A real-time weld seam trajectory positioning method

Abstract

The application discloses a kind of weld seam trajectory real-time positioning method, belong to laser positioning technical field, comprising: based on ambient light intensity and the temperature of structured light emitter obtains environmental state coefficient;Based on welding dust particle concentration and arc intensity obtains welding state coefficient;Based on the welding state coefficient under current structured light emitter vibration amplitude and environmental state coefficient obtains environmental stability coefficient;Based on laser wavelength, line width and structured light emitter power obtains structured light stability coefficient;Based on environmental stability coefficient and the laser working distance under structured light stability coefficient and workpiece surface reflectivity obtains distance-reflectivity matching degree;Based on distance-reflectivity matching degree and current laser divergence angle constructs divergence angle optimization model and outputs target laser divergence angle;The method is realized through multiple model cooperation weld seam trajectory high-precision real-time positioning in strong interference environment, significantly improve the robustness and adaptability of welding automation system.

Description

Technical Field

[0001] This invention belongs to the field of laser positioning technology, and in particular relates to a method for real-time positioning of weld seam trajectory. Background Technology

[0002] In the field of welding automation, traditional structured light vision positioning technology faces the challenge of multi-physics coupling interference. Factors such as strong arc light, dust particles, equipment vibration, and changes in ambient light generated during the welding process can cause problems such as positioning offset and light spot distortion in traditional single-parameter control methods. Existing technologies typically use fixed parameter modes, which cannot dynamically adapt to complex working conditions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a real-time weld trajectory positioning method, which solves the aforementioned problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a real-time positioning method for weld seam trajectories, comprising the following steps:

[0005] An environmental state model is constructed based on ambient light intensity and structured light emitter temperature, and the environmental state coefficients are output.

[0006] A welding condition model is constructed based on the concentration of welding fume particles and the intensity of arc light, and the welding condition coefficient is output.

[0007] An environmental stability model is constructed based on the welding state coefficient and environmental state coefficient under the current vibration amplitude of the structured light emitter, and the environmental stability coefficient is output.

[0008] A structured light stability model is constructed based on laser wavelength, linewidth, and structured light emitter power, and the structured light stability coefficient is output.

[0009] Based on the environmental stability coefficient and the structured light stability coefficient, a distance-reflection matching degree model is constructed using the laser working distance and the reflectivity of the workpiece surface to output the distance-reflection matching degree.

[0010] Based on the current distance-reflection matching degree and the current laser divergence angle, a divergence angle optimization model is constructed to output the target laser divergence angle.

[0011] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0012] Further technical solution: The steps for constructing an environmental state model and outputting environmental state coefficients based on ambient light intensity and structured light emitter temperature are as follows:

[0013] The ambient light intensity is obtained by performing maximum-min normalization on the current ambient light intensity.

[0014] The temperature index is obtained by comparing the difference between the current structured light transmitter temperature and the optimal operating temperature of the structured light transmitter with the difference between the maximum allowable temperature and the optimal operating temperature of the structured light transmitter.

[0015] An environmental state model is constructed based on the ambient light intensity index and the temperature index. This environmental state model is expressed as follows:

[0016]

[0017] in, Represents the environmental state coefficient. Indicates the ambient light intensity index. Indicates the temperature index. Represents the weight coefficient and .

[0018] Further technical solution: The steps for constructing a welding state model and outputting welding state coefficients based on welding fume particle concentration and arc intensity are as follows:

[0019] The welding fume particle concentration index is obtained by performing maximum-min normalization on the current welding fume particle concentration.

[0020] Import the current arc intensity into the formula Obtain the arc intensity index. Indicates the reference arc intensity. Indicates the current arc light intensity;

[0021] A welding state model is constructed based on the welding fume particle concentration index and the arc light intensity index. The welding state model is expressed as follows:

[0022]

[0023] in, Indicates the welding condition coefficient. Indicates the smoke and dust attenuation coefficient. This indicates the concentration index of welding fume particles. Indicates the arc intensity index. Represents the weight coefficient and ;

[0024] The welding fume particle concentration index and the current arc light intensity index are imported into the welding condition model to output the welding condition coefficient.

[0025] Further technical solution: The steps for constructing an environmental stability model and outputting the environmental stability coefficient based on the welding state coefficient and environmental state coefficient under the current structured light emitter vibration amplitude are as follows:

[0026] The vibration amplitude index is obtained by comparing the current vibration amplitude of the structured light emitter with the maximum allowable vibration amplitude.

[0027] An environmental stability model is constructed based on the vibration amplitude index, environmental state coefficient, and welding state coefficient. The environmental stability model is expressed as follows:

[0028]

[0029] in, Indicates the environmental stability coefficient. Represents the environmental state coefficient. Indicates the welding condition coefficient. Indicates the vibration amplitude index. Represents the weight coefficient and ;

[0030] The vibration amplitude index, environmental state coefficient, and welding state coefficient are imported into the environmental stability model to output the environmental stability coefficient.

[0031] Further technical solution: The steps for constructing a structured light stability model and outputting the structured light stability coefficient based on laser wavelength, linewidth, and structured light emitter power are as follows:

[0032] The current line width is processed by maximum-min normalization to obtain the line width index;

[0033] The wavelength index is obtained by comparing the absolute value of the difference between the current laser wavelength and the nominal wavelength with the wavelength tolerance (which represents the sensitivity to wavelength shift).

[0034] A structured light stability model is constructed based on the linewidth exponent, wavelength exponent, and current structured light emitter power. This structured light stability model is expressed as follows:

[0035]

[0036] in, This represents the structured light stability coefficient. Indicates the wavelength index. Indicates the line width index. This indicates the current power of the structured light transmitter. This indicates the optimal structured light emitter power. This represents the standard deviation of the structured light emitter power. Represents the weight coefficient and ;

[0037] The linewidth index, wavelength index, and current structured light emitter power are imported into the structured light stability model to output the structured light stability coefficient.

[0038] A further technical solution: The distance-reflection matching degree model is expressed as:

[0039]

[0040] in, Indicates distance-reflection matching degree. Indicates the current laser working distance. Indicates the optimal laser working distance. Indicates the standard deviation of working distance. Indicates the environmental stability coefficient. Indicates the welding stability coefficient. Indicates the reflectivity of the workpiece surface.

[0041] A further technical solution: The divergence angle optimization model is expressed as:

[0042]

[0043] in, Indicates the target divergence angle. Indicates the current divergence angle. Indicates distance-reflection matching degree. Indicates target distance - reflectivity matching degree. This indicates that the gain coefficient is adjusted.

[0044] A further technical solution: the temperature of the structured light emitter is between the minimum allowable temperature and the maximum allowable temperature.

[0045] This invention provides a real-time positioning method for weld seam trajectories, which has the following advantages compared with the prior art:

[0046] 1. This invention, through the dual coupling of the environmental state model (light intensity / temperature) and the welding state model (fumes / arc light), combined with the exponential decay mechanism of vibration influence, enables the system to improve its comprehensive anti-interference capability under strong arc light, high fume and equipment vibration conditions, and effectively suppresses positioning deviation.

[0047] 2. Based on the distance-reflection matching degree, the laser divergence angle is adjusted in real time, so that the spot energy distribution adapts to different working distances and surface reflectivity (such as high reflectivity of stainless steel and low reflectivity of cast iron), improving the stripe signal-to-noise ratio and solving the adaptability defects of traditional fixed parameter modes.

[0048] 3. The structured light stability model (wavelength / linewidth / power) is linked with the environmental stability model, which can compensate for wavelength shift caused by laser temperature drift and optical plane distortion caused by mechanical vibration. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0052] In existing technologies, the field of welding automation has long faced the challenge of multi-physics coupling interference. Traditional structured light vision positioning technology uses fixed parameter modes, which are difficult to cope with the dynamic changes in strong arc light, dust particles, equipment vibration, and ambient light fluctuations during the welding process. For example, in a welding workshop, high temperatures cause the structured light emitter temperature to rise, while the concentration of welding dust particles changes continuously with the welding process. The coupling effect of these factors causes spot distortion and positioning offset, affecting the accuracy of weld trajectory recognition. Existing methods only compensate for single interference factors and cannot achieve synergistic suppression of multi-source interference.

[0053] Please see Figure 1 The present invention provides a real-time positioning method for weld seam trajectory, comprising the following steps:

[0054] An environmental state model is constructed based on ambient light intensity and structured light emitter temperature, and the environmental state coefficients are output.

[0055] A welding condition model is constructed based on the concentration of welding fume particles and the intensity of arc light, and the welding condition coefficient is output.

[0056] An environmental stability model is constructed based on the welding state coefficient and environmental state coefficient under the current vibration amplitude of the structured light emitter, and the environmental stability coefficient is output.

[0057] A structured light stability model is constructed based on laser wavelength, linewidth, and structured light emitter power, and the structured light stability coefficient is output.

[0058] Based on the environmental stability coefficient and the structured light stability coefficient, a distance-reflection matching degree model is constructed using the laser working distance and the reflectivity of the workpiece surface to output the distance-reflection matching degree.

[0059] Based on the distance-reflection matching degree and the current laser divergence angle, a divergence angle optimization model is constructed to output the target laser divergence angle.

[0060] The environmental state model quantifies the combined impact of ambient light intensity and equipment temperature on light source stability, eliminating the superimposed interference of ambient light fluctuations and equipment temperature rise on beam quality. The welding state model uses dynamic correlation analysis of welding fume particle concentration and arc light intensity to suppress the coupling effect of fume shielding and strong light interference during welding. The environmental stability model considers the synergistic effect of vibration amplitude, environmental state, and welding state to assess the combined impact of mechanical vibration and multi-source interference. The structured light stability model evaluates beam quality based on laser physical parameters to ensure the stability of light source output characteristics. The distance-reflection matching degree model combines environmental stability, light source stability, and workpiece surface characteristics to optimize the matching relationship between laser working distance and surface reflection. The divergence angle optimization model dynamically adjusts the beam divergence angle based on the matching degree, specifically calculated using the product of gain coefficient and difference, to correct beam shape in real time.

[0061] Specifically, this method quantifies the combined effects of ambient light intensity and emitter temperature through an environmental state model, eliminating interference from changes in external illumination and equipment temperature rise on the light source; a welding state model calculates the dynamic attenuation effect of welding fume particle concentration and arc light intensity in real time, reducing optical interference generated during the welding process; an environmental stability model integrates the synergistic effects of vibration amplitude, environment, and welding state to assess the combined effects of mechanical vibration and multi-source interference; a structured light stability model analyzes the physical characteristic deviations of laser wavelength, linewidth, and power to ensure the stability of light source output parameters; a distance-reflection matching degree model integrates environmental stability, light source stability, and workpiece surface characteristics to calculate the matching degree between the optimal working distance and reflection parameters; and a divergence angle optimization model adjusts the laser divergence angle based on the real-time matching degree, forming a closed-loop control mechanism to ultimately achieve dynamic optimization of the spot shape.

[0062] Compared to existing technologies, traditional methods compensate for single interference factors by fixing thresholds, such as adjusting laser power only to deal with arc interference or optimizing working distance only to compensate for the impact of smoke and dust. This solution establishes a dynamic correlation model of multiple physics fields, incorporating ambient light, temperature, smoke and dust, vibration, light source parameters, and workpiece characteristics into a unified calculation framework to achieve synergistic suppression of multi-source interference.

[0063] Through the above technical solution, this application effectively solves the problems of beam distortion and positioning offset caused by multi-physics field coupling interference during welding. By using collaborative calculations of multi-dimensional models, the system can perceive changes in environmental conditions, welding interference, and light source parameters in real time, dynamically adjusting the laser divergence angle to improve the quality of structured light imaging. In welding scenarios with strong arc light and high concentrations of welding fume particles, the system can automatically suppress light intensity attenuation and scattering effects, ensuring the accuracy of weld trajectory recognition. Under conditions of equipment vibration and temperature fluctuations, the system maintains the reliability of light source parameters through comprehensive evaluation using a stability model, avoiding positioning inaccuracies caused by insufficient compensation for a single interference in traditional methods.

[0064] Preferably, the steps for constructing an environmental state model based on ambient light intensity and structured light emitter temperature and outputting environmental state coefficients are as follows:

[0065] The ambient light intensity is obtained by performing maximum-min normalization on the current ambient light intensity.

[0066] The temperature index is obtained by comparing the difference between the current structured light emitter temperature and the optimal operating temperature of the structured light emitter with the difference between the maximum allowable temperature and the optimal operating temperature of the structured light emitter. The current structured light emitter temperature is between the minimum allowable temperature and the maximum allowable temperature.

[0067] An environmental state model is constructed based on the ambient light intensity index and the temperature index. This environmental state model is expressed as follows:

[0068]

[0069] in, Represents the environmental state coefficient and , Indicates the ambient light intensity index. Indicates the temperature index. Represents the weight coefficient and The The higher the value, the more stable the environment.

[0070] Among them, the ambient light intensity index refers to the standardized parameter that maps ambient light intensity of different dimensions to the [0,1] interval through maximum-minimum normalization processing. Specifically, it can be achieved by collecting ambient light intensity data in real time and calculating its relative ratio with the preset maximum and minimum light intensity values, which is used to eliminate the influence of differences in lighting conditions on the evaluation results.

[0071] The temperature index is a dimensionless parameter that characterizes the degree to which the temperature of the structured light emitter deviates from its optimal operating state. Specifically, it can be calculated by dividing the difference between the current temperature and the optimal temperature by the maximum allowable temperature difference. By limiting the temperature range, the device is ensured to be in a safe operating range, while amplifying the sensitivity of temperature fluctuations to stability.

[0072] The weighting coefficient refers to the allocation parameter used to adjust the contribution ratio of ambient light and temperature factors to the comprehensive evaluation result. Specifically, it can be dynamically adjusted using empirical values ​​or adaptive algorithms. Priority configuration under different working conditions can be achieved by satisfying the constraint that the sum of the coefficients is 1.

[0073] Specifically, the ambient light intensity index establishes a unified evaluation benchmark by eliminating dimensional differences, allowing for horizontal comparison of interference levels under strong and weak light environments. The temperature index converts absolute temperature differences into relative deviation rates through ratio calculations, reflecting both the temperature rise trend of equipment and avoiding extreme over-limit risks. In model construction, the temperature index uses a squared term, making the impact of high-temperature fluctuations on the stability coefficient exhibit non-linear amplification characteristics, which better reflects the accelerated degradation of equipment performance with increasing temperature under actual working conditions. The dynamic allocation mechanism of weighting coefficients allows for adjustment of the importance weights of environmental factors according to the welding scenario; for example, the weight of the temperature term can be appropriately increased in high-temperature workshop environments.

[0074] Compared with existing technologies, traditional methods typically rely on single environmental parameter thresholds or fixed-weight linear superposition, which struggle to accurately reflect the dynamic environmental state under the coupled effects of multiple factors. This solution introduces a nonlinear combined model and normalized exponential transformation to achieve a synergistic assessment of ambient light interference and equipment temperature rise, overcoming the limitations of single-parameter assessment. Furthermore, the design of a temperature square term enhances the sensitivity of assessments under high-temperature conditions.

[0075] Through the above technical solution, this application can dynamically quantify the combined impact of changes in light intensity and equipment temperature fluctuations in the welding environment, providing a real-time environmental stability assessment basis for the structured light vision system. This effectively suppresses positioning offset and spot distortion caused by environmental interference, improving the accuracy of weld trajectory recognition. The continuous output characteristic of the environmental state coefficient enables the subsequent control module to pre-adjust according to the stability change trend, avoiding the system response lag problem caused by sudden environmental disturbances in traditional methods.

[0076] Preferably, the step of constructing a welding state model based on welding fume particle concentration and arc intensity and outputting welding state coefficients is as follows:

[0077] The welding fume particle concentration index is obtained by performing maximum-min normalization on the current welding fume particle concentration.

[0078] Import the current arc intensity into the formula Obtain the arc intensity index. Indicates the reference arc intensity. Indicates the current arc light intensity;

[0079] A welding state model is constructed based on the welding fume particle concentration index and the arc light intensity index. The welding state model is expressed as follows:

[0080]

[0081] in, Indicates the welding condition coefficient and , Indicates the smoke and dust attenuation coefficient. This indicates the concentration index of welding fume particles. Indicates the arc intensity index. Represents the weight coefficient and The The larger the value, the less interference.

[0082] The welding fume particle concentration index and arc light intensity index are imported into the welding condition model to output the welding condition coefficient.

[0083] Among them, the current welding fume particle concentration is subjected to maximum-minimum normalization, which refers to the standardization operation of mapping the original concentration data to the range of 0 to 1. Specifically, the concentration data can be collected by a sensor (laser scattering particle sensor or resistive dust sensor). This process eliminates dimensional differences and quantifies the degree of interference of the fume on the light path scattering.

[0084] The arc intensity index calculation formula refers to suppressing strong arc light interference by using the ratio of reference intensity to actual intensity. Specifically, it can be calculated by substituting the arc light intensity into a nonlinear function after real-time measurement using a photoelectric sensor. This formula reduces the smothering effect of high-intensity arc light on structured light imaging.

[0085] The exponential decay function refers to the nonlinear mapping of the welding fume particle concentration index in the model. Specifically, it can be implemented using the natural exponential function, which enhances the aggravating effect of high-concentration fumes on interference.

[0086] The weighting coefficient refers to the contribution ratio of smoke and arc light factors in the model. It can be dynamically adjusted using empirical values ​​or adaptive algorithms. This coefficient enables the autonomous identification and compensation of the main interference factors under different working conditions.

[0087] Specifically, the concentration of welding fume particles is collected by sensors and normalized to form a welding fume particle concentration index in the range of 0 to 1. This index directly reflects the level of interference from the scattering of structured light by the fume particles. Arc light intensity is measured in real time by photoelectric sensors and calculated using a nonlinear formula. This index decreases as the actual arc light intensity increases, effectively suppressing the negative impact of high-intensity arc light on image quality. During model building, the welding fume particle concentration index is input into an exponential decay function, causing the interference level to increase exponentially when the welding fume particle concentration exceeds a threshold. The arc light intensity index is represented by a linear superposition method, reflecting its continuous interference characteristics on image quality. The weighting coefficients are dynamically adjusted according to the operating conditions. When the welding fume particle concentration is too high, the weight of the exponential decay term is increased; when the arc light intensity suddenly increases, the weight of the arc light term is increased, achieving a distinction between primary and secondary interference factors and a comprehensive evaluation.

[0088] Compared to existing technologies, traditional methods use fixed thresholds to determine welding fume or arc light interference, failing to quantify the coupling effect of multiple interference factors. This solution establishes a dynamic model incorporating nonlinear functions, enabling not only the coordinated evaluation of welding fume particle concentration and arc light intensity but also automatic identification of primary interference sources through weight allocation. Existing linear compensation methods for arc light interference are prone to failure under strong arc light, while the arc light intensity index calculation formula used in this solution is adaptive, automatically reducing its influence weight when the arc light intensity exceeds the reference value.

[0089] Through the above technical solution, this application can quantify the comprehensive interference level of smoke and arc light on the visual positioning system in real time during the welding process. It can accurately characterize the scattering effect of high-concentration smoke and the flooding effect of strong arc light through a nonlinear model, and use a dynamic weight allocation mechanism to distinguish the primary and secondary interference factors, providing accurate interference degree parameters for subsequent stability assessment, thereby effectively improving the anti-interference capability of the structured light vision system in complex welding environments.

[0090] Preferably, the step of constructing an environmental stability model and outputting environmental stability coefficients based on the welding state coefficients and environmental state coefficients under the current vibration amplitude of the structured light emitter is as follows:

[0091] The vibration amplitude index is obtained by comparing the current vibration amplitude of the structured light emitter with the maximum allowable vibration amplitude.

[0092] An environmental stability model is constructed based on the vibration amplitude index, environmental state coefficient, and welding state coefficient. The environmental stability model is expressed as follows:

[0093]

[0094] in, Indicates the environmental stability coefficient and , Represents the environmental state coefficient. Indicates the welding condition coefficient. Indicates the vibration amplitude index. Represents the weight coefficient and The The higher the value, the stronger the anti-interference ability;

[0095] The vibration amplitude index, environmental state coefficient, and welding state coefficient are imported into the environmental stability model to output the environmental stability coefficient.

[0096] The vibration amplitude index is the ratio of the current vibration amplitude of the structured light emitter to the maximum allowable vibration amplitude. It can be calculated by measuring the vibration amplitude using sensors and then performing a proportional calculation. This index quantifies the impact of vibration on the stability of the light source. The environmental state coefficient is a stability index calculated using ambient light intensity and the temperature of the structured light emitter. It can be achieved using normalization and a weighted square term combination. This index characterizes the combined interference of ambient light variations and temperature fluctuations on the system. The welding state coefficient is an interference level index calculated using welding fume particle concentration and arc light intensity. It can be achieved using exponential decay and normalization. This index reflects the dynamic changes in fume shielding and arc light interference during welding. The environmental stability model is a mathematical expression that integrates the vibration amplitude index, environmental state coefficient, and welding state coefficient. It can be implemented using a combination of linear weighting and exponential functions. This model is used to dynamically evaluate the system's anti-interference capability under combined interference.

[0097] Specifically, the vibration amplitude index transforms mechanical vibration amplitude into a standardized parameter through ratio processing. For example, when the vibration amplitude reaches 80% of the maximum allowable value, the vibration amplitude index is 0.8. The environmental state coefficient and welding state coefficient quantify interfering factors from two dimensions: environmental conditions and the welding process, respectively. For example, the environmental state coefficient can enhance the influence of temperature deviation through the squared term of the temperature difference. In the environmental stability model, the first term... The positive superposition effect of simultaneous stability of the environment and welding condition is enhanced by a product form; for example, this term contributes the greatest stability when both are close to 1. The second term... The negative impact of increased vibration amplitude is quickly suppressed by an exponential decay function; for example, for every 0.1 increase in the vibration amplitude exponent, the exponential term decays by approximately 10%. Weighting coefficients. The priority of different interference factors can be adjusted according to the actual working conditions. For example, in high-temperature environments, the priority can be increased. Weights are added to enhance sensitivity to vibrations.

[0098] Compared to existing technologies, traditional methods typically compensate for only a single interference source (such as vibration or arc light) independently. This solution, however, constructs a multi-factor coupled model that can simultaneously handle the combined interference of mechanical vibration, ambient light fluctuations, and welding fumes and arc light. For example, existing fixed-threshold vibration suppression strategies cannot adapt to dynamically changing welding fume particle concentrations, while this solution dynamically adjusts the anti-interference strategy using an environmental stability coefficient, automatically increasing the vibration suppression weight when welding fume particle concentration suddenly increases.

[0099] Through the above technical solution, this application can quantify the comprehensive impact of composite interference on the stability of structured light in real time, providing a dynamic evaluation basis for subsequent laser parameter optimization. For example, when coupled interference occurs during welding, such as increased equipment vibration and increased welding fume particle concentration, the environmental stability coefficient automatically decreases, triggering the divergence angle adjustment module to increase laser focusing accuracy, thereby maintaining the stability of weld trajectory positioning. Furthermore, through a model structure combining exponential and linear functions, the basic correlation between the environment and welding state is preserved, while the rapid response capability to sudden changes in vibration amplitude is enhanced, effectively solving the positioning offset problem caused by multi-physics coupling.

[0100] Preferably, the steps for constructing a structured light stability model based on the laser wavelength, linewidth, and structured light emitter power, and then outputting the structured light stability coefficients, are as follows:

[0101] The current line width is processed by maximum-min normalization to obtain the line width index;

[0102] The wavelength index is obtained by comparing the absolute value of the difference between the current laser wavelength and the nominal wavelength with the wavelength tolerance (which represents the sensitivity to wavelength shift).

[0103] A structured light stability model is constructed based on the linewidth exponent, wavelength exponent, and current structured light emitter power. This structured light stability model is expressed as follows:

[0104]

[0105] in, Indicates the structured light stability coefficient and , Indicates the wavelength index. Indicates the line width index. This indicates the current power of the structured light transmitter. This indicates the optimal structured light emitter power. This represents the standard deviation of the structured light emitter power. Represents the weight coefficient and The The higher the value, the more reliable the light source;

[0106] The linewidth index, wavelength index, and current structured light emitter power are imported into the structured light stability model to output the structured light stability coefficient.

[0107] Linewidth normalization refers to the process of mapping the actual linewidth value to a standardized range. Specifically, it can be implemented using the max-min normalization algorithm to eliminate the differences in linewidth dimensions under different working conditions and to transform linewidth fluctuations into a comparable exponential form.

[0108] The wavelength index is a quantitative ratio of the wavelength deviation to the system sensitivity. It can be achieved by dividing the absolute value difference by the tolerance threshold, and is used to reflect the nonlinear effect of wavelength deviation from the nominal value on the quality of the light source.

[0109] The structured light stability model refers to the evaluation function that integrates wavelength, linewidth, and power parameters. Specifically, it can be implemented by weighted summation of exponential decay term and Gaussian distribution term. The exponential function characterizes the sensitivity to wavelength and linewidth offset, while the Gaussian function describes the stability decay law when the power deviates from the optimal state.

[0110] Specifically, linewidth data of different dimensions are converted into standardized exponents through linewidth normalization. For example, when the linewidth fluctuates within the range of 0.1nm to 0.5nm, the normalized exponent is obtained in the range of 0 to 1. For wavelength offset, the ratio of its value to the system's allowable tolerance is calculated. For example, when the nominal wavelength is 980nm and the tolerance is 5nm, the wavelength exponent corresponding to the actual wavelength of 982nm is 0.4. In power parameter processing, a Gaussian function is used to evaluate the deviation of the current power from the optimal power. For example, when the power standard deviation is set to 10W, the exponent corresponding to the current power deviating from the optimal value by 5W is 0.882. By assigning weighting coefficients to the three exponents and linearly combining them, the final output structured light stability coefficient can dynamically reflect the comprehensive state of the light source in three dimensions: wavelength stability, linewidth consistency, and power control accuracy.

[0111] Compared to existing technologies, traditional methods typically monitor only a single parameter or use fixed thresholds to determine the light source status, such as issuing alarms based solely on power fluctuation ranges. This solution, however, constructs a multi-parameter coupled model that simultaneously considers wavelength shift sensitivity, linewidth fluctuation normalization, and the Gaussian distribution characteristics of power deviation. It employs a combination of nonlinear functions to more accurately characterize the stability changes of the light source, overcoming the misjudgment problem caused by single-parameter evaluation.

[0112] Through the above technical solution, this application can quantify the operating status of the laser source in real time at three key parameters: wavelength, linewidth, and power, and dynamically evaluate the overall stability of the structured light emitter. When the concentration of welding fume particles changes abruptly or the wavelength shift is caused by equipment vibration, the model can quickly identify the coupled influence of linewidth broadening and power fluctuations on the quality of the light source, providing accurate light source status data for subsequent adjustment of the laser divergence angle, thereby reducing weld trajectory positioning errors and spot imaging distortion caused by light source instability.

[0113] Preferably, the distance-reflection matching degree model is expressed as:

[0114]

[0115] in, Indicates distance-reflection matching degree and , Indicates the current laser working distance. Indicates the optimal laser working distance. Indicates the standard deviation of working distance. Indicates the environmental stability coefficient. Indicates the welding stability coefficient. The reflectivity of the workpiece surface is indicated by the following: The higher the value, the better the image quality.

[0116] The environmental stability coefficient is a comprehensive anti-interference parameter output from the environmental state model and welding state model, used to quantify the degree of environmental interference on structured light imaging. The structured light stability coefficient is a reliability index calculated using laser wavelength, linewidth, and transmitter power parameters, used to characterize the stability of the light source itself. The current laser working distance refers to the real-time distance between the structured light transmitter and the workpiece surface, which can be measured using a laser rangefinder or binocular vision system. The optimal laser working distance is a pre-calibrated reference distance at which the imaging quality is optimal. The working distance standard deviation is a parameter representing the tolerance range allowed for the laser working distance to deviate from the optimal value. The workpiece surface reflectivity is a parameter representing the workpiece surface's ability to reflect laser light, specifically achieved by measuring the intensity of reflected light using an optical sensor and normalizing it, used to reflect the influence of different materials or surface roughness on imaging.

[0117] Specifically, the product of the environmental stability coefficient and the structured light stability coefficient serves as the basic weight, and their synergistic effect suppresses the impact of external interference and light source fluctuations on imaging. An exponential term, in the form of a Gaussian function, penalizes the deviation between the current working distance and the optimal distance; the larger the distance deviation, the more significant the matching degree attenuation, thus constraining the laser working distance within an allowable range. Workpiece surface reflectivity, as a multiplicative factor, directly relates to surface reflection characteristics, incorporating the reflectivity differences of different materials or roughness into the model. Through multi-parameter dynamic fusion, this model unifies environmental stability, light source reliability, distance deviation, and surface reflectivity characteristics into a matching degree index, providing a quantitative basis for subsequent divergence angle optimization.

[0118] Compared to existing technologies, traditional methods typically adjust laser parameters based on fixed parameters or a single variable, such as compensation based solely on working distance or reflectivity, without comprehensively considering the coupled effects of environmental interference, light source fluctuations, and surface characteristics. In contrast, this solution constructs a multi-factor fusion matching degree model, which can dynamically adapt to multi-physics interference under complex working conditions, avoiding image quality degradation caused by adjusting a single parameter.

[0119] Through the above technical solution, this application solves the problem of image quality degradation caused by insufficient environmental stability, poor light source reliability, working distance deviation, and surface reflection differences during the welding process. By dynamically quantifying the matching degree index and optimizing the laser divergence angle parameters, precise positioning of the weld trajectory under complex working conditions is achieved, improving the adaptability and reliability of the automated welding system.

[0120] Preferably, the divergence angle optimization model is expressed as:

[0121]

[0122] in, Indicates the target divergence angle. Indicates the current divergence angle. Indicates distance-reflection matching degree. Indicates target distance - reflectivity matching degree. This indicates that the gain coefficient is adjusted.

[0123] The target divergence angle refers to the laser beam diffusion angle dynamically adjusted according to real-time operating conditions. Specifically, it can be achieved using tunable optical elements or electronically controlled focusing lens groups to compensate for the beam distortion caused by environmental interference.

[0124] The current divergence angle refers to the reference value of the divergence angle of the laser beam currently output by the laser emitter. It can be obtained by measuring the laser's built-in sensor or an external spot analyzer, and is used as the initial reference for adjustment.

[0125] Among them, the current distance-reflection matching degree refers to the optical adaptation index calculated by combining the stability of the environment and the stability of the light source. Specifically, it can be calculated in real time through a multi-sensor fusion algorithm to characterize the degree of deviation between the current imaging quality and the ideal state.

[0126] Among them, the target distance-reflection matching degree refers to the preset optimal threshold for imaging quality, which can be determined through calibration experiments or historical data statistics, and serves as a target reference value for adjusting the divergence angle.

[0127] Among them, the adjustment gain coefficient refers to the proportional parameter that controls the rate of change of the divergence angle. Specifically, it can be dynamically optimized through system stability testing or adaptive algorithms to balance the adjustment sensitivity and the risk of system oscillation.

[0128] Specifically, the model achieves dynamic optimization of the divergence angle through a closed-loop feedback mechanism. When the concentration of welding fume particles or equipment vibration during welding causes a decrease in the distance-reflection matching degree, the model uses the deviation between the current matching degree and the target value as an input parameter and calculates the divergence angle adjustment amount through a linear proportional relationship. The adjustment gain coefficient is set as a configurable parameter, for example, automatically adjusted according to the rate of change of operating conditions using a fuzzy control algorithm, thereby achieving a balance between rapid response and system stability. When the matching degree is higher than the target value, the divergence angle is reduced proportionally to enhance the energy concentration of the light spot; when the matching degree is lower than the target value, the divergence angle is increased proportionally to compensate for the light intensity attenuation. This forms a real-time adjustment loop based on optical adaptation indicators, effectively suppressing positioning deviations caused by environmental interference.

[0129] Through the above technical solution, this application solves the problem of beam distortion caused by dynamic interference during welding, and achieves real-time optimization of structured light imaging quality. Specifically, in scenarios with fluctuating welding fume particle concentration, the effective beam area is maintained by dynamically expanding the divergence angle; when equipment vibration causes changes in working distance, the divergence angle is automatically corrected through matching degree feedback, reducing imaging blur caused by focal length shift. Furthermore, the introduction of gain coefficient adjustment avoids frequent parameter oscillations caused by sensor noise or transient interference, ensuring the stability of the control system.

[0130] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0131] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time positioning of a weld seam trajectory, characterized in that The method comprises the following steps: An environment state model is constructed based on the ambient light intensity and the structured light emitter temperature to output an environment state coefficient; A welding state model is constructed based on the welding fume particle concentration and the arc light intensity to output a welding state coefficient; An environment stability model is constructed based on the welding state coefficient and the environment state coefficient under the current vibration amplitude of the structured light emitter to output an environment stability coefficient; A structured light stability model is constructed based on the laser wavelength, the line width and the structured light emitter power to output a structured light stability coefficient; A distance-reflectivity matching degree model is constructed based on the environment stability coefficient and the laser working distance and the workpiece surface reflectivity under the structured light stability coefficient to output a distance-reflectivity matching degree; A divergence angle optimization model is constructed based on the distance-reflectivity matching degree and the current laser divergence angle to output a target laser divergence angle.

2. The method of real-time weld seam trajectory positioning according to claim 1, characterized in that The step of constructing the environment state model based on the ambient light intensity and the structured light emitter temperature to output the environment state coefficient comprises the following steps: The current ambient light intensity is subjected to maximum-minimum normalization processing to obtain an ambient light intensity index; The difference between the current structured light emitter temperature and the optimal working temperature of the structured light emitter is subjected to ratio processing with the difference between the maximum allowable temperature and the optimal working temperature of the structured light emitter to obtain a temperature index, wherein the current structured light emitter temperature is between the minimum allowable temperature and the maximum allowable temperature; An environment state model is constructed according to the ambient light intensity index and the temperature index, and the environment state model is expressed as: wherein, represents the environmental state coefficient and , represents the ambient light intensity index, represents the temperature index, represents the weight coefficient and , the the greater the value, the more stable the environment.

3. The method of real-time weld seam trajectory positioning according to claim 2, characterized in that The step of constructing the welding state model based on the welding fume particle concentration and the arc light intensity to output the welding state coefficient comprises the following steps: The current welding fume particle concentration is subjected to maximum-minimum normalization processing to obtain a welding fume particle concentration index; Introducing the current arc light intensity into the formula obtaining an arc light intensity index, denotes the reference arc light intensity, denotes the current arc light intensity; A welding state model is constructed based on the welding fume particle concentration index and the arc light intensity index, and the welding state model is expressed as: wherein, represents a welding condition coefficient and , represents a smoke attenuation coefficient, represents a welding smoke particle concentration index, represents an arc light intensity index, represents a weight coefficient and , the the greater the value the less the interference; The current welding fume particle concentration index and the current arc light intensity index are introduced into the welding state model to output the welding state coefficient.

4. The method of real-time weld seam trajectory positioning according to claim 3, characterized in that The step of constructing the environment stability model based on the welding state coefficient and the environment state coefficient under the current vibration amplitude of the structured light emitter to output the environment stability coefficient comprises the following steps: The current vibration amplitude of the structured light emitter is subjected to ratio processing with the maximum allowable vibration amplitude to obtain a vibration amplitude index; An environment stability model is constructed based on the vibration amplitude index, the environment state coefficient and the welding state coefficient, and the environment stability model is expressed as: wherein, represents the environmental stability coefficient and , represents the environmental state coefficient, represents the welding state coefficient, represents the vibration amplitude index, represents the weight coefficient and , the greater the value, the stronger the anti-interference ability; The vibration amplitude index, the environment state coefficient and the welding state coefficient are introduced into the environment stability model to output the environment stability coefficient.

5. The method of real-time weld seam trajectory positioning according to claim 4, characterized in that The step of constructing the structured light stability model based on the laser wavelength, the line width and the structured light emitter power to output the structured light stability coefficient comprises the following steps: The current line width is subjected to maximum-minimum normalization processing to obtain a line width index; The absolute value of the difference between the current laser wavelength and the nominal wavelength is subjected to ratio processing with the wavelength tolerance to obtain a wavelength index; A structured light stability model is constructed according to the line width index, the wavelength index and the current structured light emitter power, and the structured light stability model is expressed as: wherein, represents the coefficient of structural light stability and , represents the wavelength index, represents the line width index, represents the current structural light emitter power, represents the optimal structural light emitter power, represents the structural light emitter power standard deviation, represents the weight coefficient and , the higher the value, the more reliable the light source; The line width index, the wavelength index and the current structured light emitter power are introduced into the structured light stability model to output the structured light stability coefficient.

6. The method of real-time weld trajectory positioning of claim 5, wherein, The distance-reflection matching degree model is expressed as: wherein, represents the distance-reflectivity matching degree and , represents the current laser working distance, represents the optimal laser working distance, represents the working distance standard deviation, represents the environmental stability coefficient, represents the welding stability coefficient, represents the workpiece surface reflectivity, and the the higher the value, the better the imaging quality.

7. The method of real-time weld seam trajectory positioning according to claim 6, characterized in that, The divergence angle optimization model is expressed as: wherein, denotes a target divergence angle, denotes a current divergence angle, denotes a distance-reflectivity matching degree, denotes a target distance-reflectivity matching degree, denotes an adjustment gain coefficient.

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