Lithium battery shell welding control method and welding clamp

Abstract

This application relates to the field of welding control technology, specifically to a welding control method and welding fixture for lithium battery casings. The method includes: real-time acquisition of laser reflection intensity data, absorbance data of each metal element at its preset wavelength, and electron temperature data of the plasma during the welding process of the lithium battery casing; presetting the adjustment period of the proportional term parameter of the PID controller used to control the wire feed speed during the welding process, and obtaining the laser reflection change value within each period; obtaining the absorption distribution coefficient, peak significance value, and absorption anomaly value within each period; obtaining the temperature fluctuation coefficient within each period based on the non-uniformity of the electron temperature data distribution; obtaining the welding anomaly value within each period, and adjusting the proportional term parameter of the PID controller for the next period. This application aims to improve welding quality by increasing the response speed of welding control and improving the stability of the welding process.

Description

Technical Field

[0001] This application relates to the field of welding control technology, specifically to a welding control method and welding fixture for lithium battery casings. Background Technology

[0002] In the production of lithium batteries, the welding process is closely related to the battery's sealing performance. A tight seal ensures that the electrolyte and other chemical substances inside the battery do not leak, and also prevents external moisture and impurities from entering the battery, thus guaranteeing its normal operation and lifespan. Therefore, the welding quality of the lithium battery casing directly affects the battery's safety, reliability, and performance.

[0003] Laser welding is commonly used for welding lithium battery casings due to its advantages such as low heat input, concentrated energy, and minimal post-weld deformation. However, in actual production, factors such as the stability of the laser beam performance and mismatch in filament spacing can still lead to improper droplet transfer, thus affecting the weld quality. Existing technologies do not fully consider the impact of these factors, resulting in problems with suboptimal weld quality. Summary of the Invention

[0004] In view of the above, it is necessary to provide a welding control method and welding fixture for lithium battery casings. Compared with traditional lithium battery casing welding control methods, this method improves welding quality by increasing the response speed of welding control and enhancing the stability of the welding process. In a first aspect, embodiments of this application provide a method for controlling the welding of a lithium battery casing, the method comprising the following steps: Real-time acquisition of laser reflection intensity data, absorbance data of each metal element at its preset wavelength, and electron temperature data of plasma during the welding process of lithium battery casing; The adjustment period of the proportional term parameter of the PID controller used to control the wire feed speed during the welding process is preset. The laser reflection change value in each cycle is obtained by the difference between the laser reflection intensity data and its nonlinear fitting value in each cycle, as well as the rate of change of the laser reflection intensity data in each cycle. The absorption distribution coefficient in each cycle is obtained by the distribution of the absorbance data of each metal element at its preset wavelength in each cycle. Combined with the peak characteristics of the absorbance data of each metal element at its preset wavelength in each cycle, the peak significance value in each cycle is obtained, and the absorption anomaly value in each cycle is obtained. The temperature fluctuation coefficient in each cycle is obtained by the non-uniformity of the electronic temperature data distribution in each cycle. By analyzing the correlation between changes in laser reflection, abnormal light absorption, and temperature fluctuation coefficients, welding anomalies in each cycle are obtained, and the proportional parameters of the PID controller in the next cycle are adjusted accordingly.

[0005] In one embodiment, the process of obtaining the laser reflection change value is as follows: The goodness of fit when performing nonlinear fitting on laser reflection intensity data within each period; Obtain the time-series fitting curve of the laser reflection intensity data in each period, and calculate the absolute value of the slope of the fitting curve at each acquisition time in each period; obtain the laser reflection change value in each period by using the goodness of fit and the absolute value in each period.

[0006] In one embodiment, the process of obtaining the laser reflection change value is as follows: Calculate the mean of all the absolute values ​​within each period; The change in laser reflection is directly proportional to the mean value and inversely proportional to the goodness of fit.

[0007] In one embodiment, the process of obtaining the light absorption distribution coefficient is as follows: Calculate the arithmetic mean of all absorbance data of each metal element at its preset wavelength within each period, wherein the arithmetic mean of the metal element iron is denoted as the first mean. Calculate the sum of the arithmetic means of all metallic elements; The light absorption distribution coefficient is directly proportional to the first average and inversely proportional to the sum.

[0008] In one embodiment, the process of obtaining the peak significance value is as follows: Obtain the peak values ​​of all absorbance data of iron at its preset wavelength in each period over time; calculate the average value of all absorbance data of iron at its preset wavelength in each period, excluding the peak values. Calculate the ratio of each peak value to the average value within each period; The peak significance value is positively correlated with all the ratios in each period.

[0009] In one embodiment, the absorption anomaly value is the product of the absorption distribution coefficient and the peak significance value.

[0010] In one embodiment, the process of obtaining the temperature fluctuation coefficient is as follows: Obtain the difference between the maximum and minimum values ​​of the electron temperature data in each cycle; The average value of the electronic temperature data within each period is denoted as the temperature mean. The temperature fluctuation coefficient is the ratio of the difference value to the average temperature value.

[0011] In one embodiment, the process of obtaining the welding anomaly value is as follows: The laser reflection change value, light absorption anomaly value and temperature fluctuation coefficient are recorded as various welding evaluation coefficients. The mean of the correlation coefficients between any two welding evaluation coefficients in time series in each cycle and all previous cycles is recorded as the correlation mean. The average values ​​of laser reflection variation, absorption anomaly, and temperature fluctuation coefficient within each period are recorded as the comprehensive average. The welding anomalies are positively correlated with the relevant mean and the comprehensive mean, respectively.

[0012] In one embodiment, adjusting the proportional term parameter of the PID controller in the next cycle of each cycle includes: Calculate the normalized value of the welding anomaly value in each period and the sum of the values ​​of 1; The product of the proportional term parameter value of the PID controller in each cycle and the accumulated value is used as the value of the proportional term parameter of the PID controller in the next cycle.

[0013] Secondly, embodiments of this application also provide a lithium battery casing welding fixture, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described lithium battery casing welding control methods.

[0014] This application has at least the following beneficial effects: This application dynamically adjusts the proportional term parameter of the PID controller by setting the adjustment period, thereby optimizing the wire feed speed and ensuring the stability and quality of the welding process. By comparing laser reflection intensity data with its nonlinear fitting value, the abrupt changes in the laser reflection intensity data can be detected. Furthermore, by combining the rate of change of the laser reflection intensity data, the short-term abrupt changes and rapid change characteristics of the laser reflection intensity data can be quantified. By monitoring the absorbance data of specific metal elements, the absorption distribution coefficient can be calculated, reflecting the excitation state of these elements during the welding process, thus assessing the welding quality. Moreover, by identifying peak characteristics in the absorbance data, potential instability during the welding process can be monitored, enabling a more comprehensive assessment of absorbance data anomalies and providing important evidence for welding quality evaluation. By analyzing the non-uniformity of electronic temperature data, the temperature stability during the welding process can be evaluated, allowing for timely detection of temperature fluctuations and anomalies. Finally, by combining the synchronicity of changes in laser reflection intensity data, absorbance data, and electronic temperature data, as well as the overall anomaly state, multi-feature fusion is used to evaluate welding quality, reducing misjudgments caused by interference from single data points and improving the accuracy of welding quality evaluation. Furthermore, by dynamically adjusting the proportional term parameters of the PID controller based on the welding quality assessment results, it can quickly respond to changes in the welding process, improve the response speed of welding control, reduce the impact of laser beam performance stability and filament spacing mismatch, thereby improving the stability of the welding process and enhancing welding quality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a lithium battery casing welding control method according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the process of obtaining welding anomaly values; Figure 3 This is a schematic diagram illustrating the calculation process for welding anomalies. Detailed Implementation

[0017] In the description of the embodiments of this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0019] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a lithium battery casing welding control method and welding fixture provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a lithium battery casing welding control method according to an embodiment of this application, the method comprising the following steps: Step 1: Real-time acquisition of laser reflection intensity data, absorbance data of each metal element at its preset wavelength, and electron temperature data of plasma during the welding process of lithium battery casing.

[0022] In the laser welding process of lithium battery casings, the laser beam interacts with the material, triggering dramatic physicochemical changes and generating plasma, radiation, and temperature signals. By analyzing these signals and correlating them with the stability of the welding process and welding defects, real-time monitoring of the laser welding process and welding quality can be achieved. Laser reflection intensity refers to the signal where a portion of the laser energy is reflected back from the workpiece surface when the laser beam irradiates it during laser welding. This reflection phenomenon is caused by factors such as the unevenness of the workpiece surface, the optical properties of the material, and the incident angle of the laser beam. Furthermore, during laser welding, the spectral information of specific elements in specific wavelength bands can effectively reflect the welding state; for example, the spectral line intensity of chromium (Cr) at 529 nm and iron (Fe) at 636 nm. Measuring the spectral intensity at these wavelengths using a spectrometer and converting it into absorbance data allows for further analysis of the welding process. Specifically, using the Lambert-Beer law, the spectral line intensities at 529 nm and 636 nm wavelengths at each acquisition moment are converted into absorbance data, yielding the absorbance data of Cr at 529 nm and Fe at 636 nm at each acquisition moment during the welding process. Electron temperature changes drastically when welding defects occur; therefore, monitoring changes in electron temperature is crucial for assessing the stability of the welding process. By comprehensively analyzing the above data, comprehensive monitoring of the laser welding process and effective evaluation of welding quality can be achieved.

[0023] In this embodiment, the acquisition frequency of laser reflection intensity data, absorbance data and electronic temperature data is 100Hz. The acquisition frequency value is preset by the user and the implementer can set it according to the actual situation. This application does not impose any special restrictions.

[0024] Step 2: Preset the adjustment cycle of the proportional parameter of the PID controller used to control the wire feed speed during the welding process, and comprehensively analyze the collected laser reflection intensity data, absorbance data and electronic temperature data to obtain welding anomaly values ​​in each cycle.

[0025] The welding quality of battery casings is easily affected by various factors, such as the stability of the laser beam and whether the filament spacing is matched. Evaluating the welding quality based on real-time monitoring data and then adjusting the welding process in real time is a key way to improve welding control efficiency.

[0026] Step 2.1: Obtain the laser reflection change value in each period by the difference between the laser reflection intensity data and its nonlinear fitting value in each period, and the rate of change of the laser reflection intensity data in each period.

[0027] When the welding quality is good, the laser reflection intensity data is relatively smooth and without obvious fluctuations. However, poor welding will result in an uneven weld surface, causing the reflection intensity data to show obvious sudden drops or increases in the short term with a large rate of change. Therefore, the adjustment period of the proportional term parameter of the PID (Proportional Integral Derivative) controller used to control the wire feed speed during the welding process is preset. Taking the i-th period as an example, the goodness of fit of the laser reflection intensity data in the i-th period is obtained when performing nonlinear fitting. If the sudden drop or increase in the short term is obvious, the difference between the collected laser reflection intensity data and its fitted value will be greater, resulting in a smaller calculated goodness of fit. At the same time, in order to obtain the characteristics of the rate of change of laser reflection intensity data in the short term, the time-series fitting curve of the laser reflection intensity data in the i-th period is obtained, and the absolute value of the slope of the fitting curve in the i-th period at each acquisition time is calculated. The mean of all the absolute values ​​in the i-th period is recorded as the mean slope in the i-th period. The larger the mean slope, the greater the rate of change of laser reflection intensity data in the i-th period during the welding process. The calculation of the slope is a well-known technique and will not be described in detail in this application.

[0028] In this embodiment, the length of the adjustment period is 200ms. The length of the adjustment period is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0029] In this embodiment, the goodness of fit is specifically the coefficient of determination. The calculation of the coefficient of determination is a well-known technique and will not be described in detail in this application. As other implementation methods, based on the ability to measure the degree of difference between the laser reflection intensity data and its fitted value, the implementer may use other existing techniques, such as the reciprocal of the mean square error, etc. This application does not impose any special restrictions.

[0030] In this embodiment, the least squares method is used to obtain the fitting curve of the laser reflection intensity data in time series. The least squares method is a well-known technique and will not be described in detail in this application. As other implementation methods, based on the ability to obtain the fitting curve of the laser reflection intensity data in time series, the implementer may use other existing techniques, such as local weighted regression, K-nearest neighbor regression, etc. This application does not impose any special restrictions.

[0031] Furthermore, the laser reflection change value in the i-th period is obtained by using the goodness of fit and the average slope in the i-th period. Specifically, the laser reflection change value in the i-th period is directly proportional to the average slope in the i-th period and inversely proportional to the goodness of fit in the i-th period.

[0032] In this embodiment, the expression for the change in laser reflection during the i-th period is: In the formula, This represents the change in laser reflection during the i-th period; This represents the average slope during the i-th period; The goodness of fit is expressed in the i-th period; exp() represents an exponential function with the natural constant as the base, used to express the goodness of fit in the i-th period. The mapping is to positive numbers.

[0033] It should be noted that the larger the calculated change value of laser reflection, the more obvious the short-term abrupt change and rapid change characteristics of the laser reflection intensity data in the i-th period.

[0034] Calculate the laser reflection change value in each period according to the calculation method for the laser reflection change value in the i-th period.

[0035] Step 2.2: By analyzing the distribution of absorbance data of each metal element at its preset wavelength within each period, the absorption distribution coefficient within each period is obtained. Combined with the peak characteristics of the absorbance data of each metal element at its preset wavelength within each period, the peak significance value within each period is obtained, and the absorption anomaly value within each period is obtained.

[0036] In the laser welding process of lithium battery casings, due to the small spot diameter, unstable laser beam performance can easily lead to poor weld surface quality. This instability is also reflected in the spectral data, manifesting as fluctuations. The absorbance of Cr and Fe at specific wavelengths is particularly significantly affected by the welding quality. Specifically, if the welding process is good, the spectral intensity of Cr is higher in the time domain, while the spectral intensity of Fe is lower. This is because the molten droplets generated by the welding wire fall stably onto the predetermined weld bead. At this time, Cr atoms with lower ionization energy transition to an excited state under heating, and then spontaneously transition to lower energy levels. Simultaneously, the spontaneously emitted photons of specific wavelengths are precisely captured by the fiber optic probe. Therefore, the spectral intensity of Cr is higher in a good weld, while Fe is less easily excited during this process, resulting in a lower spectral intensity for Fe. However, when the welding is unstable, the molten droplets fail to fall properly onto the predetermined weld bead, causing the detected spectral intensity of Cr to weaken. In this case, Fe, with its higher excitation energy, is more easily excited, leading to an increase in the spectral intensity of Fe. Therefore, by monitoring the changes in absorbance of chromium and iron at specific wavelengths, the quality of laser welding can be effectively evaluated.

[0037] Based on the above analysis, the absorption distribution coefficient for the i-th period is obtained by analyzing the distribution of absorbance data of each metal element at its preset wavelength within the i-th period. Specifically: The arithmetic mean of all absorbance data of Fe at a wavelength of 636nm in the i-th period is denoted as the first mean. The sum of the arithmetic means of Fe and Cr in the i-th period is calculated. The absorbance distribution coefficient in the i-th period is directly proportional to the first mean and inversely proportional to the sum.

[0038] In this embodiment, the ratio of the first average to the sum is used as the light absorption distribution coefficient in the i-th period.

[0039] It should be noted that the larger the calculated absorption distribution coefficient, the more abnormal the absorbance state of Cr and Fe in the i-th period.

[0040] Furthermore, the more unstable the welding process, the more obvious the peak characteristics will appear in the absorbance data of Fe, because the large molten droplets formed by the unstable droplet formation excite Fe.

[0041] Based on the above analysis, the peak significance value within the i-th period is obtained by analyzing the peak characteristics of Fe absorbance data at 636nm wavelength during the i-th period. Specifically: Obtain the peak values ​​of all absorbance data of Fe at a wavelength of 636 nm in the time series within the i-th period; calculate the average value of all absorbance data of Fe at a wavelength of 636 nm within the i-th period except for the peak values; calculate the ratio of each peak value to the average value within the i-th period; the significant value of the peak in the i-th period is positively correlated with all the ratios in the i-th period.

[0042] In this embodiment, the peak values ​​of absorbance data in time series are obtained by an automatic multi-scale peak finding algorithm. The automatic multi-scale peak finding algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to obtain the peak values ​​of absorbance data in time series, implementers may use other existing technologies, such as peak and valley detection algorithms and extreme point detection algorithms. This application does not impose any special restrictions.

[0043] In this embodiment, the sum of all the ratios in the i-th period is taken as the peak significance value in the i-th period.

[0044] It should be noted that the mean of all non-peak absorbance data in the i-th period reflects the overall state of the absorbance data; the larger the calculated peak significance value, the more obvious the peak characteristics in the Fe absorbance data in the i-th period during the welding process.

[0045] Furthermore, by combining the absorption distribution coefficient and the peak significance value in the i-th period, the absorption anomaly value in the i-th period is obtained. Specifically, the product of the absorption distribution coefficient and the peak significance value in the i-th period is taken as the absorption anomaly value in the i-th period.

[0046] It should be noted that the larger the calculated absorption anomaly value, the more abnormal the absorbance data is in the i-th cycle during the welding process, and the more obvious the peak characteristics in the absorbance data of Fe.

[0047] Calculate the absorption anomaly value in each period according to the calculation method for the absorption anomaly value in the i-th period.

[0048] Step 2.3: Obtain the temperature fluctuation coefficient for each cycle by analyzing the uneven distribution of electronic temperature data within each cycle.

[0049] Furthermore, under stable welding quality, the electronic temperature data fluctuates steadily around a certain temperature value. However, if the wire feeding is unstable and causes wire deviation defects, the electronic temperature will fluctuate drastically around that temperature value.

[0050] Based on the above analysis, the temperature fluctuation coefficient within the i-th period is obtained by considering the non-uniformity of the electron temperature data distribution within the i-th period, specifically as follows: Obtain the difference between the maximum and minimum values ​​of the electronic temperature data in the i-th period; denot the mean of the electronic temperature data in the i-th period as the temperature mean; the temperature fluctuation coefficient in the i-th period is the ratio of the difference value to the temperature mean.

[0051] In this embodiment, the difference between the maximum and minimum values ​​is the absolute value of the difference.

[0052] It should be noted that the difference between the maximum and minimum values ​​reflects the degree of fluctuation in the electronic temperature data. Under normal circumstances, the average temperature is relatively stable. Therefore, the ratio of the difference to the average temperature can reflect the degree of fluctuation in the electronic temperature data near the steady-state temperature value. The larger the calculated temperature fluctuation coefficient, the greater the degree of fluctuation in the electronic temperature data near the steady-state temperature value.

[0053] Calculate the temperature fluctuation coefficient for each cycle according to the calculation method for the temperature fluctuation coefficient in the i-th cycle.

[0054] Step 2.4: Obtain welding anomaly values ​​for each cycle by analyzing the correlation between laser reflection change values, light absorption anomaly values, and temperature fluctuation coefficients.

[0055] Laser welding is a complex physicochemical process involving multiple phases and fields, generating a wealth of associated information, such as reflected light and temperature. Evaluating weld quality based on only a single feature is susceptible to interference from noise or operational drift, leading to reduced monitoring accuracy. For example, fluctuations in shielding gas flow during welding can cause a decrease in spectral intensity, and environmental thermal radiation interference can affect the accuracy of electronic temperature data, resulting in misjudgments. Therefore, this application employs multi-feature fusion to evaluate weld quality, facilitating cross-validation and significantly improving the accuracy and stability of quality assessment.

[0056] If the characteristic changes of laser reflection intensity data, absorbance data and electron temperature data are more synchronized over multiple cycles, and the overall unstable welding characteristics reflected are higher, then the possibility of welding quality problems is greater.

[0057] Based on the above analysis, the welding anomaly value in the i-th cycle is obtained by correlating the changes in laser reflection, the anomaly value in light absorption, and the temperature fluctuation coefficient in the i-th cycle and all previous cycles. Specifically: The laser reflection variation value, absorption anomaly value, and temperature fluctuation coefficient are denoted as various welding evaluation coefficients. The temporal correlation coefficient between any two welding evaluation coefficients in the i-th period and all preceding periods is calculated. The mean of the temporal correlation coefficients between all any two welding evaluation coefficients in the i-th period and all preceding periods is denoted as the correlation mean. The correlation mean reflects the synchronous characteristics of the changes in welding quality characteristics corresponding to the laser reflection intensity data, absorbance data, and electronic temperature data. The larger the correlation mean, the higher the degree of synchronization of the changes in welding quality characteristics corresponding to the laser reflection intensity data, absorbance data, and electronic temperature data. The mean of the laser reflection variation value, absorption anomaly value, and temperature fluctuation coefficient in the i-th period is denoted as the comprehensive mean. The welding anomaly value in the i-th period is positively correlated with the correlation mean and the comprehensive mean, respectively.

[0058] In this embodiment, the correlation coefficient is the Pearson correlation coefficient, which is a well-known technology and will not be described in detail here. As other implementation methods, based on the ability to measure the temporal correlation between welding evaluation coefficients, implementers may use other existing technologies, such as the Spearman correlation coefficient, etc. This application does not impose any special restrictions.

[0059] In this embodiment, the product of the relevant mean and the comprehensive mean is used as the welding anomaly value in the i-th period.

[0060] It should be noted that the larger the calculated welding anomaly value, the worse the welding quality within the i-th cycle. A schematic diagram of the welding anomaly value acquisition process is shown below. Figure 2 As shown in the diagram. The calculation process for welding anomalies is illustrated below. Figure 3 As shown.

[0061] Step 3: Based on the welding anomalies in each cycle, adjust the proportional term parameter of the PID controller for the next cycle.

[0062] Furthermore, by deeply analyzing the changes in laser reflection intensity, absorbance data, and electron temperature data during the battery casing welding process, and combining the synchronicity of these changes with overall abnormal conditions, the welding quality is evaluated in real time. Based on the evaluation results, the welding process is dynamically adjusted. During the wire welding process, mismatched wire spacing can easily affect weld formation quality. This application adjusts the wire feed speed based on the welding quality evaluation results. A larger welding anomaly value in the i-th cycle indicates that the wire feed speed in the i-th cycle is unfavorable for good weld formation, requiring an increase in the proportional term parameter of the PID controller to quickly adjust the wire feed speed to approach the optimal wire spacing. Conversely, a lower proportional term parameter reduces overshoot and improves the stability of welding control.

[0063] Based on the above analysis, the proportional term parameter of the PID controller in the (i+1)th cycle is adjusted using the welding anomaly value within the i-th cycle, as expressed in the following expression: In the formula, , These represent the proportional parameters of the PID controller in the (i+1)th and i-th cycles, respectively. This represents the normalized value of the welding anomaly value within the i-th period.

[0064] In this embodiment, the Z-Score normalization method is used to obtain the normalized value of the welding anomaly. The Z-Score normalization method is a well-known technology and will not be described in detail in this application. The proportional term parameter of the PID controller is adjusted starting from the third cycle.

[0065] In this embodiment, the initial value of the proportional term parameter of the PID controller is obtained by trial and error. Trial and error is a well-known technique and will not be described in detail here.

[0066] Based on the same inventive concept as the above method, this application embodiment also provides a lithium battery casing welding fixture, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described lithium battery casing welding control methods.

[0067] In summary, this application, by setting the adjustment period of the proportional term parameter of the PID controller, can dynamically adjust the proportional term parameter of the PID controller, thereby optimizing the wire feed speed and ensuring the stability and quality of the welding process. By comparing the laser reflection intensity data with its nonlinear fitting value, the degree of abrupt changes in the laser reflection intensity data can be detected. Furthermore, by combining the rate of change of the laser reflection intensity data, the short-term abrupt changes and rapid change characteristics of the laser reflection intensity data can be quantified. By monitoring the absorbance data of specific metal elements, the absorbance distribution coefficient can be calculated, reflecting the excitation state of these elements during the welding process, thus evaluating the welding quality. Moreover, by identifying the peak characteristics in the absorbance data, potential instability during the welding process can be monitored, enabling a more comprehensive assessment of absorbance data anomalies and providing important evidence for welding quality evaluation. By analyzing the non-uniformity of the electronic temperature data, the temperature stability during the welding process can be evaluated, allowing for timely detection of temperature fluctuations and anomalies. Finally, by combining the synchronicity of changes in laser reflection intensity data, absorbance data, and electronic temperature data, as well as the overall anomaly state, multi-feature fusion is used to evaluate welding quality, reducing misjudgments caused by interference from single data points and improving the accuracy of welding quality evaluation. Furthermore, by dynamically adjusting the proportional term parameters of the PID controller based on the welding quality assessment results, it can quickly respond to changes in the welding process, improve the response speed of welding control, reduce the impact of laser beam performance stability and filament spacing mismatch, thereby improving the stability of the welding process and enhancing welding quality.

[0068] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0069] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A method for controlling the welding of a lithium battery casing, characterized in that, The method includes the following steps: Real-time acquisition of laser reflection intensity data, absorbance data of each metal element at its preset wavelength, and electron temperature data of plasma during the welding process of lithium battery casing; The adjustment period of the proportional term parameter of the PID controller used to control the wire feed speed during the welding process is preset. The laser reflection change value in each cycle is obtained by the difference between the laser reflection intensity data and its nonlinear fitting value in each cycle, as well as the rate of change of the laser reflection intensity data in each cycle. The absorption distribution coefficient in each cycle is obtained by the distribution of the absorbance data of each metal element at its preset wavelength in each cycle. Combined with the peak characteristics of the absorbance data of each metal element at its preset wavelength in each cycle, the peak significance value in each cycle is obtained, and the absorption anomaly value in each cycle is obtained. The temperature fluctuation coefficient in each cycle is obtained by the non-uniformity of the electronic temperature data distribution in each cycle. By analyzing the correlation between changes in laser reflection, abnormal light absorption, and temperature fluctuation coefficients, welding anomalies in each cycle are obtained, and the proportional parameters of the PID controller in the next cycle are adjusted accordingly.

2. The lithium battery casing welding control method as described in claim 1, characterized in that, The process for obtaining the laser reflection change value is as follows: The goodness of fit when performing nonlinear fitting on laser reflection intensity data within each period; Obtain the time-series fitting curve of the laser reflection intensity data in each period, and calculate the absolute value of the slope of the fitting curve at each acquisition time in each period; obtain the laser reflection change value in each period by using the goodness of fit and the absolute value in each period.

3. The lithium battery casing welding control method as described in claim 2, characterized in that, The process for obtaining the laser reflection change value is as follows: Calculate the mean of all the absolute values ​​within each period; The change in laser reflection is directly proportional to the mean value and inversely proportional to the goodness of fit.

4. The lithium battery casing welding control method as described in claim 1, characterized in that, The process of obtaining the light absorption distribution coefficient is as follows: Calculate the arithmetic mean of all absorbance data of each metal element at its preset wavelength within each period, wherein the arithmetic mean of the metal element iron is denoted as the first mean. Calculate the sum of the arithmetic means of all metallic elements; The light absorption distribution coefficient is directly proportional to the first average and inversely proportional to the sum.

5. The lithium battery casing welding control method as described in claim 4, characterized in that, The process for obtaining the peak significance value is as follows: Obtain the peak values ​​of all absorbance data of iron at its preset wavelength in each period over time; calculate the average value of all absorbance data of iron at its preset wavelength in each period, excluding the peak values. Calculate the ratio of each peak value to the average value within each period; The peak significance value is positively correlated with all the ratios in each period.

6. The lithium battery casing welding control method as described in claim 1, characterized in that, The absorption anomaly value is the product of the absorption distribution coefficient and the peak significance value.

7. The lithium battery casing welding control method as described in claim 1, characterized in that, The process for obtaining the temperature fluctuation coefficient is as follows: Obtain the difference between the maximum and minimum values ​​of the electron temperature data in each cycle; The average value of the electronic temperature data within each period is denoted as the temperature mean. The temperature fluctuation coefficient is the ratio of the difference value to the average temperature value.

8. The lithium battery casing welding control method as described in claim 1, characterized in that, The process for obtaining the welding anomaly value is as follows: The laser reflection change value, light absorption anomaly value and temperature fluctuation coefficient are recorded as various welding evaluation coefficients. The mean of the correlation coefficients between any two welding evaluation coefficients in time series in each cycle and all previous cycles is recorded as the correlation mean. The average values ​​of laser reflection variation, absorption anomaly, and temperature fluctuation coefficient within each period are recorded as the comprehensive average. The welding anomalies are positively correlated with the relevant mean and the comprehensive mean, respectively.

9. The lithium battery casing welding control method as described in claim 1, characterized in that, The adjustment of the proportional term parameter of the PID controller in the next cycle of each cycle includes: Calculate the normalized value of the welding anomaly value in each period and the sum of the values ​​of 1; The product of the proportional term parameter value of the PID controller in each cycle and the accumulated value is used as the value of the proportional term parameter of the PID controller in the next cycle.

10. A lithium battery casing welding fixture, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the lithium battery casing welding control method as described in any one of claims 1-9.

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