A forging processing method and temperature regulation method of a new energy automobile part

Abstract

The present application relates to the technical field of metal forming machine tool control, and proposes a forging processing method of new energy automobile parts and a temperature regulation method thereof, which comprises the following steps: obtaining part surface temperature data, machine tool pressure data, part stress data and environmental wind speed data at several time points in the part forging process; obtaining several monitoring time periods; analyzing the temperature data change trend and determining the part forging temperature loss rate in combination with the environmental wind speed data; performing similarity analysis on the stress data and machine tool pressure data at each position to obtain the processing stress of each position on the part surface; obtaining the non-uniformity of the processing temperature distribution; obtaining the non-smoothness of the temperature change; quantifying the temperature control lag degree of each monitoring time period; determining the response coefficient of the forging processing temperature control system, and feeding back and adjusting the temperature control system through a PID controller. The present application aims to solve the problem that frequent temperature adjustment in the forging process will affect the processing quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal forming machine tool control, and particularly relates to a forging processing method of new energy automobile parts and a temperature regulation method thereof. BACKGROUND

[0002] The power system, power battery and corresponding mechanical parts of new energy vehicles require lightweight, high strength and high performance, which promotes the importance of forging processing technology in the manufacturing of their parts. In the forging process, temperature regulation is a key factor to ensure the quality and performance of the parts. Proper forging temperature not only affects the plasticity and formability of the material, but also directly affects the microstructure and mechanical properties of the parts. By precisely controlling the forging temperature, the strength, toughness and fatigue life of the product can be effectively improved, thereby ensuring the overall performance and safety of new energy vehicles.

[0003] In existing forging processing metal forming, the control of forging temperature relies on the heating and temperature holding process in the traditional operation method. The temperature in the forging process is monitored by a single sensor, which cannot comprehensively and real-time monitor the temperature of the forging processing process. When the temperature fluctuates greatly, it cannot be adjusted in time, thereby producing frequent heating and temperature holding processes. The temperature is always in continuous fluctuation, which seriously affects the forging processing quality of new energy automobile parts. At the same time, the experience temperature holding process lacks intelligent and real-time feedback automatic adjustment mechanism, so that the forging process is easily disturbed by human factors, affecting the stability of the processing quality. SUMMARY

[0004] The present application provides a forging processing method of new energy automobile parts and a temperature regulation method thereof to solve the problem that frequent temperature adjustment in the existing forging processing process affects the processing quality. The technical solution adopted is as follows:

[0005] The present application provides a forging processing temperature regulation method of new energy automobile parts, which comprises the following steps:

[0006] Obtain the part surface temperature data, machine tool pressure data, part stress data and environmental wind speed data at several moments of the part forging processing process;

[0007] Obtain several monitoring periods; analyze the downward trend of the part surface temperature data, combine the environmental wind speed data and the difference between the part surface temperature data and the forging temperature range, and determine the part forging temperature loss rate of each monitoring period; analyze the similarity of the stress data and machine tool pressure data of each position on the part surface during the forging processing process, and combine the overall numerical value of the machine tool pressure data to obtain the processing stress of each position on the part surface in each monitoring period.

[0008] Based on the part forging temperature loss rate of the monitoring period, and the processing stress difference between different positions on the part surface, the processing temperature distribution unevenness of each monitoring period is obtained; based on the difference between the temperature performance before the temperature adjustment operation in the monitoring period and the forging temperature range, combined with the processing temperature distribution unevenness, the temperature change non-smoothness of each monitoring period is obtained;

[0009] Based on the time difference between the predicted value and the actual value of the part surface temperature data in the monitoring period exceeding the forging temperature range, the temperature control lag degree of each monitoring period is quantified; combined with the temperature change non-smoothness, the response coefficient of the forging processing temperature control system is determined.

[0010] Optionally, the method for analyzing the falling trend of the part surface temperature data comprises the following specific method:

[0011] For the part surface temperature data at each time in any monitoring period, the ratio of the difference between the part surface temperature data at any time and the part surface temperature data at the adjacent previous time to the time interval between the adjacent times is taken as the surface temperature change slope at the time;

[0012] A plurality of times with negative surface temperature change slope in the monitoring period are obtained as a plurality of falling times; the product of the proportion of the number of falling times in all times in the monitoring period and the absolute value average of the surface temperature change slope of all falling times in the monitoring period is taken as the processing temperature falling rate of the monitoring period.

[0013] Optionally, the part forging temperature loss rate of each monitoring period is obtained by the following specific method:

[0014] The forging temperature range is obtained; the absolute value of the difference between the part surface temperature data at each time in any monitoring period and the lower limit of the forging temperature range is obtained, and the average of the absolute value of the difference at all times in the monitoring period is obtained as the forging temperature lower limit deviation degree of the monitoring period;

[0015] The ratio of the average of the environmental wind speed data at all times in the monitoring period to the forging temperature lower limit deviation degree is obtained, combined with the processing temperature falling rate of the monitoring period, to obtain the part forging temperature loss rate of the monitoring period, and the part forging temperature loss rate is positively correlated with the ratio and the processing temperature falling rate.

[0016] Optionally, the processing stress of each position on the part surface of each monitoring period is obtained by the following specific method:

[0017] Numerical processing is performed on all machine tool pressure data to obtain normalized pressure values at each time; based on the machine tool pressure data at each time within any monitoring period and the part stress data at any position, a pressure fitting curve for the monitoring period and a stress fitting curve for the position are obtained;

[0018] The Pearson correlation coefficient is obtained by comparing the pressure fitting curve and the stress fitting curve, and the product of the Pearson correlation coefficient and the average of the normalized pressure values at all times within the monitoring period is used as the machining stress of the part surface at the position.

[0019] Optionally, the method for obtaining the machining temperature distribution unevenness of each monitoring period includes the following specific steps:

[0020] For any monitoring period, the absolute value of the difference between the machining stress of any two positions of the part within the monitoring period is obtained, and the average of all machining stress difference absolute values is obtained as the stress difference of the monitoring period; combined with the forging temperature loss rate of the part in the monitoring period, the machining temperature distribution unevenness of the monitoring period is obtained, and the machining temperature distribution unevenness is positively correlated with the stress difference and the forging temperature loss rate of the part.

[0021] Optionally, the method for obtaining the temperature change non-smoothness of each monitoring period includes the following specific steps:

[0022] For any monitoring period, the time when the temperature adjustment operation occurs within the monitoring period is recorded, which is denoted as several temperature adjustment times, and the part surface temperature data at the adjacent previous time of each temperature adjustment time is recorded, which is denoted as the adjustment temperature data before each temperature adjustment time.

[0023] According to the situation that the adjustment temperature data exceeds the upper limit or lower limit of the forging temperature range, the temperature deviation degree of each temperature adjustment time is obtained.

[0024] The average of the temperature deviation degrees of all temperature adjustment times is multiplied by the machining temperature distribution unevenness of the monitoring period to obtain the temperature change non-smoothness of the monitoring period.

[0025] Optionally, the method for quantifying the temperature control lag degree of each monitoring period includes the following specific steps:

[0026] For each time within each monitoring period in the same forging process, the multi-dimensional data at each time is obtained by Kalman filtering to obtain the machining predicted temperature data at each time.

[0027] acquire the time point when the first part surface temperature data exceeds the forging temperature range in any monitoring period, and mark it as the processing temperature deviation time point; after the processing temperature deviation time point, the system restores the part surface temperature data to the forging temperature range through temperature adjustment operation, and after the restoration is completed, the next part surface temperature data exceeding the forging temperature range is acquired, and marked as the processing temperature deviation time point, and so on, so as to acquire several processing temperature deviation time points in the monitoring period;

[0028] Based on the processing prediction temperature data at each time point in the monitoring period, the corresponding processing temperature deviation time point is acquired, and marked as several prediction temperature deviation time points; the prediction temperature deviation time point and the processing temperature deviation time point are matched according to the order value, and the processing temperature deviation time point and the prediction temperature deviation time point with the same order value are taken as a group.

[0029] The difference obtained by subtracting the prediction temperature deviation time point from the processing temperature deviation time point of any group in the monitoring period is taken as the temperature control hysteresis length of the group, and the mean value of the temperature control hysteresis lengths of all groups is taken as the temperature control hysteresis degree of the monitoring period.

[0030] Optionally, the specific method for obtaining the processing prediction temperature data at each time point includes:

[0031] For any monitoring period, the part surface temperature data, machine tool pressure data, environmental wind speed data and part stress data at each position in the forging process of the monitoring period are acquired, and the multi-dimensional data at the same time point constitutes a state vector at the corresponding time point.

[0032] The state vectors of all time points before any time point in the monitoring period are input into Kalman filtering, and the prediction state vector of the time point is output, and then the part surface prediction temperature data of the time point is obtained as the processing prediction temperature data of the time point; the processing prediction temperature data at each time point in the monitoring period is acquired.

[0033] Optionally, the response coefficient of the forging processing temperature control system is acquired by the following specific method:

[0034] Based on the temperature change non-smoothness and the temperature control hysteresis degree of each monitoring period in the forging process of the current monitoring period, the temperature change non-smoothness factor and the temperature control hysteresis factor of each monitoring period are acquired by normalization processing respectively.

[0035] The product of the temperature change non-smoothness factor and the temperature control hysteresis factor of the current monitoring period is taken as the response coefficient of the forging processing temperature control system in the current monitoring period.

[0036] The application further provides a forging processing method for new energy automobile parts, and the method comprises:

[0037] a forging processing data acquisition module configured to acquire part surface temperature data, machine pressure data, part stress data, and environmental wind speed data at several time points in a part forging processing procedure;

[0038] a forging processing data analysis module configured to acquire several monitoring time periods, analyze the falling trend of the part surface temperature data, determine the part forging temperature loss rate at each monitoring time period in combination with the environmental wind speed data and the difference between the part surface temperature data and the forging temperature range, and perform similarity analysis on the stress data and the machine pressure data at each position on the part surface during the forging processing procedure in combination with the overall numerical performance of the machine pressure data to obtain the processing stress performance at each position on the part surface at each monitoring time period;

[0039] based on the part forging temperature loss rate at the monitoring time period and the processing stress performance difference between different positions on the part surface, the processing temperature distribution unevenness at each monitoring time period is obtained; based on the difference between the temperature performance before the temperature adjustment operation and the forging temperature range within the monitoring time period and in combination with the processing temperature distribution unevenness, the temperature change non-smoothness at each monitoring time period is obtained;

[0040] based on the time difference between the predicted value of the part surface temperature data and the actual value exceeding the forging temperature range within the monitoring time period, the temperature control lag degree at each monitoring time period is quantified; in combination with the temperature change non-smoothness, the response coefficient of the forging processing temperature control system is determined;

[0041] a forging processing temperature control module configured to perform feedback adjustment on the temperature control system of the forging processing procedure through a PID controller.

[0042] The beneficial effects of the present application are: the present application analyzes the temperature drop trend in the monitoring period in the one-time forging process, combines the influence of air convection generated by environmental wind speed data on temperature heat dissipation, and the distribution of part surface temperature data in the forging temperature range, quantifies the forging temperature loss rate of the part, to reflect the temperature loss in the monitoring period in the forging process, then through analyzing the heat loss state, the temperature change in the forging process is found in advance, and according to the stress influence of temperature change on the part, the stress and pressure of each position of the part are analyzed, the processing stress is quantified, and the basis for subsequent stress distribution uniformity analysis of each position of the part is provided; by analyzing the processing stress difference of different positions, combining the forging temperature loss rate of the part, the unevenness of the overall stress distribution of the part in the forging process is quantified, and the processing temperature distribution unevenness of the part is further reflected, and the influence of temperature change and uneven distribution on stress is fully considered; and the temperature performance before temperature adjustment in the monitoring period is analyzed, the more the temperature exceeds the forging temperature range, the greater the influence of temperature adjustment operation on the temperature change fluctuation in the monitoring period, combined with the temperature distribution unevenness, the temperature change non-smoothness is obtained, reflecting the temperature unstable change in the monitoring period; based on the lag performance of the temperature control system for temperature adjustment, combined with the temperature change non-smoothness to obtain the response demand of the temperature control system, to output the adjusted PID parameter to feedback adjust the temperature control system, and ensure the temperature stability of the part in the forging process through real-time adjustment, reduce the interference of human factors on the temperature adjustment of the forging process, and improve the stability and production efficiency of the new energy automobile part in the forging process. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0044] Figure 1 A flow chart of a forging processing temperature regulation method for a new energy automobile part provided by an embodiment of the present application;

[0045] Figure 2 A fitting curve diagram of the pressure of the forging machine and the stress of the part;

[0046] Figure 3 A structure block diagram of a forging processing method for a new energy automobile part provided by another embodiment of the present application. DETAILED DESCRIPTION

[0047] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0048] Please refer to Figure 1 which shows a new energy automobile part forging processing temperature regulation method flow chart provided by an embodiment of the present application, the method comprises the following steps:

[0049] Step S001, obtaining the part surface temperature data, machine tool pressure data, part stress data and environmental wind speed data at several moments of the part forging processing process.

[0050] In the power system of a new energy automobile, core parts such as motor rotor, stator and transmission shaft need to be produced by forging process, and these parts are required to have superior mechanical properties (such as high strength and toughness) and excellent wear resistance to ensure the stable operation of the electric vehicle under high load and high temperature environment; the specific processing flow is: material selection and preparation, forging processing technology, part forging forming, post-processing and finishing, quality detection and verification, and application feedback and adjustment.

[0051] The purpose of the embodiment is to timely adjust the temperature control measures for the forging processing process in the forging processing process of the new energy automobile parts, reduce the frequency of temperature regulation, so as to ensure that the metal parts can be in a more stable processing temperature range during the forming process, and ensure the processing quality of the parts, then the related temperature data and stress data of the parts in the forging processing process, and the machine tool pressure data and environmental wind speed data of the forging process are first needed.

[0052] Specifically, a high-precision temperature sensor such as a thermocouple or an infrared sensor is used to monitor the surface temperature of the forging material in real time as the part surface temperature data; at the same time, strain gauges are installed at several positions on the die working surface of the forging material to monitor the stress data at different positions on the part surface, and the part stress data at each position is obtained; the pressure sensor is used to obtain the pressure applied on the forging material by the forging machine during the forging processing process, and the machine tool pressure data is obtained; the wind speed sensor is arranged above the machine tool to monitor the environmental air flow in real time, and the environmental wind speed data is obtained; the sampling time interval of the multiple data in the embodiment is set to 1 second, and a hardware device with multi-channel data acquisition function such as a data acquisition card or an industrial controller is selected to receive the data transmitted by the multiple sensors in real time, and the collected data is transmitted to the data monitoring center in real time, then the multiple data at several moments of the forging processing process are obtained.

[0053] It should be noted that after receiving the multiple data at each time, the data monitoring center analyzes through the data processing software, and dynamically adjusts the temperature in the actual forging process through the feedback mode, so as to ensure that the temperature in the forging process is always in the best working condition.

[0054] Step S002, obtaining a plurality of monitoring periods; analyzing the falling trend of the surface temperature data of the parts, combining the environmental wind speed data and the difference between the surface temperature data of the parts and the forging temperature range, determining the forging temperature loss rate of the parts in each monitoring period; similarity analysis is performed on the stress data of each position on the surface of the parts and the machine tool pressure data in the forging process, and the machining stress of each position on the surface of the parts in each monitoring period is obtained by combining the overall numerical value of the machine tool pressure data.

[0055] It should be noted that in the forging process of new energy automobile parts, the management of heat directly affects the forging quality, material performance and production efficiency; in the forging process, the material is deformed in a high temperature state, but in the whole processing process, heat is lost to the surrounding environment through radiation, convection and heat conduction; heat loss will lead to insufficient forging temperature, which will affect the plasticity and flowability of the material, and increase the risk of defects such as cracks, pores and insufficient forming.

[0056] Preferably, in an embodiment of the present application, a plurality of monitoring periods are obtained, including the following specific methods:

[0057] Taking the whole forging process as a whole period, this embodiment takes 5 minutes as the monitoring time, and the whole period is divided into monitoring periods from the beginning of the forging process, and a plurality of monitoring periods of the forging process are obtained; it is particularly pointed out that if the remaining time is less than 5 minutes, the actual existing time constitutes the last monitoring period of the forging process.

[0058] It should be noted that by analyzing the change trend of the surface temperature data of the parts in the monitoring period, the greater the proportion of the falling trend, the greater the change degree of the falling process, that is, the absolute value of the slope, the faster the temperature drop rate, which will exacerbate the temperature fluctuation of the forging process, and the environmental wind speed data needs to be analyzed, that is, the air flow is strong, the heat loss speed through convection is accelerated, which will cause the surface temperature of the parts to drop rapidly, resulting in rapid loss of temperature in the forging process, so as to quantify the forging temperature loss rate in the monitoring period.

[0059] Preferably, in an embodiment of the present application, the falling trend of the surface temperature data of the parts is analyzed, the environmental wind speed data and the difference between the surface temperature data of the parts and the forging temperature range are combined, and the forging temperature loss rate of the parts in each monitoring period is determined, including the following specific methods:

[0060] For the part surface temperature data of each time in any monitoring period, the difference between the part surface temperature data of any time and the part surface temperature data of the adjacent previous time is divided by the time interval of the adjacent time, and the result is taken as the surface temperature change slope of the time. It is particularly pointed out that the surface temperature change slope of the first time in the monitoring period is calculated by the part surface temperature data of the previous time, i.e. the part surface temperature data of the last time in the previous monitoring period. If the monitoring period is the first monitoring period in the forging process, the surface temperature change slope of the first time is set to 0.

[0061] Further, the number of times with negative surface temperature change slope in the monitoring period is taken as the number of falling times. The product of the proportion of the number of falling times in all times in the monitoring period and the average absolute value of the surface temperature change slope of all falling times in the monitoring period is taken as the processing temperature drop rate of the monitoring period.

[0062] As an example, the calculation method of the processing temperature drop rate u of the monitoring period is as follows:

[0063]

[0064] Wherein, N0 represents the number of times in the monitoring period, N represents the number of falling times in the monitoring period, k j represents the surface temperature change slope of the jth falling time in the monitoring period, and || represents the absolute value function.

[0065] It should be noted that the greater the proportion of the number of falling times, the greater the overall temperature change in the monitoring period shows a downward trend, and the greater the surface temperature change slope, the greater the falling amplitude, the faster the processing temperature drop rate in the corresponding monitoring period, and the greater the processing temperature drop rate.

[0066] Further, the forging process is provided with a fixed forging temperature range. The absolute value of the difference between the part surface temperature data of each time in the monitoring period and the lower limit of the forging temperature range is obtained, and the average of the absolute values of the differences of all times in the monitoring period is taken as the forging temperature lower limit deviation degree of the monitoring period. The ratio of the average of the environmental wind speed data of all times in the monitoring period to the forging temperature lower limit deviation degree is obtained, combined with the processing temperature drop rate of the monitoring period, to obtain the part forging temperature loss rate of the monitoring period. The part forging temperature loss rate is positively correlated with the ratio and the processing temperature drop rate.

[0067] As an example, the calculation method of the part forging temperature loss rate Y of the monitoring period is as follows:

[0068]

[0069] wherein u represents the processing temperature drop rate of the monitoring period, represents the mean value of the ambient wind speed data at all times within the monitoring period, represents the forging temperature lower limit deviation degree of the monitoring period.

[0070] It should be noted that the faster the processing temperature drop rate during the forging processing, the closer the part surface temperature data at each time to the lower limit of the forging temperature range, the more likely the overall temperature to quickly lose and exceed the forging temperature range, and the greater the ambient wind speed data, the stronger the air convection effect, the greater the influence of heat dissipation on the forging processing temperature, resulting in a greater forging temperature loss rate of the part, thereby obtaining the forging temperature loss rate of the part within each monitoring period.

[0071] It should be further noted that during the forging processing of the part, the phenomenon of insufficient temperature not only leads to a decrease in material plasticity, but also may cause an increase in the pressure required to be applied to the material during the forging processing, resulting in a certain degree of deviation in the forging processing requirements of the new energy automobile part; insufficient temperature will reduce the flowability of the material, making it more difficult to deform during forging, thereby requiring greater pressure to complete the forming, which may cause a sharp fluctuation in pressure; when the material fails to uniformly warm up, local overheating or overcooling may occur during forging, leading to uneven stress distribution and thereby causing fluctuations in the applied pressure.

[0072] Preferably, in an embodiment of the present application, the stress data of each position on the surface of the part and the machine tool pressure data during the forging processing are analyzed for similarity, and the overall numerical performance of the machine tool pressure data is obtained to obtain the processing stress of each position on the surface of the part during each monitoring period, including the specific method:

[0073] It should be noted that by analyzing the stress data of each position on the surface of the part and the machine tool pressure data, the greater the similarity of the time sequence, and the greater the overall numerical performance of the machine tool pressure data within the monitoring period, the closer the relationship between the stress change of the corresponding position and the machine tool pressure data, and the greater the internal stress of the workpiece, i.e., the greater the processing stress.

[0074] Specifically, all machine tool pressure data is subjected to numerical processing, i.e., linear normalization processing, to obtain pressure normalization values at each time point, and the stress data of the position of the part is subjected to numerical processing to obtain stress normalization values at each position at each time point; the pressure normalization values at each time point in any monitoring period are subjected to curve fitting, and the stress normalization values at any position at each time point in the monitoring period are subjected to curve fitting to obtain a pressure fitting curve of the monitoring period and a stress fitting curve of the position, as shown in FIG. 8; a Pearson correlation coefficient is obtained from the pressure fitting curve and the stress fitting curve, and the product of the Pearson correlation coefficient and the average of the pressure normalization values at all time points in the monitoring period is taken as the machining stress property of the position on the surface of the part in the monitoring period; the machining stress property of each position on the surface of the part in each monitoring period is obtained according to the above method. Figure 2

[0075] At this point, by analyzing the temperature drop trend in the monitoring period in the one-time forging process, combining the influence of air convection generated by the environmental wind speed data on the heat dissipation of the temperature, and the distribution of the part surface temperature data in the forging temperature range, the forging temperature loss rate of the part is quantified to reflect the temperature loss in the monitoring period in the forging process. By analyzing the heat loss state, the temperature change in the forging process is found in advance, and the stress and pressure of each position of the part are analyzed according to the influence of temperature change on the stress of the part, and the machining stress property is quantified to provide a basis for the subsequent analysis of the stress distribution uniformity of each position of the part.

[0076] Step S003, based on the forging temperature loss rate of the part in the monitoring period and the machining stress property difference between different positions on the surface of the part, the machining temperature distribution non-uniformity of each monitoring period is obtained; based on the difference between the temperature performance before the temperature adjustment operation in the monitoring period and the forging temperature range, combined with the machining temperature distribution non-uniformity, the temperature change non-smoothness of each monitoring period is obtained.

[0077] Preferably, in an embodiment of the present application, based on the forging temperature loss rate of the part in the monitoring period and the machining stress property difference between different positions on the surface of the part, the machining temperature distribution non-uniformity of each monitoring period is obtained, which includes the following specific method:

[0078] It should be noted that during the forging process of the automobile part, the pressure applied is transmitted to the workpiece through the die; generally, the greater the pressure, the greater the stress generated in the workpiece; and the increase in temperature will reduce the yield strength of the material, so that the stress level in some areas may be lower than that in other areas under the same applied pressure; therefore, based on the machining stress property difference between different positions, the temperature change uniformity of each position of the part is further analyzed based on the forging temperature loss rate of the part, so as to quantify the machining temperature distribution non-uniformity.​

[0079] Specifically, for any given monitoring period, the absolute value of the difference in processing stress between any two positions of the component within that monitoring period is obtained. The average of all the absolute values ​​of the difference in processing stress is taken as the stress difference for that monitoring period. Combined with the component forging temperature loss rate during that monitoring period, the processing temperature distribution non-uniformity during that monitoring period is obtained. The processing temperature distribution non-uniformity is positively correlated with both the stress difference and the component forging temperature loss rate.

[0080] It should be noted that the greater the difference in processing stress at different locations, and the greater the average difference in processing stress of the entire component, the more uneven the distribution of processing stress at each location of the component. Combined with a larger forging temperature loss rate, the more uneven the distribution of processing temperature of the component during the monitoring period, resulting in uneven stress distribution.

[0081] It should be further noted that after in-depth analysis of pressure fluctuations during the forging process of parts, the amplitude and frequency of pressure fluctuations are closely related to the unevenness of temperature distribution during the processing. Larger pressure fluctuations often indicate that there are significant differences in the temperature distribution of the material during the heating process, which will affect the quality and performance of the final product. Therefore, it is necessary to analyze the smoothness of temperature changes during the forging process in order to more comprehensively quantify the impact of temperature on processing quality.

[0082] Preferably, in one embodiment of the present invention, based on the difference between the temperature performance before the temperature adjustment operation and the forging temperature range during the monitoring period, and combined with the non-uniformity of the processing temperature distribution, the temperature change non-smoothness of each monitoring period is obtained, including the following specific method:

[0083] It should be noted that during the forging process of automotive parts, the temperature control system is adjusted by setting a forging temperature range in advance. When the temperature exceeds the upper limit of the forging temperature range, the system will automatically lower the temperature to prevent overheating. Although this adjustment mechanism is necessary, it may cause abrupt changes in temperature, affecting the slow and continuous change of temperature. When adjusting the temperature, it may cause the temperature to change rapidly in a short period of time. Such drastic temperature fluctuations will increase the stress and non-uniformity of the material, thereby affecting the forging effect.

[0084] Specifically, for any monitoring period, record the time when the temperature adjustment operation occurs in the monitoring period, denoted as several temperature adjustment times, and record the surface temperature data of the adjacent previous time of each temperature adjustment time, denoted as the temperature data before adjustment of each temperature adjustment time; for any temperature adjustment time, obtain the ratio of the temperature data before adjustment to the upper limit of the forging temperature range, and the ratio of the lower limit of the forging temperature range to the temperature data before adjustment, and the sum of the two ratios is the temperature deviation degree of the temperature adjustment time; the product of the average value of the temperature deviation degrees of all temperature adjustment times and the unevenness of the processing temperature distribution in the monitoring period is taken as the temperature change non-smoothness of the monitoring period.

[0085] It should be noted that the greater the ratio of the temperature data before adjustment to the upper limit of the forging temperature range, the greater the surface exceeds the upper limit of the forging temperature range, and similarly, the greater the ratio of the lower limit of the forging temperature range to the temperature data before adjustment, the smaller the lower limit of the forging temperature range. The sum of the two ratios indicates that the temperature deviation before the temperature adjustment time is greater. At this time, the temperature adjustment operation controls the forging temperature to recover to the forging temperature range, which will cause the temperature of the forging process to fluctuate sharply. Combined with the unevenness of the processing temperature distribution, it further indicates that the temperature change is fluctuating sharply and unevenly distributed in the monitoring period, and the temperature change non-smoothness is greater.

[0086] So far, by analyzing the stress difference of different positions of the processing, combined with the forging temperature loss rate of the parts, the unevenness of the overall stress distribution of the parts in the forging process is quantified, which further reflects the unevenness of the processing temperature distribution of the parts. The influence of temperature change and uneven distribution on stress is fully considered; and the temperature before adjustment in the monitoring period is analyzed, the more it exceeds the forging temperature range, the greater the influence of the temperature adjustment operation on the fluctuation of the temperature change in the monitoring period, combined with the temperature distribution unevenness, the temperature change non-smoothness is obtained, which reflects the unstable change of the temperature in the monitoring period.

[0087] Step S004, based on the time difference between the predicted value and the actual value of the surface temperature data of the parts in the monitoring period exceeding the forging temperature range, quantifying the temperature control lag degree of each monitoring period; combined with the temperature change non-smoothness, determine the response coefficient of the forging processing temperature control system, and feedback adjust the temperature control system of the forging processing process through the PID controller.

[0088] Preferably, in an embodiment of the present application, based on the time difference between the predicted value and the actual value of the surface temperature data of the parts in the monitoring period exceeding the forging temperature range, the temperature control lag degree of each monitoring period is quantified, which includes the specific method:

[0089] It should be noted that since the forging processing of automobile parts is carried out in batches, there are multiple monitoring time periods in the same batch of forging processing, and the temperature change is related to the actual processing technology and the material of the parts, so the temperature change in the same batch of forging processing is analyzed, and the Kalman filter is combined with the historical multi-dimensional data at each time to perform smoothing prediction.

[0090] Specifically, for any monitoring time period, the part surface temperature data, machine tool pressure data, environmental wind speed data and part stress data at each position in the forging process of the monitoring time period are obtained, and the multi-dimensional data at the same time constitute the state vector at the corresponding time. The state vectors of all times before any time in the monitoring time period (times in the forging process) are input into the Kalman filter, and the predicted state vector at the time is output, and then the part surface predicted temperature data at the time is obtained as the processing predicted temperature data at the time. The processing predicted temperature data at each time in the monitoring time period is obtained.

[0091] Further, the time when the first part surface temperature data in the monitoring time period exceeds the forging temperature range is recorded as the processing temperature deviation time; after the processing temperature deviation time, the system restores the part surface temperature data to the forging temperature range through temperature adjustment operation, and continues to traverse (the recovery process is from the forging temperature range to the forging temperature range) after the recovery is completed, and obtains the time when the next part surface temperature data exceeds the forging temperature range (the first time when the forging temperature range is exceeded after the time when the recovery is completed) is recorded as the processing temperature deviation time. Similarly, a number of processing temperature deviation times are obtained in the monitoring time period.

[0092] Further, according to the above method, the processing predicted temperature data at each time in the monitoring time period is obtained, and the corresponding processing temperature deviation time is recorded as a number of predicted temperature deviation times; the processing temperature deviation time and the predicted temperature deviation time are matched according to the order value, and the processing temperature deviation time and the predicted temperature deviation time with the same order value are taken as a group; it should be noted that if the number of processing temperature deviation times and the number of predicted temperature deviation times are different, the order value corresponding to the smallest number is matched, and the extra processing temperature deviation time or predicted temperature deviation time is not analyzed.

[0093] Further, the difference obtained by subtracting the predicted temperature deviation time from the processing temperature deviation time of any group in the monitoring time period is taken as the temperature control lag length of the group, and the mean value of the temperature control lag length of all groups is taken as the temperature control lag degree of the monitoring time period.

[0094] It needs to be explained that the greater the difference between the corresponding processing temperature deviation moment and the intersection prediction temperature deviation moment in the monitoring period, the greater the control delay of the temperature control system, that is, the temperature has exceeded the forging temperature range under normal prediction, and the system responds to control after a period of time, and the greater the temperature control lag degree.

[0095] It needs to be further explained that the temperature control lag degree combined with the temperature change non-smoothness can quantify the system response performance in the monitoring period, that is, if the temperature change is too violent, it will lead to a delayed system response and increase the control difficulty; in the forging process of automobile parts, if the processing temperature shows a trend of exceeding the forging temperature range, the temperature control system can take measures in advance, such as starting the cooling equipment or adjusting the heating power; take action before the problem occurs to avoid excessive high or low temperature, further reduce the temperature distribution unevenness in the forging process, so that the temperature in the processing process can be kept stable.

[0096] Preferably, in an embodiment of the present application, the response coefficient of the forging processing temperature control system is determined combined with the temperature change non-smoothness, and the temperature control system of the forging processing process is feedback adjusted by the PID controller, including the specific method:

[0097] The temperature change non-smoothness and the temperature control lag degree of all monitoring periods of the forging processing process in which the current monitoring period is located are linearly normalized to obtain the temperature change non-smoothness factor and the temperature control lag factor of each monitoring period; the product of the temperature change non-smoothness factor and the temperature control lag factor of the current monitoring period is taken as the response coefficient of the forging processing temperature control system in the current monitoring period.

[0098] It needs to be explained that the greater the temperature change non-smoothness of the current monitoring period indicates that the temperature change is more violent, and the greater the temperature control lag degree indicates that the control delay is greater, so a greater response coefficient is needed to show stronger response ability, that is, the greater the adjustment degree of the proportional gain of the PID controller.

[0099] Further, the initial PID parameters are obtained, the product of the response coefficient of the current monitoring period and the proportional gain coefficient (Kp) in the PID parameters is taken as the adjusted proportional gain coefficient, and is re-input into the PID control chip; the actual control signal is output by the PID controller to feedback the temperature control system, and the power of the heater or the working state of the cooling system in the forging processing process is adjusted to realize temperature adjustment; the part surface temperature data is monitored in real time, and the response coefficient is obtained in real time according to the above method to dynamically update the parameters of the PID controller, so as to feedback adjust the temperature control system of the forging processing process.

[0100] Therefore, based on the hysteresis performance of the temperature control system for temperature adjustment, the response requirement of the temperature control system is obtained in combination with the non-smoothness of temperature change, so as to output the adjusted PID parameters for feedback adjustment of the temperature control system, to ensure the temperature stability of the parts in the forging process, reduce the interference of human factors on the temperature adjustment of the forging process, and improve the stability and production efficiency in the forging process of the new energy automobile parts.

[0101] Please refer to Figure 3 which shows a structure block diagram of a forging processing method of a new energy automobile part provided by another embodiment of the application, and the method comprises the following modules:

[0102] The forging processing data acquisition module acquires the part surface temperature data, machine tool pressure data, part stress data and environmental wind speed data at several time points in the part forging process.

[0103] The forging processing data analysis module acquires several monitoring time periods, analyzes the falling trend of the part surface temperature data, determines the part forging temperature loss rate at each monitoring time period in combination with the environmental wind speed data and the difference between the part surface temperature data and the forging temperature range, and performs similarity analysis on the stress data and machine tool pressure data at each position on the part surface in the forging process, to obtain the processing stress of each position on the part surface at each monitoring time period in combination with the overall numerical performance of the machine tool pressure data.

[0104] Based on the part forging temperature loss rate at the monitoring time period and the processing stress difference between different positions on the part surface, the processing temperature distribution non-uniformity at each monitoring time period is obtained; based on the difference between the temperature performance before the temperature adjustment operation and the forging temperature range at the monitoring time period, the temperature change non-smoothness at each monitoring time period is obtained in combination with the processing temperature distribution non-uniformity.

[0105] Based on the time difference between the predicted value of the part surface temperature data and the actual value exceeding the forging temperature range at the monitoring time period, the temperature control hysteresis degree at each monitoring time period is quantified; in combination with the temperature change non-smoothness, the response coefficient of the forging processing temperature control system is determined.

[0106] The forging processing temperature control module performs feedback adjustment on the temperature control system of the forging process through the PID controller.

[0107] The above only describes the preferred embodiments of the application and is not intended to limit the application, and any modification, equivalent replacement, improvement, etc. made within the principles of the application shall be included in the protection scope of the application.

Claims

1. A method for controlling the forging processing temperature of a new energy vehicle part, characterized in that, The method comprises the following steps: Obtain the part surface temperature data, machine tool pressure data, part stress data and environmental wind speed data at several time points in the part forging process; Obtain several monitoring periods; analyze the downward trend of the part surface temperature data, combine the environmental wind speed data and the difference between the part surface temperature data and the forging temperature range to determine the part forging temperature loss rate in each monitoring period; perform similarity analysis on the stress data of each position on the part surface and the machine tool pressure data during the forging process, and combine the overall numerical value of the machine tool pressure data to obtain the processing stress of each position on the part surface in each monitoring period; Based on the part forging temperature loss rate in the monitoring period and the processing stress difference between different positions on the part surface, obtain the processing temperature distribution unevenness in each monitoring period; based on the difference between the temperature performance before the temperature adjustment operation and the forging temperature range in the monitoring period, combine the processing temperature distribution unevenness to obtain the temperature change non-smoothness in each monitoring period; Based on the difference between the predicted value of the part surface temperature data in the monitoring period and the actual time exceeding the forging temperature range, quantify the temperature control lag degree in each monitoring period; combine the temperature change non-smoothness to determine the response coefficient of the forging processing temperature control system; The specific method for obtaining the part forging temperature loss rate in each monitoring period is as follows: Obtain the forging temperature range; obtain the absolute value of the difference between the part surface temperature data at each time point in any monitoring period and the lower limit of the forging temperature range, and obtain the average value of the absolute value of the difference at all time points in the monitoring period as the forging temperature lower limit deviation degree in the monitoring period; Obtain the ratio of the average value of the environmental wind speed data at all time points in the monitoring period to the forging temperature lower limit deviation degree, and combine the processing temperature drop rate in the monitoring period to obtain the part forging temperature loss rate in the monitoring period, which is positively correlated with the ratio and the processing temperature drop rate; The specific method for obtaining the temperature change non-smoothness in each monitoring period is as follows: For any monitoring period, record the time points when the temperature adjustment operation occurs in the monitoring period, denoted as several temperature adjustment time points, and record the part surface temperature data at the adjacent previous time point of each temperature adjustment time point, denoted as the adjustment temperature data before each temperature adjustment time point; According to whether the adjustment temperature data exceeds the upper limit or lower limit of the forging temperature range, obtain the temperature deviation degree of each temperature adjustment time point; The product of the average value of the temperature deviation degree of all temperature adjustment time points and the processing temperature distribution unevenness in the monitoring period is taken as the temperature change non-smoothness in the monitoring period; The specific method for obtaining the response coefficient of the forging processing temperature control system is as follows: Based on the temperature change non-smoothness and the temperature control lag degree of each monitoring period in the current monitoring period, perform normalization processing respectively to obtain the temperature change non-smoothness factor and the temperature control lag factor of each monitoring period; The product of the temperature variation non-smooth factor of the current monitoring period and the temperature control lag factor is taken as the response coefficient of the forging processing temperature control system in the current monitoring period.

2. The forging processing temperature regulation method of a new energy vehicle part according to claim 1, characterized in that, The method for analyzing the decreasing trend of the surface temperature data of the parts includes the following specific steps: For the surface temperature data of the parts at any time in any monitoring period, the difference between the surface temperature data at any time and the surface temperature data of the parts at the adjacent previous time is obtained, and the ratio of the difference to the time interval between the adjacent times is taken as the surface temperature variation slope at the time; A plurality of times at which the surface temperature variation slope is negative in the monitoring period are obtained as a plurality of decreasing times, and the product of the proportion of the number of the decreasing times in all times in the monitoring period and the absolute value average of the surface temperature variation slope of all the decreasing times in the monitoring period is taken as the processing temperature decreasing rate of the monitoring period.

3. The forging process temperature control method of new energy vehicle parts according to claim 1, characterized in that, The method for obtaining the processing stress of the parts at each position on the surface in each monitoring period includes the following specific steps: The machine tool pressure data are numerically processed to obtain pressure normalized values at each time, and based on the machine tool pressure data at each time and the part stress data at any position in any monitoring period, a pressure fitting curve of the monitoring period and a stress fitting curve of the position are obtained; The Pearson correlation coefficient is obtained by comparing the pressure fitting curve with the stress fitting curve, and the product of the Pearson correlation coefficient and the average of the pressure normalized values at all times in the monitoring period is taken as the processing stress of the parts at the position on the surface in the monitoring period.

4. The forging process temperature control method of new energy vehicle parts according to claim 1, characterized in that, The method for obtaining the non-uniformity of the processing temperature distribution in each monitoring period includes the following specific steps: For any monitoring period, the absolute value of the difference between the processing stresses of the parts at any two positions in the monitoring period is obtained, and the average of the absolute values of all the differences in the processing stresses is taken as the stress difference of the monitoring period; in combination with the forging temperature loss rate of the parts in the monitoring period, the non-uniformity of the processing temperature distribution in the monitoring period is obtained, and the non-uniformity of the processing temperature distribution is positively correlated with the stress difference and the forging temperature loss rate of the parts.

5. The forging processing temperature regulation method of new energy vehicle parts according to claim 1, characterized in that, The method for quantifying the temperature control lag degree in each monitoring period includes the following specific steps: For each time in each monitoring period in the same forging processing process, the processing predicted temperature data at each time are obtained by Kalman filtering based on the multi-dimensional data at each time; In any monitoring period, the time at which the surface temperature data of the first part exceeds the forging temperature range is recorded as a processing temperature deviation time; after the processing temperature deviation time, the system restores the surface temperature data of the parts to the forging temperature range through temperature adjustment, and after the restoration is completed, the next surface temperature data of the parts exceeding the forging temperature range is obtained, which is recorded as a processing temperature deviation time, and so on, and a plurality of processing temperature deviation times in the monitoring period are obtained; The method for obtaining the non-uniformity of the processing temperature distribution in each monitoring period includes the following specific steps: For any monitoring period, the absolute value of the difference between the processing stresses of the parts at any two positions in the monitoring period is obtained, and the average of the absolute values of all the differences in the processing stresses is taken as the stress difference of the monitoring period; in combination with the forging temperature loss rate of the parts in the monitoring period, the non-uniformity of the processing temperature distribution in the monitoring period is obtained, and the non-uniformity of the processing temperature distribution is positively correlated with the stress difference and the forging temperature loss rate of the parts. The method for quantifying the temperature control lag degree in each monitoring period includes the following specific steps: For each time in each monitoring period in the same forging processing process, the processing predicted temperature data at each time are obtained by Kalman filtering based on the multi-dimensional data at each time; In any monitoring period, the time at which the surface temperature data of the first part exceeds the forging temperature range is recorded as a processing temperature deviation time; after the processing temperature deviation time, the system restores the surface temperature data of the parts to the forging temperature range through temperature adjustment, and after the restoration is completed, the next surface temperature data of the parts exceeding the forging temperature range is obtained, which is recorded as a processing temperature deviation time, and so on, and a plurality of processing temperature deviation times in the monitoring period are obtained; Based on the processing prediction temperature data at each time in the monitoring period, a corresponding processing temperature deviation time is obtained, denoted as a plurality of prediction temperature deviation times; the prediction temperature deviation time and the processing temperature deviation time are matched according to the order value, and the processing temperature deviation time and the prediction temperature deviation time with the same order value are taken as a group; The difference obtained by subtracting the prediction temperature deviation time from the processing temperature deviation time of any group in the monitoring period is taken as the temperature control lag length of the group, and the mean value of the temperature control lag length of all groups is taken as the temperature control lag degree of the monitoring period.

6. The forging processing temperature regulation method of a new energy vehicle part according to claim 5, characterized in that, The specific method for obtaining the processing prediction temperature data at each time includes: For any monitoring period, the part surface temperature data, machine tool pressure data, environmental wind speed data and part stress data at each position in the forging process of the monitoring period are obtained, and the multi-dimensional data at the same time constitute a state vector at the corresponding time; The state vectors of all times before any time in the monitoring period are input into the Kalman filter, and the prediction state vector at the time is output, and then the part surface prediction temperature data at the time is obtained as the processing prediction temperature data at the time; the processing prediction temperature data at each time in the monitoring period is obtained.

7. A method for forging a new energy automobile part, characterized by, The steps of the forging processing temperature control method of the new energy automobile part are realized, and the method includes: A forging processing data acquisition module is configured to acquire part surface temperature data, machine tool pressure data, part stress data and environmental wind speed data at a plurality of times in a part forging process; A forging processing data analysis module is configured to acquire a plurality of monitoring periods, analyze the decreasing trend of the part surface temperature data, determine the part forging temperature loss rate of each monitoring period in combination with the difference between the environmental wind speed data and the part surface temperature data and the forging temperature range, perform similarity analysis on the stress data of each position on the part surface and the machine tool pressure data in the forging process, and obtain the processing stress of each position on the part surface in each monitoring period in combination with the overall numerical value of the machine tool pressure data; Based on the part forging temperature loss rate of the monitoring period and the processing stress difference between different positions on the part surface, the processing temperature distribution non-uniformity of each monitoring period is obtained; based on the difference between the temperature performance before the temperature adjustment operation in the monitoring period and the forging temperature range, and in combination with the processing temperature distribution non-uniformity, the temperature change non-smoothness of each monitoring period is obtained; Based on the difference between the prediction value of the part surface temperature data in the monitoring period and the actual time exceeding the forging temperature range, the temperature control lag degree of each monitoring period is quantified; in combination with the temperature change non-smoothness, the response coefficient of the forging processing temperature control system is determined; A forging processing temperature control module is configured to feedback adjust the temperature control system of the forging process through a PID controller.

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