A machine tool cooling device and a numerical control machine tool system

Abstract

The present application relates to the field of machining, in particular to a machine tool cooling device and a numerical control machine tool system, wherein the machine tool cooling device comprises: a front-end acquisition module for acquiring cutting parameters and characteristic parameters of a workpiece during cutting of a tool on the surface of the workpiece according to preset workpiece cutting path data before actual cutting machining; a heat generation evaluation module for establishing a heat generation prediction model, taking the cutting parameters as main input parameters and the characteristic parameters as auxiliary variables to evaluate the cutting heat parameters of the workpiece at a pre-cutting position; and a cooling regulation module for outputting a cooling scheme before cutting machining of the workpiece based on the evaluation results and a heat transfer coefficient of a cooling liquid. Through front-end parameter acquisition and accurate heat generation prediction, the present application dynamically matches the change of cutting heat, solves the problem of insufficient or excessive cooling caused by fixed cooling parameters, effectively suppresses thermal deformation of the workpiece, reduces quality defects, reduces tool wear, and improves machining qualification rate, precision and production efficiency.

Description

Technical Field

[0001] This invention relates to the field of machining, and more specifically to a machine tool cooling device and a CNC machine tool system. Background Technology

[0002] CNC machine tools are core automated machining equipment that integrates computer numerical control technology, servo drive technology and precision mechanical structure. They precisely control the relative movement of the tool and the workpiece through digital program instructions, thereby achieving automation and high precision in parts machining.

[0003] However, in practical applications, the machining accuracy and production efficiency of CNC machine tools are still limited by the challenge of controlling cutting heat during the cutting process. As an unavoidable byproduct of metal processing, the intensity and diffusion of cutting heat vary significantly with changes in workpiece material, process parameters, and machining conditions. Existing cooling systems generally adopt fixed parameter modes based on experience, failing to fully consider the differences in the thermophysical properties of different workpiece materials. For example, titanium alloys have strong high-temperature thermal stability and are prone to tool sticking during cutting, while aluminum alloys have high thermal conductivity and good plasticity, making them more sensitive to cooling shocks. Existing cooling parameters cannot be specifically adapted to the differentiated heat dissipation requirements of these materials. At the same time, there is a lack of effective parameter adaptation mechanisms for the core differences in the different process types, such as roughing (high heat intensity, requiring rapid heat dissipation) and finishing (relatively gentle heat generation, requiring consideration of cooling accuracy to avoid workpiece deformation). Insufficient cooling leads to heat buildup in the workpiece's cutting area, causing thermal deformation, dimensional inaccuracies, and surface defects such as burns and cracks, significantly reducing product yield. High temperatures also accelerate tool wear and dulling, even causing chipping, drastically shortening tool life, increasing material costs, and disrupting the machining process, thus lowering overall production efficiency. Conversely, excessive cooling results in wasted energy and coolant due to excess flow and pressure.

[0004] Therefore, this invention proposes a machine tool cooling device and a CNC machine tool system that adapts to the differences in thermophysical properties of different workpiece materials and the process requirements of roughing and finishing. It dynamically matches the dynamic change law of cutting heat to solve the problems of insufficient cooling causing workpiece thermal deformation, quality defects and tool wear caused by empirical fixed parameter mode, or excessive cooling causing resource waste. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a machine tool cooling device and a CNC machine tool system, designed to adapt to the differences in heat dissipation of different workpiece materials and the requirements of roughing and finishing processes. By accurately predicting cutting heat and dynamically adjusting cooling parameters, it reduces insufficient cooling, suppresses workpiece thermal deformation, reduces surface defects, extends tool life, saves resources, and efficiently improves part machining accuracy, pass rate, and production efficiency.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A machine tool cooling device, comprising:

[0007] The pre-acquisition module is used to acquire the cutting parameters of the tool cutting the workpiece surface and the characteristic parameters of the workpiece during the actual cutting process, based on the preset workpiece cutting path data.

[0008] The heat generation assessment module is used to establish a heat generation prediction model. It uses cutting parameters as the main input parameters and characteristic parameters as auxiliary variables to assess the cutting thermal parameters at the pre-cutting position of the workpiece and output the assessment results.

[0009] The cooling control module is used to obtain the heat transfer coefficient of the coolant and, in conjunction with the evaluation results, output a cooling plan before the workpiece is machined.

[0010] Furthermore, the cutting parameters include the tool approach posture, tool approach speed, and process type; the tool approach posture, tool approach speed, and process type are obtained through user-preset machining instructions; the characteristic parameters include thermal diffusivity and coefficient of thermal expansion.

[0011] Furthermore, the cutting thermal parameters include the heat flux in the cutting zone, the peak temperature at the cutting point, the heat generated per unit time, the range of the heat-affected zone, and the rate of temperature change over time.

[0012] Furthermore, the cooling solution includes injection flow rate, injection pressure, injection angle, and injection position.

[0013] Furthermore, the specific process by which the cooling control module generates a cooling scheme is as follows:

[0014] S1. Receives cutting thermal parameters and coolant heat transfer coefficient from the heat assessment module; then, based on the heat flux in the cutting area and the coolant heat transfer coefficient, assesses the required cooling heat dissipation using the heat balance equation.

[0015] S2. Based on the cooling heat dissipation, and combined with the specific heat capacity, density and preset temperature difference of the coolant, the initial value of the injection flow rate is obtained by mapping.

[0016] S3. Based on the difference between the peak temperature at the cutting point and the heat resistance threshold of the workpiece or tool, the injection pressure is determined by a proportional adjustment algorithm: if the peak temperature is higher than the preset heat resistance threshold, the pressure increases by a preset proportion for every unit increase in the difference by the preset temperature.

[0017] S4. Based on the geometric boundary of the heat-affected zone, calculate the minimum coverage requirement of the spray angle and locate the initial coordinates of the spray position to the center of the cutting point.

[0018] S5. Match and verify the heat generation per unit time with the heat dissipation capacity of the initial flow rate: If the heat generation is greater than the maximum heat dissipation capacity corresponding to the initial flow rate, then the initial flow rate is adjusted twice according to the ratio of heat generation to heat dissipation capacity.

[0019] S6. Integrate the jet flow rate, pressure, angle and position parameters obtained in steps S1-S5 to form an initial cooling scheme before processing.

[0020] Furthermore, it also includes an optimization module; the optimization module is used to collect temperature data of the workpiece cutting area in real time during the workpiece cutting process and generate a real-time temperature change map; the real-time temperature change map is compared with the heat-affected zone range output by the heat assessment module to calculate the temperature deviation value;

[0021] If the temperature deviation exceeds the preset deviation threshold, the current cooling scheme will be dynamically corrected based on the direction and magnitude of the deviation: when the real-time temperature is higher than the pre-evaluated temperature, the injection flow rate and pressure will be increased proportionally to the deviation; when the real-time temperature is lower than the pre-evaluated temperature, the current cooling scheme will not be adjusted.

[0022] Furthermore, after the cooling scheme is corrected, the optimization module monitors the temperature response rate and steady-state temperature fluctuation range of the cutting area in real time: if the temperature response rate reaches the preset target within 1 to 2 seconds after correction, and the steady-state temperature fluctuation range is ≤ ±3℃, the correction is deemed effective and the current cooling scheme is maintained; if the target is not reached, the scheme is adjusted again based on the secondary temperature deviation value, with the adjustment range being 25% to 40% of the first correction range.

[0023] Furthermore, in the initial cooling scheme, the initial value of the jet flow rate corresponds to a maximum heat dissipation capacity of 110% to 120% of the heat generated per unit time.

[0024] Furthermore, the process of establishing the fever prediction model is as follows:

[0025] Step 1: Select workpiece material and tool type that match the actual machining, design several sets of orthogonal experiments. The variables of each set of orthogonal experiments include the angle between the tool and the cutting surface, the feed rate, the process type, the thermal diffusivity and the coefficient of thermal expansion. Simultaneously collect the cutting thermal parameters under each set of experiments to form a sample set containing more than 1,000 sets.

[0026] Step 2: The main and secondary cutting edges of the tool and the cutting surface are used as core input features. They are normalized with the feed rate, process type, and thermal diffusivity. After removing abnormal data, key features with a weight of ≥0.6 on the impact of heat generation are selected through correlation analysis.

[0027] Step 3: Use the gradient boosting tree algorithm to build a prediction model. With key features as input and cutting thermal parameters as output, divide the samples into 80% as training set and 20% as test set. Iteratively optimize the heat prediction model until the prediction error of the heat prediction model is ≤5%.

[0028] Furthermore, a CNC machine tool system includes a CNC system, a servo drive system, and a machine tool body, and also includes a machine tool cooling device, wherein the machine tool cooling device establishes bidirectional signal communication with the CNC system and the servo drive system;

[0029] When the CNC system issues the preset workpiece cutting path and machining instructions, it simultaneously transmits the cutting parameters to the machine tool cooling device; the servo drive system provides real-time feedback of the tool's dynamic data to the machine tool cooling device; the machine tool cooling device is used to evaluate the cutting thermal parameters of the workpiece's pre-cutting position based on the preset workpiece cutting path and machining instructions, and then, in conjunction with the preset coolant heat transfer coefficient, output a cooling scheme before the workpiece is cut.

[0030] The above approach has the following beneficial effects:

[0031] 1. This solution overcomes the limitations of traditional fixed cooling parameters by employing a pre-precise prediction and differentiated adaptation mechanism, achieving differentiated and efficient cutting cooling. First, the pre-acquisition module comprehensively captures cutting parameters such as tool infeed posture and speed, as well as workpiece characteristic parameters such as thermal diffusivity and coefficient of thermal expansion. Combined with key variables such as the tool-cutting surface angle, and based on a sample set containing over 1000 data points, a heat prediction model with a prediction error ≤5% is constructed using a gradient boosting tree algorithm. This model accurately assesses core cutting thermal parameters such as heat flux and peak temperature, predicting the differences in heat dissipation requirements for different workpiece materials (e.g., titanium alloys, aluminum alloys) and different process types (roughing / finishing). Second, the cooling control module calculates heat dissipation requirements using a thermal balance equation. Combined with a 110%–120% heat dissipation redundancy design, it outputs targeted combinations of jet flow rate, pressure, angle, and position. This satisfies the rapid heat dissipation requirements of high loads and high heat generation in roughing while adapting to the stringent cooling precision requirements of finishing, reducing workpiece thermal deformation and tool chipping caused by insufficient cooling, and minimizing resource waste caused by over-cooling.

[0032] 2. In this solution, a multi-dimensional temperature change graph is generated through an optimization module. This graph is compared with the pre-assessment results to calculate the deviation. When the real-time temperature exceeds the predicted value, the cooling parameters are adjusted proportionally. Combined with a rapid response verification mechanism and a secondary correction margin of 25%–40%, this ensures that temperature fluctuations are controlled within ±3℃, effectively compensating for heat generation deviations caused by fluctuations in operating conditions during cutting. Simultaneously, the machine tool cooling unit establishes bidirectional communication with the CNC system and servo drive system, receiving machining instructions and tool dynamic data in real time. This achieves seamless coordination between machining parameters, heat generation prediction, cooling control, and status feedback, ensuring that cooling adjustments are synchronized with the machining progress.

[0033] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0034] Figure 1 This is a schematic flowchart of an embodiment of the machine tool cooling device of the present invention;

[0035] Figure 2 This is a flowchart illustrating an embodiment of the CNC machine tool system of the present invention;

[0036] Figure 3 This is a flowchart illustrating the generation of a cooling scheme in the cooling control module of an embodiment of the machine tool cooling device of the present invention. Detailed Implementation

[0037] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] The following detailed description illustrates the specific implementation methods:

[0039] Example 1:

[0040] like Figure 1 As shown, a machine tool cooling device includes a pre-processing acquisition module, a heat assessment module, and a cooling control module.

[0041] Specifically, the pre-processing acquisition module actively collects two types of key parameters based on preset workpiece cutting path data before actual machining: Firstly, cutting parameters, including the tool approach posture (affecting the cutting contact area), tool feed speed (related to cutting force and heat generation rate), and process type (distinguishing the differences in heat generation characteristics between roughing and finishing), which directly reflect the dynamic characteristics of machining. These parameters are directly extracted through user-preset machining commands. Secondly, workpiece characteristic parameters, focusing on thermal diffusivity (determining the speed of heat conduction within the workpiece) and coefficient of thermal expansion (related to the risk of thermal deformation). Both reflect the workpiece's sensitivity to cutting heat and its heat dissipation capacity. By comprehensively collecting these cutting and characteristic parameters, the module can specifically adapt to the differentiated needs of different materials (such as titanium alloys and aluminum alloys) and different processes.

[0042] Specifically, the heat generation assessment module is used to establish a heat generation prediction model. Using cutting parameters as the main input parameters and characteristic parameters as auxiliary variables, it assesses the cutting thermal parameters at the pre-cutting position of the workpiece and outputs the assessment results. These cutting thermal parameters include the heat flux in the cutting zone, the peak temperature at the cutting point, the heat generated per unit time, the range of the heat-affected zone, and the rate of temperature change over time.

[0043] The establishment of the fever prediction model relies on experimental data and algorithm optimization to ensure prediction accuracy, as detailed below:

[0044] Step 1: Select workpiece material and tool type that match the actual machining, design several sets of orthogonal experiments. The variables of each set of orthogonal experiments include the angle between the tool and the cutting surface, the feed rate, the process type, the thermal diffusivity and the coefficient of thermal expansion. Simultaneously collect the cutting thermal parameters under each set of experiments to form a sample set containing more than 1,000 sets.

[0045] Step 2: The main and secondary cutting edges of the tool and the cutting surface are used as core input features. They are normalized with the feed rate, process type, and thermal diffusivity. After removing abnormal data, key features with a weight of ≥0.6 on the impact of heat generation are selected through correlation analysis.

[0046] Step 3: Use the gradient boosting tree algorithm to build a prediction model. With key features as input and cutting thermal parameters as output, divide the samples into 80% as training set and 20% as test set. Iteratively optimize the heat prediction model until the prediction error of the heat prediction model is ≤5%.

[0047] Specifically, the cooling control module is used to output a cooling plan before workpiece machining based on the evaluation results and the coolant heat transfer coefficient. The cooling plan includes the injection flow rate, injection pressure, injection angle, and injection position.

[0048] Combination Figure 3 As shown, the specific process for generating the cooling scheme is as follows:

[0049] S1. Receive the cutting thermal parameters and coolant heat transfer coefficient output by the heat generation assessment module; then, based on the heat flux in the cutting area and the coolant heat transfer coefficient, assess the required cooling heat dissipation through the heat balance equation; wherein, the heat balance equation is as follows:

[0050]

[0051] Where Q is the heat to be dissipated per unit time, q is the heat flux, S is the area of ​​the cutting zone, K is the heat transfer coefficient, and ΔT is the temperature difference between the coolant and the cutting zone.

[0052] S2. Based on the cooling heat dissipation, and combined with the specific heat capacity, density and preset temperature difference of the coolant, the initial value of the injection flow rate is obtained by mapping.

[0053] S3. Based on the difference between the peak temperature at the cutting point and the heat resistance threshold of the workpiece / tool, the injection pressure is determined by a proportional adjustment algorithm: if the peak temperature is higher than the preset heat resistance threshold, the pressure increases by a preset proportion for every unit increase in the difference by the preset temperature.

[0054] S4. Based on the geometric boundary of the heat-affected zone, calculate the minimum coverage requirement of the spray angle and locate the initial coordinates of the spray position to the center of the cutting point.

[0055] S5. Match and verify the heat generation per unit time with the heat dissipation capacity of the initial flow rate: If the heat generation is greater than the maximum heat dissipation capacity corresponding to the initial flow rate, then the initial flow rate is corrected a second time according to the ratio of heat generation to heat dissipation capacity. The deviation between theoretical calculation and actual working conditions is eliminated through secondary verification to ensure sufficient cooling capacity.

[0056] S6. Integrate the jet flow rate, pressure, angle and position parameters obtained in steps S1-S5 to form an initial cooling scheme before processing.

[0057] In addition, this solution also includes an optimization module, which is used to collect temperature data of the workpiece cutting area in real time during the workpiece cutting process and generate a real-time temperature change map, which can be detected by a non-contact infrared thermometer; the real-time temperature change map is compared with the heat-affected zone range output by the heat assessment module to calculate the temperature deviation value.

[0058] If the temperature deviation exceeds the preset deviation threshold, the current cooling scheme will be dynamically corrected based on the direction and magnitude of the deviation: when the real-time temperature is higher than the pre-evaluated temperature, the injection flow rate and pressure will be increased proportionally to the deviation; when the real-time temperature is lower than the pre-evaluated temperature, the current cooling scheme will not be adjusted.

[0059] Meanwhile, after the cooling scheme is corrected, the optimization module monitors the temperature response rate and steady-state temperature fluctuation range of the cutting area in real time: if the temperature response rate reaches the preset target within 1 to 2 seconds after correction, and the steady-state temperature fluctuation range is ≤ ±3℃, the correction is deemed effective and the current cooling scheme is maintained; if the target is not reached, the scheme is adjusted again based on the secondary temperature deviation value, with the adjustment range being 25% to 40% of the initial correction range. Furthermore, in the initial cooling scheme, the maximum heat dissipation capacity corresponding to the initial value of the jet flow rate is configured to be 110% to 120% of the heat generated per unit time. This redundancy design is based on possible heat generation fluctuations during the cutting process (such as material inhomogeneity, instantaneous deviation of feed rate) and system response delay, reserving a buffer margin to cope with sudden overheating situations.

[0060] Example 2:

[0061] like Figure 2 As shown, a CNC machine tool system includes a CNC system, a servo drive system, and a machine tool body, and also includes the machine tool cooling device described in Embodiment 1. The machine tool cooling device establishes bidirectional signal communication with the CNC system and the servo drive system.

[0062] When the CNC system issues the preset workpiece cutting path and machining instructions, it simultaneously transmits the cutting parameters to the machine tool cooling device; the servo drive system provides real-time feedback of the tool's dynamic data to the machine tool cooling device; the machine tool cooling device is used to evaluate the cutting thermal parameters of the workpiece's pre-cutting position based on the preset workpiece cutting path and machining instructions, and then, in conjunction with the preset coolant heat transfer coefficient, output a cooling scheme before the workpiece is cut.

[0063] Specifically, the machine tool cooling unit does not operate independently, but rather establishes high-speed bidirectional signal communication with the CNC system and servo drive system, forming a collaborative closed loop of data exchange and action linkage. The CNC system, as the command center of the entire machine tool, simultaneously transmits core cutting parameters (including preset values ​​for tool approach posture, reference values ​​for tool approach speed, and process type identifiers) to the front-end acquisition module of the machine tool cooling unit when issuing preset workpiece cutting paths and machining commands (such as G-codes and M-codes), providing initial input data for the heat generation prediction model.

[0064] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A machine tool cooling device, characterized in that, include: The pre-acquisition module is used to acquire cutting parameters and workpiece characteristic parameters of the tool cutting the workpiece surface during the actual cutting process, based on preset workpiece cutting path data. The cutting parameters include tool approach posture, tool approach speed, and process type. The tool approach posture, tool approach speed, and process type are acquired through user-preset machining instructions. The characteristic parameters include thermal diffusivity and coefficient of thermal expansion. The heat generation assessment module is used to establish a heat generation prediction model. It uses cutting parameters as the main input parameters and characteristic parameters as auxiliary variables to evaluate the cutting thermal parameters at the pre-cutting position of the workpiece and output the evaluation results. The cutting thermal parameters include the heat flux of the cutting area, the peak temperature at the cutting point, the heat generated per unit time, the range of the heat-affected zone, and the rate of temperature change over time. The cooling control module is used to obtain the preset heat transfer coefficient of the coolant, and then, in combination with the evaluation results, output a cooling plan before the workpiece is machined. The cooling plan includes the spray flow rate, spray pressure, spray angle, and spray position. The specific process by which the cooling control module generates a cooling scheme is as follows: S1. Receive the cutting thermal parameters and the heat transfer coefficient of the coolant output by the heat assessment module; then, based on the heat flux of the cutting area and the heat transfer coefficient of the coolant, assess the required cooling heat dissipation through the heat balance equation. S2. Based on the cooling heat dissipation, and combined with the specific heat capacity, density and preset temperature difference of the coolant, the initial value of the injection flow rate is obtained by mapping. S3. Based on the difference between the peak temperature at the cutting point and the heat resistance threshold of the workpiece or tool, the injection pressure is determined by a proportional adjustment algorithm: if the peak temperature is higher than the preset heat resistance threshold, the pressure increases by a preset proportion for every unit increase in the difference by the preset temperature. S4. Based on the geometric boundary of the heat-affected zone, calculate the minimum coverage requirement of the spray angle and locate the initial coordinates of the spray position to the center of the cutting point. S5. Match and verify the heat generation per unit time with the heat dissipation capacity of the initial flow rate: If the heat generation is greater than the maximum heat dissipation capacity corresponding to the initial flow rate, then the initial flow rate is adjusted twice according to the ratio of heat generation to heat dissipation capacity. S6. Integrate the jet flow rate, pressure, angle and position parameters obtained in steps S1-S5 to form an initial cooling scheme before processing.

2. The machine tool cooling device according to claim 1, characterized in that, It also includes an optimization module; the optimization module is used to collect temperature data of the workpiece cutting area in real time during the workpiece cutting process and generate a real-time temperature change map; the real-time temperature change map is compared with the heat-affected zone range output by the heat assessment module to calculate the temperature deviation value; If the temperature deviation exceeds the preset deviation threshold, the current cooling scheme will be dynamically corrected based on the direction and magnitude of the deviation: when the real-time temperature is higher than the pre-evaluated temperature, the injection flow rate and pressure will be increased proportionally to the deviation; when the real-time temperature is lower than the pre-evaluated temperature, the current cooling scheme will not be adjusted.

3. The machine tool cooling device according to claim 2, characterized in that, After the cooling scheme is corrected, the optimization module monitors the temperature response rate and steady-state temperature fluctuation range of the cutting area in real time: if the temperature response rate reaches the preset target within 1 to 2 seconds after correction, and the steady-state temperature fluctuation range is ≤ ±3℃, the correction is deemed effective and the current cooling scheme is maintained; if the target is not reached, the scheme is adjusted again based on the secondary temperature deviation value, with the adjustment range being 25% to 40% of the first correction range.

4. The machine tool cooling device according to claim 3, characterized in that, In the initial cooling scheme, the initial value of the jet flow rate corresponds to a maximum heat dissipation capacity of 110% to 120% of the heat generated per unit time.

5. The machine tool cooling device according to claim 4, characterized in that, The process of establishing the fever prediction model is as follows: Step 1: Select workpiece material and tool type that match the actual machining, design several sets of orthogonal experiments. The variables of each set of orthogonal experiments include the angle between the tool and the cutting surface, the feed rate, the process type, the thermal diffusivity and the coefficient of thermal expansion. Simultaneously collect the cutting thermal parameters under each set of experiments to form a sample set containing more than 1,000 sets. Step 2: The main and secondary cutting edges of the tool and the cutting surface are used as core input features. They are normalized with the feed rate, process type, and thermal diffusivity. After removing abnormal data, key features with a weight of ≥0.6 on the impact of heat generation are selected through correlation analysis. Step 3: Use the gradient boosting tree algorithm to build a prediction model. With key features as input and cutting thermal parameters as output, divide the samples into 80% as training set and 20% as test set. Iteratively optimize the heat prediction model until the prediction error of the heat prediction model is ≤5%.

6. A CNC machine tool system, comprising a CNC system, a servo drive system, and a machine tool body, and further comprising any one of the machine tool cooling devices described in claims 1-5, characterized in that, The machine tool cooling unit establishes bidirectional signal communication with the CNC system and servo drive system; When the CNC system issues the preset workpiece cutting path and machining instructions, it simultaneously transmits the cutting parameters to the machine tool cooling device; the servo drive system provides real-time feedback of the tool's dynamic data to the machine tool cooling device. The machine tool cooling unit is used to evaluate the cutting thermal parameters of the workpiece at the pre-cutting position based on the preset workpiece cutting path and machining instructions, and then output a cooling plan before the workpiece is cut.

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