from the expected cooling trajectory, and ensure the consistency and stability of the cooling process.

[0095] The data processing sub-module calculates the actual cooling rate Ra based on the acquired temperature data Tc using the formula Ra = (Tf - Tc) / t, where t is the time interval, to evaluate the current cooling effect. By calculating the difference between the target final temperature and the current temperature and dividing it by the time interval, the actual rate of temperature decrease per unit time is obtained. This method helps the system understand whether the current cooling process meets expectations and make corresponding adjustments accordingly.

[0096] The feedback adjustment sub-module adjusts the cooling parameters according to the calculated actual cooling rate Ra. If Ra deviates from the ideal cooling rate Rc, it is corrected by changing the flow rate Vs of the cooling medium according to the formula Vs = Vs0 + K*(Rc - Ra), where Vs0 is the initial flow rate and K is the adjustment coefficient. By multiplying the difference between the ideal cooling rate and the actual cooling rate by an adjustment coefficient K and adding the initial flow rate Vs0, the new flow rate of the cooling medium is calculated. This method can flexibly adjust the supply rate of the cooling medium according to the actual cooling situation to ensure that the material cools evenly at the predetermined cooling rate.

[0097] The status synchronization sub-module synchronizes the results of the feedback adjustment sub-module to the cooling rate control module to ensure that the cooling rate control module can perform optimized adjustment based on the latest temperature data Tc and the adjusted flow rate Vs of the cooling medium. This step ensures the information flow between modules and improves the response speed and control accuracy of the system.

[0098] Example Five

[0099] Suppose there is a piece of alloy steel material with the target final temperature Tf set at 200 degrees Celsius, the currently measured temperature Tc at 600 degrees Celsius, and the time interval t at 60 seconds. According to the formula Ra = (Tf - Tc) / t, the actual cooling rate can be calculated as Ra = (200 - 600) / 60 = -6.67 degrees Celsius per second.

[0100] If the ideal cooling rate Rc is set at -5 degrees Celsius per second, then the flow rate Vs of the cooling medium needs to be adjusted. Assuming the initial flow rate Vs0 is 8 liters per minute and the adjustment coefficient K is 1.5, then according to the formula Vs = Vs0 + K*(Rc - Ra), it is calculated that Vs = 8 + 1.5*(-5 - (-6.67)) = 8 + 1.5*1.67 = 10.505 liters per minute. This means that the flow rate of the cooling medium needs to be increased to approximately 10.5 liters per minute to accelerate the cooling speed and make it closer to the ideal cooling rate.

[0101] During the entire cooling process, the temperature acquisition sub-module continuously collects the temperature data Tc of the material and transmits it to the data processing sub-module for calculating the actual cooling rate. The feedback adjustment sub-module then dynamically adjusts the flow rate Vs of the cooling medium based on this data to ensure that the material cools uniformly at a predetermined cooling rate. The status synchronization sub-module synchronizes the latest temperature data Tc and the adjusted flow rate Vs of the cooling medium to the cooling rate control module for further optimization and adjustment.

[0102] Exemplarily, the stress analysis module is used to evaluate the internal stress distribution according to the data of the temperature monitoring module; specifically including:

[0103] The stress parameter calculation sub-module calculates the preliminary stress value S0 based on the latest temperature data Tc provided by the status synchronization sub-module and combines it with the material property coefficient A, using the formula S0 = A * (Tf - Tc), where Tf is the final target temperature; this formula utilizes the basic physical principle of thermal expansion and contraction, that is, temperature change will cause stress in the material. The material property coefficient A reflects the stress response characteristics of the material within a specific temperature range, and the temperature difference (Tf - Tc) represents the gap between the current temperature and the target temperature of the material.

[0104] The internal stress evaluation sub-module evaluates the internal stress distribution according to the preliminary stress value S0; by introducing the position factor Lp, the actual stress value S at each point is calculated using the formula S = S0 + Lp * Bs, where Bs is the adjustment constant; the influence of the position factor Lp is added on the basis of the preliminary stress value, considering the stress concentration phenomenon caused by factors such as geometric shape and boundary conditions at different positions. The adjustment constant Bs is used to correct these local stress effects.

[0105] The stress distribution mapping sub-module generates the internal stress distribution map of the material using the calculated actual stress value S; this step converts complex numerical information into an intuitive graphical representation, facilitating engineers to understand and analyze the internal stress condition of the material.

[0106] The adjustment suggestion sub-module proposes adjustment suggestions for the cooling rate or temperature control based on the stress distribution map and calculates the adjustment coefficient Cc, using the formula Cc = (Smax - Smin) / Vm, where Smax and Smin are the maximum and minimum stress values respectively, and Vm is the volume of the material. By calculating the ratio of the difference between the maximum and minimum stress values to the volume of the material, the uniformity of the stress distribution is measured. A larger Cc value indicates uneven stress distribution and adjustment is required to reduce the internal stress difference.

[0107] Example Six

[0108] Suppose there is an alloy steel material with a material property coefficient A of 0.01 Pascal per degree Celsius. The current measured temperature Tc is 400 degrees Celsius, and the final target temperature Tf is set at 200 degrees Celsius. According to the formula S0 = A * (Tf - Tc), the preliminary stress value S0 = 0.01 * (200 - 400) = -2 Pascal can be calculated.

[0109] Next, to evaluate the internal stress distribution, assume the position factor Lp is 0.5 and the adjustment constant Bs is 10 Pascal. Then, according to the formula S = S0 + Lp * Bs, the actual stress value S = -2 + 0.5 * 10 = 3 Pascal is calculated. This means that at certain positions, the actual stress value inside the material is 3 Pascal.

[0110] Subsequently, the stress distribution mapping sub-module generates a map of the internal stress distribution of the material, showing the actual stress values at each point. Assume the detected maximum stress value Smax is 10 Pascal, the minimum stress value Smin is -5 Pascal, and the material volume Vm is 0.045 cubic meters. According to the formula Cc = (Smax - Smin) / Vm, the adjustment coefficient Cc = (10 - (-5)) / 0.045 = 333.33 Pascal per cubic meter is calculated.

[0111] If the Cc value is high, indicating that the stress distribution is not uniform enough, the system will recommend adjusting the cooling rate or temperature control strategy. For example, the temperature gradient can be reduced by increasing the flow rate of the cooling medium or adjusting the heating amount, thereby optimizing the stress distribution.

[0112] Exemplarily, the grain optimization module uses the results of the stress analysis module to adjust process parameters to obtain a consistent grain size; specifically including:

[0113] The grain size calculation sub-module, based on the stress distribution data provided by the adjustment recommendation sub-module and combined with the base grain size G0 of the material, calculates the ideal grain size G using the formula G = G0 + Kg * (Smax - Smin), where Kg is the proportionality coefficient; it utilizes the influence of stress on grain growth. A large stress difference will lead to different grain growth rates, so the base grain size G0 needs to be adjusted according to the stress difference. The proportionality coefficient Kg is used to quantify this influence.

[0114] The process parameter adjustment sub-module adjusts the process parameters according to the ideal grain size G; it optimizes the grain growth conditions by changing the cooling rate Rc or the temperature T, using the formula Rc = Rc0 - Lr * (G - G0), where Rc0 is the initial cooling rate and Lr is the adjustment factor; by comparing the difference between the ideal grain size G and the base grain size G0, the cooling rate Rc is adjusted. The adjustment factor Lr is used to quantify the amplitude of this adjustment to ensure a moderate grain growth rate and avoid too large or too small grain sizes.

[0115] The grain uniformity evaluation sub-module uses the parameters adjusted by the process parameter adjustment sub-module to evaluate the grain uniformity; compares the actual grain size with the ideal grain size G, and measures the grain consistency by calculating the deviation Dg = |G - Ga| / Ga, where Ga is the average grain size measured actually; quantifies the grain size consistency by calculating the relative deviation between the ideal grain size G and the actual average grain size Ga. A smaller Dg value indicates more uniform grain size.

[0116] The optimization feedback sub-module provides feedback to optimize the process parameters based on the results of the grain uniformity evaluation sub-module; if the deviation Dg exceeds the set range, adjusts the cooling rate or temperature control strategy according to the formula Cf = Dg * Fg, where Fg is the correction factor. Determines the degree of adjustment required for the cooling rate or temperature control strategy by calculating the product of the deviation Dg and the correction factor Fg. The correction factor Fg is used to amplify or reduce the adjustment amplitude to ensure that the system can respond quickly and correct the deviation.

[0117] Example Seven

[0118] Suppose there is a piece of alloy steel material with a base grain size G0 of 50 microns, a maximum stress value Smax of 10 Pascals, a minimum stress value Smin of -5 Pascals, and a proportionality coefficient Kg of 0.02. According to the formula G = G0 + Kg * (Smax - Smin), the ideal grain size G = 50 + 0.02 * (10 - (-5)) = 50 + 0.02 * 15 = 50.3 microns can be calculated.

[0119] Next, to optimize the grain growth conditions, assume the initial cooling rate Rc0 is 0.1 degrees Celsius per second and the adjustment factor Lr is 0.05. According to the formula Rc = Rc0 - Lr * (G - G0), the new cooling rate Rc = 0.1 - 0.05 * (50.3 - 50) = 0.1 - 0.05 * 0.3 = 0.085 degrees Celsius per second is calculated. This means that the cooling rate needs to be reduced to 0.085 degrees Celsius per second to promote ideal grain growth.

[0120] Subsequently, the actual average grain size Ga detected by the grain uniformity evaluation sub-module is 50.5 microns. According to the formula Dg = |G - Ga| / Ga, the deviation Dg = |50.3 - 50.5| / 50.5 = 0.004 is calculated. If the maximum allowable deviation within the set range is 0.005, the current deviation is within the acceptable range. But if the deviation exceeds the set range, for example, Dg is 0.01, then according to the formula Cf = Dg * Fg, assuming the correction factor Fg is 10, the adjustment coefficient Cf = 0.01 * 10 = 0.1 is calculated. This means that the cooling rate or temperature control strategy needs to be further adjusted to reduce the grain size deviation.

[0121] Exemplarily, the cutting module performs cutting based on the optimal parameters determined by the grain optimization module; specifically, it includes:

[0122] The cutting parameter setting sub-module determines the optimal cutting parameters according to the finally adjusted cooling rate Rc or temperature T provided by the optimization feedback sub-module, and calculates the cutting pressure Pc using the formula Pc = Kpc * G + Bpc, where Kpc is the proportionality coefficient, G is the ideal grain size, and Bpc is the basic cutting pressure; by considering the influence of the grain size on the cutting pressure, the basic cutting pressure Bpc is adjusted. The proportionality coefficient Kpc is used to quantify the influence of the change in grain size on the required cutting pressure, ensuring that an appropriate cutting force can be applied under different grain sizes.

[0123] The material positioning sub-module accurately positions the material after grain optimization processing to the cutting position based on the optimal cutting parameters; this step ensures that the material is in the correct position before cutting, reducing cutting errors caused by position deviation.

[0124] The precision cutting execution sub-module performs material cutting based on the position data of the material positioning sub-module with the calculated cutting pressure Pc, and monitors the cutting speed Vs at the same time. Using the formula Vs = Vso - Dr * (Pc - Pno), where Vso is the initial cutting speed, Dr is the adjustment factor, and Pno is the standard cutting pressure; by comparing the difference between the actual cutting pressure Pc and the standard cutting pressure Pno, the cutting speed Vs is adjusted. The adjustment factor Dr is used to quantify the amplitude of this adjustment, ensuring that the cutting speed is always within the optimal range and avoiding cutting quality problems caused by being too fast or too slow.

[0125] The quality inspection sub-module performs quality inspection on the cutting finished products completed by the precision cutting execution sub-module; by measuring the cutting edge roughness Ra, using the formula Ra = Rao + Er * (Vs - Vopt), where Rao is the reference roughness, Er is the error amplification coefficient, and Vopt is the optimal cutting speed, to evaluate whether the cutting quality meets the requirements. By comparing the difference between the actual cutting speed Vs and the optimal cutting speed Vopt, the roughness Ra of the cutting edge is calculated. The error amplification coefficient Er is used to quantify the influence of the speed deviation on the roughness, helping to identify possible quality problems during the cutting process.

[0126] Example Eight

[0127] Suppose there is a piece of alloy steel material with an ideal grain size G of 50.3 microns, a proportionality coefficient Kpc of 0.1, and a basic cutting pressure Bpc of 200 Newtons. According to the formula Pc = Kpc * G + Bpc, the cutting pressure Pc = 0.1 * 50.3 + 200 = 205.03 Newtons can be calculated.

[0128] Next, for precise cutting, assume the initial cutting speed Vso is 0.5 m / s, the adjustment factor Dr is 0.02, and the standard cutting pressure Pno is 200 N. According to the formula Vs = Vso - Dr*(Pc - Pno), the calculated cutting speed Vs = 0.5 - 0.02*(205.03 - 200) = 0.5 - 0.02*5.03 = 0.49 m / s. This means that the cutting speed needs to be adjusted to 0.49 m / s to adapt to the current cutting pressure.

[0129] Subsequently, the quality inspection sub-module inspects the quality of the cut finished products. Assume the reference roughness Rao is 0.1 μm, the error amplification factor Er is 0.05, and the optimal cutting speed Vopt is 0.5 m / s. According to the formula Ra = Rao + Er*(Vs - Vopt), the calculated cutting edge roughness Ra = 0.1 + 0.05*(0.49 - 0.5) = 0.1 - 0.0005 = 0.0995 μm. If the set maximum allowable roughness is 0.1 μm, then the current roughness is within the acceptable range.

[0130] Exemplarily, the inspection module is used to verify whether the cutting accuracy and service life of the tool produced by the cutting module meet the standards; specifically including:

[0131] The cutting accuracy detection sub-module is used to measure the actual cutting accuracy Pa of the tool produced by the precise cutting execution sub-module. Based on the designed cutting path value Pd, through the formula Pa = Pd - Ep*(Vs - Vopt), where Ep is the error coefficient, Vs is the actual cutting speed, and Vopt is the optimal cutting speed; by considering the difference between the actual cutting speed Vs and the optimal cutting speed Vopt, the designed cutting path value Pd is adjusted. The error coefficient Ep is used to quantify the amplitude of this adjustment to ensure that the actual cutting accuracy is as close as possible to the designed value.

[0132] The service life prediction sub-module calculates the predicted service life Lt of the tool according to the cutting edge roughness Ra, in combination with the material wear rate Wr, using the formula Lt = Lo / (Ra*Wr), where Lo is the basic service life; by dividing the basic service life Lo by the product factor composed of the cutting edge roughness Ra and the material wear rate Wr, the expected service life of the tool is estimated. Higher roughness or wear rate will result in a shorter service life.

[0133] The performance verification sub-module uses the actual cutting accuracy Pa and the expected service life Lt to verify the overall performance of the tool; by comparing Pa with the preset accuracy standard Ps and Lt with the expected service life Le, the formula Dv = (Ps - Pa) + (Le - Lt) is used, where Dv is the performance deviation value, to determine whether the tool meets the standard; by calculating the gap between the actual cutting accuracy and the preset standard, and the gap between the actual service life and the expected service life, the overall performance deviation of the tool is quantified. A smaller Dv value indicates that the tool performance is closer to the ideal state.

[0134] The data recording sub-module records the performance deviation value Dv to provide a basis for analysis and improvement; at the same time, the results are fed back to the system to adjust the process parameters. By recording detailed performance data and feeding it back to the system, dynamic monitoring and optimization of the production process can be achieved, improving the overall production efficiency and product consistency.

[0135] Example Nine

[0136] Suppose there is a tool with a designed cutting path value Pd of 10 mm, an error coefficient Ep of 0.05, an actual cutting speed Vs of 0.49 m / s, and an optimal cutting speed Vopt of 0.5 m / s. According to the formula Pa = Pd - Ep * (Vs - Vopt), the actual cutting accuracy Pa = 10 - 0.05 * (0.49 - 0.5) = 10 + 0.0005 = 10.0005 mm can be calculated.

[0137] Next, to predict the service life of the tool, assume that the cutting edge roughness Ra is 0.0995 microns, the material wear rate Wr is 0.001 mm / year, and the basic service life Lo is 5 years. According to the formula Lt = Lo / (Ra * Wr), the calculated expected service life Lt = 5 / (0.0995 * 0.001) = 50251.26 years.

[0138] Subsequently, the performance verification sub-module verifies the tool. Assume that the preset accuracy standard Ps is 10 mm and the expected service life Le is 50000 years. According to the formula Dv = (Ps - Pa) + (Le - Lt), the performance deviation value is calculated as follows:

[0139] Dv = (10 - 10.0005) + (50000 - 50251.26) = -0.0005 - 251.26 = -251.2605.

[0140] If the set maximum allowable deviation is ±1, the current tool performance deviation exceeds the allowable range, indicating that the process parameters need to be adjusted to improve the cutting accuracy and service life of the tool.

[0141] Through the above steps, not only is a comprehensive evaluation of the cutting accuracy and service life of the tool ensured, but also the stability and consistency of the production process are guaranteed through real-time monitoring and adjustment, thereby improving the quality and production efficiency of the final product.

[0142] In summary, by introducing a mechanism for dynamically adjusting the cooling rate, the present invention solves the problems of inconsistent grain size and uneven internal stress distribution caused by improper cooling rate in traditional processing methods, thereby significantly improving the cutting accuracy of the tool, reducing burrs and uneven cuts, extending the service life of the tool, and reducing the risk of internal crack formation in the material.

[0143] In addition, the optimized cooling process not only improves production efficiency, reduces production interruptions and rework situations, but also reduces energy consumption and resource consumption, avoiding unnecessary energy waste and raw material losses. The system can flexibly adapt to different material characteristics and processing requirements, and is applicable to the production of lithium battery slitting tools of various specifications and types, overall improving the efficiency, stability and environmental friendliness of the manufacturing process, and providing an efficient, reliable and economical solution for the lithium battery manufacturing industry.

[0144] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A high-precision lithium battery strip cutting tool processing system, characterized in that, include: A feeding module, used for providing alloy steel materials; A heating module is used to heat the material provided by the feeding module; a forging module, receiving the material from the heating module and performing isothermal forging; A cooling rate control module adjusts the cooling rate according to the working state of the forging module to ensure uniform cooling; A temperature monitoring module detects temperature in real time and feeds back temperature data to the cooling rate control module; A stress analysis module, which evaluates internal stress distribution based on data from the temperature monitoring module; a grain optimization module, which uses the results of the stress analysis module to adjust process parameters to obtain a consistent grain size; A cutting module, which performs cutting based on the optimal parameters determined by the grain optimization module; The inspection module verifies whether the cutting accuracy and service life of the cutting tool produced by the cutting module meet the standards.

2. The high-precision lithium battery strip cutting tool processing system according to claim 1, characterized in that: The feeding module comprises: Raw material preparation submodule, used to select alloy steel materials; A size adjustment submodule, according to the size of the material provided by the raw material preparation submodule, adjusts the material to a predetermined size by calculating L=(W*H) / D, where L is the length, W is the width, H is the height, and D is the density; A weight calibration submodule, which measures the weight of the material processed by the size adjustment submodule using the formula M=V*P, where M represents mass, V represents volume, and P represents density; The position fixing submodule places the material firmly at the processing starting position according to the result of the weight calibration submodule.

3. The high-precision lithium battery slitting tool processing system according to claim 2, wherein: The heating module comprises: The temperature setting submodule determines the target heating temperature T according to the material type based on the material firmly placed by the position fixing submodule, and calculates it using the formula T=(Cm*Mc)+B, where Cm is the material specific heat capacity, Mc is the material mass, and B is the basic heating value; The heating execution submodule heats up the material provided by the feeding module according to the determined target heating temperature T; The temperature monitoring submodule monitors the material temperature in the heating execution submodule in real time, calculates Dt=T-Ta by comparing the difference Dt between the actual temperature Ta and the target temperature T, where Ta is the actual measured temperature, and adjusts the heating rate to maintain a constant temperature rise; The state confirmation submodule verifies whether the material is evenly heated to the target temperature based on the data of the temperature monitoring submodule.

4. The high-precision lithium battery strip cutting tool processing system according to claim 3, wherein: The forging module comprises: a material introduction submodule, receiving the material that has been uniformly heated to the target temperature from the state confirmation submodule, and introducing the material into the forging position; The pressure applying submodule calculates the required pressure Pr based on the material imported by the material importing submodule according to the material size and target density, using the formula Pr = (Dr*Vr) / Ar, where Dr is the material density, Vr is the volume, and Ar is the pressure area; The temperature maintenance submodule works synchronously with the pressure application submodule to ensure that the material temperature remains constant during the forging process by monitoring and adjusting the heating amount Ht and correcting it according to the difference Tm=T-Ta, where T is the target temperature and Ta is the actual measured temperature; The structure optimization sub-module adjusts the forging parameters to optimize the internal structure of the material according to the temperature data provided by the temperature maintenance sub-module and the pressure application conditions of the pressure application sub-module.

5. The high-precision lithium battery slitting tool processing system according to claim 4, characterized in that: The cooling rate control module includes: The initial temperature detection sub-module is used to receive the material processed by the structure optimization sub-module and measure its initial temperature Ti. The cooling parameter setting sub-module calculates the ideal cooling rate Rc based on the temperature Ti measured by the initial temperature detection sub-module, using the formula Rc = (Ti - Tf) / Td, where Tf is the target final temperature and Td is the desired cooling time. The temperature regulation sub-module adjusts the flow rate Vs and temperature Ta of the cooling medium according to the determined ideal cooling rate Rc, and matches the optimal cooling conditions through the formula Vs = K * Rc, while monitoring and maintaining Ta within the set range, where K is a proportionality constant. The temperature verification sub-module measures the material temperature Tf again based on the operation results of the temperature regulation sub-module to confirm whether the expected cooling target is achieved.

6. A high-precision lithium battery slitting tool processing system according to claim 5, characterized in that: The temperature monitoring module includes: The temperature acquisition sub-module is used to collect the temperature data Tc of the material during the cooling process in real time and compare it with the material temperature Tf confirmed by the temperature verification sub-module. The data processing sub-module calculates the actual cooling rate Ra based on the acquired temperature data Tc, using the formula Ra = (Tf - Tc) / t, where t is the time interval, to evaluate the current cooling effect. The feedback adjustment sub-module adjusts the cooling parameters according to the calculated actual cooling rate Ra; if Ra deviates from the ideal cooling rate Rc, it is corrected by changing the flow rate Vs of the cooling medium, according to the formula Vs = Vs0 + K * (Rc - Ra), where Vs0 is the initial flow rate and K is the adjustment coefficient. The status synchronization sub-module synchronizes the results of the feedback adjustment sub-module to the cooling rate control module to ensure that the cooling rate control module can perform optimized adjustment according to the latest temperature data Tc and the adjusted flow rate Vs of the cooling medium.

7. A high-precision lithium battery slitting tool processing system according to claim 6, characterized in that: The stress analysis module includes: The stress parameter calculation sub-module calculates the preliminary stress value S0 based on the latest temperature data Tc provided by the status synchronization sub-module and in combination with the material property coefficient A, using the formula S0 = A * (Tf - Tc), where Tf is the final target temperature. The internal stress evaluation sub-module evaluates the internal stress distribution according to the preliminary stress value S0; by introducing the position factor Lp, the actual stress value S at each point is calculated using the formula S = S0 + Lp * Bs, where Bs is the adjustment constant. The stress distribution mapping sub-module generates the internal stress distribution map of the material using the calculated actual stress value S. The adjustment suggestion sub-module proposes adjustment suggestions for the cooling rate or temperature control based on the stress distribution map and calculates the adjustment coefficient Cc, using the formula Cc = (Smax - Smin) / Vm, where Smax and Smin are the maximum and minimum stress values respectively, and Vm is the volume of the material.

8. A high-precision lithium battery strip cutting tool processing system according to claim 7, characterized in that: The grain optimization module includes: The grain size calculation sub-module calculates the ideal grain size G based on the stress distribution data provided by the adjustment suggestion sub-module and in combination with the basic grain size G0 of the material, using the formula G = G0 + Kg * (Smax - Smin), where Kg is the proportionality coefficient; The process parameter adjustment sub-module adjusts the process parameters according to the ideal grain size G; optimizes the grain growth conditions by changing the cooling rate Rc or the temperature T, using the formula Rc = Rc0 - Lr * (G - G0), where Rc0 is the initial cooling rate and Lr is the adjustment factor; The grain uniformity evaluation sub-module evaluates the grain uniformity using the parameters adjusted by the process parameter adjustment sub-module; compares the actual grain size with the ideal grain size G, and measures the grain consistency by calculating the deviation Dg = |G - Ga| / Ga, where Ga is the average grain size measured actually; The optimization feedback sub-module provides feedback to optimize the process parameters based on the results of the grain uniformity evaluation sub-module; if the deviation Dg exceeds the set range, adjusts the cooling rate or the temperature control strategy according to the formula Cf = Dg * Fg, where Fg is the correction coefficient.

9. A high-precision lithium battery strip cutting tool processing system according to claim 8, characterized in that: The cutting module includes: The cutting parameter setting sub-module determines the optimal cutting parameters according to the finally adjusted cooling rate Rc or temperature T provided by the optimization feedback sub-module, and calculates the cutting pressure Pc using the formula Pc = Kpc * G + Bpc, where Kpc is the proportionality coefficient, G is the ideal grain size, and Bpc is the basic cutting pressure; The material positioning sub-module accurately positions the material that has undergone grain optimization to the cutting position based on the optimal cutting parameters; The precision cutting execution sub-module cuts the material with the calculated cutting pressure Pc according to the position data of the material positioning sub-module, and monitors the cutting speed Vs at the same time, using the formula Vs = Vso - Dr * (Pc - Pno), where Vso is the initial cutting speed, Dr is the adjustment factor, and Pno is the standard cutting pressure; The quality inspection sub-module inspects the quality of the cutting finished product completed by the precision cutting execution sub-module; evaluates whether the cutting quality meets the requirements by measuring the cutting edge roughness Ra and using the formula Ra = Rao + Er * (Vs - Vopt), where Rao is the reference roughness, Er is the error amplification coefficient, and Vopt is the optimal cutting speed.

10. A high-precision lithium battery strip cutting tool processing system according to claim 9, characterized in that: The inspection module includes: The cutting accuracy detection sub-module is used to measure the actual cutting accuracy Pa of the tool produced by the precision cutting execution sub-module, based on the designed value Pd of the cutting path, using the formula Pa = Pd - Ep * (Vs - Vopt), where Ep is the error coefficient, Vs is the actual cutting speed, and Vopt is the optimal cutting speed; The service life prediction sub-module calculates the expected service life Lt of the tool according to the cutting edge roughness Ra and in combination with the material wear rate Wr, using the formula Lt = Lo / (Ra * Wr), where Lo is the basic service life; The performance verification sub-module uses the actual cutting accuracy Pa and the expected service life Lt to verify the overall performance of the tool; by comparing Pa with the preset accuracy standard Ps and Lt with the expected service life Le, the formula Dv = (Ps - Pa) + (Le - Lt) is adopted, where Dv is the performance deviation value, to determine whether the tool meets the standard; The data recording sub-module records the performance deviation value Dv to provide a basis for analysis and improvement; at the same time, the result is fed back to the system to adjust the process parameters.

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