Quality detection system in laser welding process

Abstract

The application discloses a quality detection system in a laser welding process and belongs to the technical field of laser welding.The application comprises a real-time monitoring module, a welding parameter adjustment module, a weld seam parameter prediction module, a data optimization module and a correlation module; the real-time monitoring module uses an industrial camera to perform real-time monitoring on equipment models, equipment placement positions, welding tracks of laser welding equipment and weld seam images based on the welding tracks on a production line, and transmits monitoring data to the welding parameter adjustment module; the welding parameter adjustment module receives the monitoring data transmitted by the real-time monitoring module, adjusts welding parameters based on the monitoring data, and transmits the adjusted welding parameters to the weld seam parameter prediction module and the data optimization module; the weld seam parameter prediction module receives the adjustment parameters transmitted by the welding parameter adjustment module, predicts weld seam parameters based on the received data and product models, and transmits the prediction results to the data optimization module.

Description

Technical Field

[0001] This invention relates to the field of laser welding technology, specifically a quality inspection system for the laser welding process. Background Technology

[0002] Laser welding is a highly efficient and precise welding method that uses a high-energy-density laser beam as a heat source. Laser welding is one of the important applications of laser material processing technology. The laser welding process parameters need to be adjusted according to the product models on the production line to ensure the quality of laser welding.

[0003] Existing quality inspection systems in laser welding processes cannot adjust the welding trajectory in real time during product welding. This means that when products of different specifications are changed or their placement is not standardized, the welding trajectory of the laser welding equipment cannot be changed in advance, resulting in unusable welded products, increased production costs, and the inability to adjust welding process parameters in a timely manner when welding different specifications, leading to low welding efficiency. Furthermore, the inability to adjust the welding time of the laser welding equipment according to the weld depth of the product results in incomplete welding and breakage of the welded product, reducing welding quality. Summary of the Invention

[0004] The purpose of this invention is to provide a quality inspection system for laser welding processes to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a quality inspection system for laser welding process, the system including a real-time monitoring module, a welding parameter adjustment module, a weld parameter prediction module, a data optimization module and a correlation module;

[0006] The real-time monitoring module uses an industrial camera to monitor the equipment model, equipment placement, welding trajectory of the laser welding equipment, and weld seam images based on the welding trajectory on the production line in real time, and transmits the monitoring data to the welding parameter adjustment module.

[0007] The welding parameter adjustment module receives the monitoring data transmitted by the real-time monitoring module, adjusts the welding parameters based on the monitoring data, and transmits the adjusted welding parameters to the weld parameter prediction module and the data optimization module.

[0008] The weld parameter prediction module receives the adjustment parameters transmitted by the welding parameter adjustment module, predicts the weld parameters based on the received data and product model, and transmits the prediction results to the data optimization module.

[0009] The data optimization module receives the prediction data transmitted by the weld parameter prediction module and the adjustment parameters transmitted by the welding parameter adjustment module, optimizes the welding process parameters based on the received data, and transmits the optimized welding process parameters to the associated module.

[0010] The association module receives the optimized data transmitted by the data optimization module and performs association processing between the optimized data and the welding trajectory of the welding product.

[0011] Furthermore, the real-time monitoring module includes a data monitoring unit and a product welding start point monitoring unit;

[0012] The data monitoring unit uses an industrial camera to monitor the product model, product placement position, product welding trajectory, and weld seam images based on the welding trajectory on the production line, and transmits the monitoring data to the product welding start point monitoring unit.

[0013] The product welding start point monitoring unit receives the monitoring data transmitted by the data monitoring unit, determines the nearest welding start point of the product based on the product placement angle and the initial position of the laser welding equipment, and transmits the determined nearest welding start point, the product welding trajectory, and the weld image based on the welding trajectory to the welding parameter adjustment module and the weld parameter prediction module. Determining the nearest welding start point of the product by using the product placement angle and the initial position of the laser welding equipment is beneficial for rapid welding of multi-specification products at different placement angles and positions, thus shortening the product welding time.

[0014] Furthermore, the welding parameter adjustment module includes a product welding path determination unit, a standard product welding path comparison unit, and a welding parameter adjustment unit;

[0015] The product welding path determination unit receives the content transmitted by the product welding start point monitoring unit, calculates the dynamic distance between the laser welding equipment and the nearest welding start point based on the received content, and adjusts the angle between the dynamic position of the laser welding equipment and the nearest welding start point according to the calculation results and the product welding trajectory, so as to realize the real-time adjustment of the product welding path, and transmits the real-time adjusted product welding path to the standard product welding path comparison unit.

[0016] The standard product welding path comparison unit receives the real-time adjusted product welding path transmitted by the product welding path determination unit, compares the standard product welding path with the received product welding path, analyzes the specification ratio between the standard product and the welded product, and transmits the analysis results to the welding parameter adjustment unit.

[0017] The welding parameter adjustment unit receives the analysis results transmitted by the standard product welding path comparison unit, adjusts the welding parameters of the laser welding equipment according to the analysis results, and transmits the adjusted welding parameters to the data optimization module.

[0018] Furthermore, the weld parameter prediction module includes a weld image processing unit, a weld data correction unit, and a weld data prediction unit;

[0019] The weld seam image processing unit receives the content transmitted by the product welding start point monitoring unit, converts the weld seam image information of the welding trajectory into data information, and transmits the data information to the weld seam data correction unit.

[0020] The weld data correction unit receives the data information transmitted by the weld image processing unit, performs correction processing on the weld data based on the received content, and transmits the corrected weld data to the weld data prediction unit.

[0021] The weld data prediction unit receives the weld data transmitted by the weld data correction unit, predicts the weld depth of products of different specifications based on the received data, and transmits the prediction results to the data optimization module.

[0022] Furthermore, the specific method by which the weld image processing unit converts the weld image information of the welding trajectory into data information is as follows:

[0023] 1) Perform grayscale processing on the weld image, construct a coordinate system with one corner of the weld image as the origin, set the vertical coordinate of the coordinate system as the grayscale value of the weld image, and the horizontal coordinate of the coordinate system as the distance value corresponding to the grayscale value of the weld image;

[0024] 2) Extract the grayscale values ​​of weld seams on the same horizontal axis, and then use the extracted grayscale values ​​to measure the relative reflectance of light. To determine this, the formula for calculating the relative reflectivity of light is: ,in, , These represent the grayscale values ​​at both ends of the weld image. This represents the grayscale value corresponding to the weld in the weld image. Image of weld seam The vertical length of the weld in the end-to-end distance weld image. Image of weld seam The vertical length of the weld in the weld image at the end distance;

[0025] 3) The weld depth is calculated based on the determined relative reflectivity of light and the gray value of the weld. The specific calculation formula is as follows: for:

[0026] ;

[0027] in, The x-coordinate is The corresponding relative reflectivity of light, The x-coordinate is The corresponding relative reflectivity of light, This indicates that when the relative reflectance of light is... The corresponding weld depth, This represents the proportionality coefficient. Indicates the weld depth;

[0028] The weld depth is calculated by using the relative reflectivity of light and the gray value of the weld. This reduces the difference between the gray value of the weld image and the actual value when the industrial camera acquires the weld image due to the different angles of the illumination light under different environments and times. As a result, the predicted weld depth is different from the standard value, which reduces the prediction accuracy.

[0029] 4) Correct the weld depth calculation results based on the determined degree of change in relative light reflectivity.

[0030] Furthermore, the weld data prediction unit predicts the weld depth of products of different specifications based on the corrected weld depth data. The specific method is as follows:

[0031] (1) Calculate the degree of change of relative reflectivity of light and the degree of change of weld depth based on the horizontal axis;

[0032] (2) Adjust the degree of change in weld depth based on the degree of change in relative reflectivity of light in (1);

[0033] (3) Adjust the calculated weld depth based on the adjustment data in (2), calculate the weld depth error coefficient after adjustment, and predict the weld depth of different specifications of products according to the error coefficient and the product specification ratio coefficient.

[0034] Furthermore, the data optimization module receives the adjustment data transmitted by the welding parameter adjustment unit and the prediction data transmitted by the weld data prediction unit, optimizes the process parameters of the laser welding equipment based on the adjustment data, optimizes the welding time of the laser welding equipment at the corresponding position in the welding trajectory based on the prediction data, and transmits the optimized welding process parameters to the associated module.

[0035] Furthermore, the association module includes an optimized data storage unit, a matching unit, and an association unit;

[0036] The optimized data storage unit receives the optimized data transmitted by the data optimization module, stores the received optimized data, and transmits the stored data to the matching unit.

[0037] The matching unit receives the stored data transmitted by the optimized data storage unit, matches the stored data with the car model, and transmits the matching result to the association unit.

[0038] The association unit receives the matching results transmitted by the matching unit and predicts the correlation of welding process parameters between different car models based on the received content.

[0039] Furthermore, the association unit predicts the correlation of welding process parameters between different car models based on the received content, using a specific prediction formula. for:

[0040] The stored optimized data is scaled proportionally to the product's standard dimensions;

[0041] ;

[0042] in, This indicates the laser welding timing information. Indicates the car model is At that time, The welding process parameters corresponding to each moment, Indicates the car model is At that time, The welding process parameters corresponding to each moment, Indicates the number of data comparison groups. Indicates in to At any time, for the car model and The proportion of changes in welding process parameters was calculated. The XOR symbol is used when... When, the output is 0, when When the time is right, the output is 1. This indicates that the car model is and Predicting the non-correlation of welding process parameters;

[0043] The correlation between welding process parameters between different car models can be predicted, and welding operations can be performed on products by calling the pre-set welding process parameters. This eliminates the need for manual modification of welding process parameters when welding products of different specifications on laser welding equipment, further improving welding efficiency.

[0044] Furthermore, based on the prediction formula When predicting the correlation of welding process parameters between different car models, the parts with high correlation between different car models can be obtained. These highly correlated parts refer to... The corresponding welding process parameters.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0046] 1. This invention calculates the relative reflection of light based on the change in grayscale value in the weld image, predicts the weld depth of different products based on the relative reflectivity of light and the grayscale value of the weld, and adjusts the laser welding time according to the predicted weld depth value. This avoids the need for manual adjustment of the laser welding time when the weld depth of products is not uniform, or the inability to completely weld the product weld, thereby reducing the quality of laser welding.

[0047] 2. This invention predicts the correlation between welding process parameters of different car models. When welding products, it calls the pre-set welding process parameters based on the predicted correlation, and fine-tunes the called welding process parameters. The welding operation is then performed based on the fine-tuned welding process parameters. This allows for timely adjustment of welding process parameters when welding products of different specifications, further improving welding efficiency.

[0048] 3. This invention finds the nearest welding point of products in different placement positions and replans the welding trajectory based on the nearest welding point, thus avoiding the inability to quickly switch welding trajectories when the product is not placed in a standard position, and further reducing production costs. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a schematic diagram illustrating the working principle of the quality inspection system in the laser welding process of this invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please see Figure 1 The present invention provides a technical solution: a quality inspection system for laser welding process, the system including a real-time monitoring module, a welding parameter adjustment module, a weld parameter prediction module, a data optimization module and a correlation module;

[0053] The real-time monitoring module uses an industrial camera to monitor the equipment model, equipment placement, welding trajectory of the laser welding equipment, and weld seam images based on the welding trajectory on the production line in real time, and transmits the monitoring data to the welding parameter adjustment module.

[0054] The real-time monitoring module includes a data monitoring unit and a product welding start point monitoring unit;

[0055] The data monitoring unit uses industrial cameras to monitor the product model, product placement position, product welding trajectory, and weld seam images based on the welding trajectory on the production line, and transmits the monitoring data to the product welding start point monitoring unit.

[0056] The product welding start point monitoring unit receives the monitoring data transmitted by the data monitoring unit, determines the nearest welding start point of the product based on the product placement angle and the initial position of the laser welding equipment, and transmits the determined nearest welding start point, the product welding trajectory, and the weld image based on the welding trajectory to the welding parameter adjustment module and the weld parameter prediction module. Determining the nearest welding start point of the product by using the product placement angle and the initial position of the laser welding equipment is beneficial for rapid welding of multi-specification products at different placement angles and positions, thus shortening the product welding time.

[0057] The welding parameter adjustment module receives the monitoring data transmitted by the real-time monitoring module, adjusts the welding parameters based on the monitoring data, and transmits the adjusted welding parameters to the weld parameter prediction module and the data optimization module.

[0058] The welding parameter adjustment module includes a product welding path determination unit, a standard product welding path comparison unit, and a welding parameter adjustment unit.

[0059] The product welding path determination unit receives the content transmitted by the product welding start point monitoring unit, calculates the dynamic distance between the laser welding equipment and the nearest welding start point based on the received content, and adjusts the angle between the dynamic position of the laser welding equipment and the nearest welding start point according to the calculation results and the product welding trajectory, so as to realize the real-time adjustment of the product welding path and transmit the real-time adjusted product welding path to the standard product welding path comparison unit.

[0060] The standard product welding path comparison unit receives the real-time adjusted product welding path transmitted by the product welding path determination unit, compares the standard product welding path with the received product welding path, analyzes the specification ratio between the standard product and the welded product, and transmits the analysis results to the welding parameter adjustment unit.

[0061] The welding parameter adjustment unit receives the analysis results transmitted by the standard product welding path comparison unit, adjusts the welding parameters of the laser welding equipment according to the analysis results, and transmits the adjusted welding parameters to the data optimization module.

[0062] The weld parameter prediction module receives the adjustment parameters transmitted by the welding parameter adjustment module, predicts the weld parameters based on the received data and product model, and transmits the prediction results to the data optimization module.

[0063] The weld parameter prediction module includes a weld image processing unit, a weld data correction unit, and a weld data prediction unit;

[0064] The weld seam image processing unit receives the content transmitted by the product welding start point monitoring unit, converts the weld seam image information of the welding trajectory into data information, and transmits the data information to the weld seam data correction unit. The specific method by which the weld seam image processing unit converts the weld seam image information of the welding trajectory into data information is as follows:

[0065] 1) Perform grayscale processing on the weld image, construct a coordinate system with one corner of the weld image as the origin, set the vertical coordinate of the coordinate system as the grayscale value of the weld image, and the horizontal coordinate of the coordinate system as the distance value corresponding to the grayscale value of the weld image;

[0066] 2) Extract the grayscale values ​​of weld seams on the same horizontal axis, and then use the extracted grayscale values ​​to measure the relative reflectance of light. To determine this, the formula for calculating the relative reflectivity of light is: ,in, , These represent the grayscale values ​​at both ends of the weld image. This represents the grayscale value corresponding to the weld in the weld image. Image of weld seam The vertical length of the weld in the end-to-end distance weld image. Image of weld seam The vertical length of the weld in the weld image at the end distance;

[0067] 3) The weld depth is calculated based on the determined relative reflectivity of light and the gray value of the weld. The specific calculation formula is as follows: for:

[0068] ;

[0069] in, The x-coordinate is The corresponding relative reflectivity of light, The x-coordinate is The corresponding relative reflectivity of light, This indicates that when the relative reflectance of light is... The corresponding weld depth, This represents the proportionality coefficient. Indicates the weld depth;

[0070] The weld depth is calculated by using the relative reflectivity of light and the gray value of the weld. This reduces the difference between the gray value of the weld image and the actual value when the industrial camera acquires the weld image due to the different angles of the illumination light under different environments and times. As a result, the predicted weld depth is different from the standard value, which reduces the prediction accuracy.

[0071] 4) Correct the weld depth calculation results based on the determined degree of change in relative light reflectivity;

[0072] The weld data correction unit receives the data information transmitted by the weld image processing unit, performs correction processing on the weld data based on the received content, and transmits the corrected weld data to the weld data prediction unit.

[0073] The weld data prediction unit receives the weld data transmitted by the weld data correction unit, predicts the weld depth for products of different specifications based on the received data, and transmits the prediction results to the data optimization module. The specific method is as follows:

[0074] (1) Calculate the degree of change of relative reflectivity of light and the degree of change of weld depth based on the horizontal axis;

[0075] (2) Adjust the degree of change in weld depth based on the degree of change in relative reflectivity of light in (1);

[0076] (3) Adjust the calculated weld depth based on the adjustment data in (2), calculate the weld depth error coefficient after adjustment, and predict the weld depth of different specifications of products according to the error coefficient and the product specification ratio coefficient.

[0077] The data optimization module receives the adjustment data transmitted by the welding parameter adjustment unit and the prediction data transmitted by the weld data prediction unit, optimizes the process parameters of the laser welding equipment based on the adjustment data, optimizes the welding time of the laser welding equipment at the corresponding position in the welding trajectory based on the prediction data, and transmits the optimized welding process parameters to the associated module.

[0078] The association module receives the optimized data transmitted by the data optimization module and performs association processing between the optimized data and the welding trajectory of the welding product;

[0079] The association module includes an optimized data storage unit, a matching unit, and an association unit;

[0080] The optimized data storage unit receives the optimized data transmitted by the data optimization module, stores the received optimized data, and transmits the stored data to the matching unit.

[0081] The matching unit receives the stored data transmitted by the optimized data storage unit, matches the stored data with the car model, and transmits the matching result to the association unit;

[0082] The association unit receives the matching results transmitted by the matching unit and predicts the correlation of welding process parameters between different car models based on the received content. The specific prediction formula is as follows: for:

[0083] The stored optimized data is scaled proportionally to the product's standard dimensions;

[0084] ;

[0085] in, This indicates the laser welding timing information. Indicates the car model is At that time, The welding process parameters corresponding to each moment, Indicates the car model is At that time, The welding process parameters corresponding to each moment, Indicates the number of data comparison groups. Indicates in to At any time, for the car model and The proportion of changes in welding process parameters was calculated. The XOR symbol is used when... When, the output is 0, when When the time is right, the output is 1. This indicates that the car model is and Predicting the non-correlation of welding process parameters;

[0086] The correlation between welding process parameters between different car models can be predicted, and the products can be welded by calling the pre-set welding process parameters. This eliminates the need for staff to manually modify the welding process parameters when welding products of different specifications on the laser welding equipment, thus further improving welding efficiency.

[0087] Based on the prediction formula When predicting the correlation of welding process parameters between different car models, the parts with high correlation between different car models can be obtained. These highly correlated parts refer to... The corresponding welding process parameters.

[0088] 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 process, method, article, or apparatus.

[0089] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quality inspection system for laser welding process, characterized in that: The system includes a real-time monitoring module, a welding parameter adjustment module, a weld parameter prediction module, a data optimization module, and a correlation module. The real-time monitoring module uses an industrial camera to monitor the equipment model, equipment placement, welding trajectory of the laser welding equipment, and weld seam images based on the welding trajectory on the production line in real time, and transmits the monitoring data to the welding parameter adjustment module. The real-time monitoring module includes a data monitoring unit and a product welding start point monitoring unit; The welding parameter adjustment module receives the monitoring data transmitted by the real-time monitoring module, adjusts the welding parameters based on the monitoring data, and transmits the adjusted welding parameters to the weld parameter prediction module and the data optimization module. The welding parameter adjustment module includes a product welding path determination unit, a standard product welding path comparison unit, and a welding parameter adjustment unit. The weld parameter prediction module receives the adjustment parameters transmitted by the welding parameter adjustment module, predicts the weld parameters based on the received data and product model, and transmits the prediction results to the data optimization module. The weld parameter prediction module includes a weld image processing unit, a weld data correction unit, and a weld data prediction unit. The weld seam image processing unit receives the content transmitted by the product welding start point monitoring unit, converts the weld seam image information of the welding trajectory into data information, and transmits the data information to the weld seam data correction unit. The weld data correction unit receives the data information transmitted by the weld image processing unit, performs correction processing on the weld data based on the received content, and transmits the corrected weld data to the weld data prediction unit. The weld data prediction unit receives the weld data transmitted by the weld data correction unit, predicts the weld depth of products of different specifications based on the received data, and transmits the prediction results to the data optimization module. The data optimization module receives the prediction data transmitted by the weld parameter prediction module and the adjustment parameters transmitted by the welding parameter adjustment module, optimizes the welding process parameters based on the received data, and transmits the optimized welding process parameters to the associated module. The data optimization module receives the adjustment data transmitted by the welding parameter adjustment unit and the prediction data transmitted by the weld data prediction unit, optimizes the process parameters of the laser welding equipment based on the adjustment data, optimizes the welding time of the laser welding equipment at the corresponding position in the welding trajectory based on the prediction data, and transmits the optimized welding process parameters to the associated module. The association module receives the optimized data transmitted by the data optimization module and performs association processing between the optimized data and the welding trajectory of the welding product; The association module includes an optimized data storage unit, a matching unit, and an association unit; The optimized data storage unit receives the optimized data transmitted by the data optimization module, stores the received optimized data, and transmits the stored data to the matching unit. The matching unit receives the stored data transmitted by the optimized data storage unit, matches the stored data with the car model, and transmits the matching result to the association unit. The association unit receives the matching results transmitted by the matching unit and predicts the correlation of welding process parameters between different car models based on the received content.

2. The quality inspection system for laser welding process according to claim 1, characterized in that: The data monitoring unit uses an industrial camera to monitor the product model, product placement position, product welding trajectory, and weld seam images based on the welding trajectory on the production line, and transmits the monitoring data to the product welding start point monitoring unit. The product welding start point monitoring unit receives the monitoring data transmitted by the data monitoring unit, determines the nearest welding start point of the product based on the product placement angle and the initial position of the laser welding equipment, and transmits the determined nearest welding start point, the product welding trajectory, and the weld image based on the welding trajectory to the welding parameter adjustment module and the weld parameter prediction module.

3. The quality inspection system for laser welding process according to claim 2, characterized in that: The product welding path determination unit receives the content transmitted by the product welding start point monitoring unit, calculates the dynamic distance between the laser welding equipment and the nearest welding start point based on the received content, and adjusts the angle between the dynamic position of the laser welding equipment and the nearest welding start point according to the calculation results and the product welding trajectory, so as to realize the real-time adjustment of the product welding path, and transmits the real-time adjusted product welding path to the standard product welding path comparison unit. The standard product welding path comparison unit receives the real-time adjusted product welding path transmitted by the product welding path determination unit, compares the standard product welding path with the received product welding path, analyzes the specification ratio between the standard product and the welded product, and transmits the analysis results to the welding parameter adjustment unit. The welding parameter adjustment unit receives the analysis results transmitted by the standard product welding path comparison unit, adjusts the welding parameters of the laser welding equipment according to the analysis results, and transmits the adjusted welding parameters to the data optimization module.

4. The quality inspection system for laser welding process according to claim 3, characterized in that: The weld data prediction unit predicts the weld depth of products of different specifications based on the corrected weld depth data. The specific method is as follows: (1) Calculate the degree of change of relative reflectivity of light and the degree of change of weld depth based on the horizontal axis; (2) Adjust the degree of change in weld depth based on the degree of change in relative reflectivity of light in (1); (3) Adjust the calculated weld depth based on the adjustment data in (2), calculate the weld depth error coefficient after adjustment, and predict the weld depth of different specifications of products according to the error coefficient and the product specification ratio coefficient.

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