Point laser product detection method

Abstract

The invention provides a point laser product detection method. The method comprises the following steps: S1, compiling a preset template; s2, point laser dynamic scanning and data acquisition; s3, data distribution and waveform segmentation: distributing the acquired data to each measurement template, and accurately segmenting the waveform into corresponding product waveform data through encoder position information; s4, filtering algorithm processing: eliminating interference signals brought by the instrument through filtering algorithms such as height, width and average value; s5, wave crest and wave trough positioning and height difference calculation; s6, performing data synthesis and planeness and parallelism calculation; and S7, tolerance judgment and result output are conducted, the size is calculated, a final result is synthesized through tolerance judgment, the result and data are sent to a controller, and automatic sorting of defective products is achieved. The flatness, the parallelism and the point-to-surface distance of the product can be calculated, and the accuracy and the reliability of the measurement result are higher.

Description

Technical Field

[0001] The invention relates to the technical field of temperature control structures, and in particular to a point laser product detection method. Background Art

[0002] There are two main methods for measuring the flatness of components. One is the contact method, which mainly involves installing a velocity sensor or a strain gauge on the component to be measured. The equipment is small, the cost is low, and the operation is easy. However, the acceleration method requires two integrations to obtain the displacement. Its accuracy and effect depend on the algorithm. The installation and arrangement of the strain gauge is cumbersome, the reliability is not high, and the measurement accuracy is not high. The other is the non-contact method, which mainly uses an industrial camera or a laser tracker for measurement. During measurement, an industrial camera or a laser tracker or other detection equipment must be installed at a suitable position away from the plane to be measured and relative to the plane to be measured.

[0003] The existing measurement methods for measuring the dimensions of product components have low accuracy and measurement precision. There is a need for a method for detecting parameters such as product parallelism and flatness using point laser ranging, which can achieve higher detection accuracy and solve the above-mentioned problems. Summary of the invention

[0004] The present invention provides a point laser product detection method, which solves the problem of low accuracy of existing dimension measurement methods by technically transforming existing product parameter measurement methods such as parallelism and flatness.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A point laser product detection method comprises the following steps:

[0007] S1. Write a preset template. According to the actual product and the corresponding measurement requirements, write the preset template information for comparison and analysis of the actual measurement product;

[0008] S2, point laser dynamic scanning and data acquisition, obtain the height data of the product cross section and the encoder position information by point laser dynamic scanning the required position of the product;

[0009] S3, data allocation and waveform segmentation, allocate the acquired data to each measurement template, and accurately segment the waveform into corresponding product waveform data through the encoder position information;

[0010] S4, filtering algorithm processing, through filtering algorithm to eliminate interference signals brought by the instrument, to achieve accurate measurement of product size;

[0011] S5. Peak and trough positioning and height difference calculation: the height difference is calculated by the peak and trough positions of the product;

[0012] S6, data synthesis and flatness and parallelism calculation, the data of different measurement lines are synthesized through additional templates to calculate flatness, parallelism and point-to-surface distance;

[0013] S7, tolerance judgment and result output, the calculated dimensions are judged through tolerance, the final result is synthesized, and the result and data are sent to the controller to realize automatic sorting of defective products.

[0014] Preferably, in step S1, the template information includes standard dimensions of the product, locations of measuring points, measuring range, and key parameters of measurement accuracy.

[0015] Preferably, in step S2, during the scanning process, the height data of the product cross section and the encoder position information are recorded simultaneously to ensure that the parameters such as the scanning speed, laser intensity and sampling frequency are reasonably set, and the collected data is subjected to preliminary processing such as denoising or smoothing;

[0016] In step S2, according to product characteristics and measurement requirements, efficient scanning strategies are designed, such as spiral scanning, grid scanning, etc., to cover all key measurement areas.

[0017] Preferably, step S3 further comprises the following steps:

[0018] S301, allocating the acquired data to each preset measurement template according to the encoder position information;

[0019] S302, using the accuracy of the encoder position information, accurately dividing the waveform into corresponding product waveform data;

[0020] S303: further verify and correct the segmented waveform data.

[0021] Preferably, in step S4, a height and width filtering algorithm is applied to eliminate interference signals caused by the instrument, and an average value filtering method algorithm is used to further smooth the data to remove interference signals. At the same time, according to changes in product characteristics and measurement environment, the filtering parameters are dynamically adjusted to achieve the best filtering effect.

[0022] Preferably, step S5 further comprises the following steps:

[0023] S501, calculating the rate of change of the waveform data by using a difference derivative algorithm, and searching for the peak and trough positions of the product;

[0024] S502, accurately locating specific locations of wave peaks and wave troughs according to preset template information;

[0025] S503. Calculate the height difference between the wave crest and the wave trough according to the positioning result to obtain the required dimension data, and apply the verification algorithm to verify the accuracy of the calculation result.

[0026] Preferably, in step S6, the data of different measurement lines are synthesized through additional templates, the data of different measurement lines are efficiently fused to form a complete three-dimensional model of the product, the difference or height dimension is calculated, multiple measurement areas are selected to apply the least squares method to calculate the flatness, parallelism and point-to-surface distance, the results are verified and corrected, the calculation results are verified, and when abnormal values ​​or deviations are found, corrections or re-measurements are performed.

[0027] Preferably, in step S7, the final result is synthesized including the product's size, geometrical quantity, and whether it is qualified, and the determination result and raw data are sent to the controller to realize automatic sorting of defective products, and the key information and data in the detection process are recorded for subsequent analysis and improvement;

[0028] Key information and data during the inspection process include product number, measurement time or defect reason.

[0029] Preferably, in step S7, the determination result and the original data are output to the controller or display screen in an intuitive manner to facilitate viewing and analysis by operators.

[0030] Preferably, in step S7, an intelligent sorting system is introduced to automatically classify products into different areas according to the detection results to improve production efficiency.

[0031] The beneficial effects of the present invention are:

[0032] The present invention is programmed with a preset measurement template according to the actual product and the corresponding measurement requirements. The height data and encoder position information of the product cross section are obtained by point laser dynamic scanning of the position to be measured on the product, and the obtained data are distributed to each measurement template. The waveform is accurately divided into corresponding product waveform data by the encoder position information. The size of the product is accurately measured by filtering algorithms such as height, width and average value, the rate of change is calculated by differential derivation, the peak and trough positions of the product are searched, and then the position to be measured of the product is located, the height difference is calculated, the data of different measurement lines are synthesized by additional templates, the flatness, parallelism and point-to-surface distance are calculated, and finally all calculated sizes are judged by tolerance, the final result is synthesized, and the result and data are sent to the controller to realize automatic sorting of defective products.

[0033] The point laser measurement method of the present invention can realize product parameter detection more comprehensively and efficiently, with higher measurement accuracy, and meet the needs in actual production. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process steps of the present invention. DETAILED DESCRIPTION

[0035] The specific contents of the present invention are described in detail below with reference to the accompanying drawings and embodiments.

[0036] See also Figure 1 As shown, the present invention provides a point laser product detection method, comprising the following steps:

[0037] S1. Write a preset template. According to the actual product and the corresponding measurement requirements, write the preset template information for comparison and analysis of the actual measurement product;

[0038] S2, point laser dynamic scanning and data acquisition, obtain the height data of the product cross section and the encoder position information by point laser dynamic scanning the required position of the product;

[0039] S3, data allocation and waveform segmentation, allocate the acquired data to each measurement template, and accurately segment the waveform into corresponding product waveform data through the encoder position information;

[0040] S4, filtering algorithm processing, through filtering algorithm to eliminate interference signals brought by the instrument, to achieve accurate measurement of product size;

[0041] S5. Peak and trough positioning and height difference calculation: the height difference is calculated by the peak and trough positions of the product;

[0042] S6, data synthesis and flatness and parallelism calculation, the data of different measurement lines are synthesized through additional templates to calculate flatness, parallelism and point-to-surface distance;

[0043] S7, tolerance judgment and result output, the calculated dimensions are judged through tolerance, the final result is synthesized, and the result and data are sent to the controller to realize automatic sorting of defective products.

[0044] Furthermore, in step S1, according to the shape, size and measurement requirements of the actual product, professional software is used to write the preset template information. The template information includes the standard size of the product, the location of the measurement point, the measurement range, and the key parameters of the measurement accuracy, ensuring that the template information accurately matches the actual product for subsequent comparison and analysis. After writing, the template information is verified to ensure its accuracy and applicability. In step S1, a machine learning algorithm can be introduced to automatically optimize the preset template information based on historical data to improve the accuracy and applicability of the template.

[0045] Furthermore, in step S2, during the scanning process, the height data of the product cross section and the encoder position information are recorded simultaneously to ensure that the parameters such as the scanning speed, laser intensity and sampling frequency are reasonably set to obtain high-quality measurement data, and the collected data is subjected to preliminary processing such as denoising or smoothing to ensure the accuracy and integrity of the data.

[0046] In step S2, according to product characteristics and measurement requirements, efficient scanning strategies are designed, such as spiral scanning, grid scanning, etc., to cover all key measurement areas. High-precision encoders are used to synchronously record the laser scanning position to ensure the synchronization and accuracy of data acquisition. The raw data is pre-processed, such as noise removal and data smoothing, to improve data quality.

[0047] In step S2, calibration and verification of the scanning device may be added to ensure the accuracy of the measurement data.

[0048] Furthermore, step S3 further includes the following steps:

[0049] S301, allocating the acquired data to each preset measurement template according to the encoder position information;

[0050] S302, using the accuracy of the encoder position information, accurately dividing the waveform into corresponding product waveform data;

[0051] S303: further verify and correct the segmented waveform data.

[0052] In step S301 , the encoder position information is used to accurately distribute the collected data to a preset measurement template, ensuring that each measurement point has corresponding data.

[0053] In steps S302 and S303, the waveform is segmented and verified. According to product characteristics and measurement requirements, the waveform data is accurately segmented, and a verification algorithm is applied to verify the accuracy of the segmentation result.

[0054] Furthermore, in step S4, a height and width filtering algorithm is applied to eliminate interference signals caused by the instrument, and an average value filtering method algorithm is used to further smooth the data to remove interference signals. At the same time, according to changes in product characteristics and measurement environment, the filtering parameters are dynamically adjusted to achieve the best filtering effect.

[0055] In order to further improve the filtering efficiency, a multi-dimensional filtering method is adopted, combining multiple dimensions such as height, width, and time, and applying advanced filtering algorithms (such as Kalman filtering, median filtering, etc.) to further remove interference signals.

[0056] Furthermore, step S5 further includes the following steps:

[0057] S501, calculating the rate of change of the waveform data by using a difference derivative algorithm, and searching for the peak and trough positions of the product;

[0058] S502, accurately locating specific locations of wave peaks and wave troughs according to preset template information;

[0059] S503. Calculate the height difference between the wave crest and the wave trough according to the positioning result to obtain the required dimension data, and apply the verification algorithm to verify the accuracy of the calculation result.

[0060] Based on the positioning results, key dimensions such as height difference and width are calculated, and the verification algorithm is applied to verify the accuracy of the calculation results.

[0061] Furthermore, in step S6, the data of different measurement lines are synthesized through additional templates, and the data of different measurement lines are efficiently fused to form a complete three-dimensional model of the product, the difference or height dimension is calculated, and multiple measurement areas are selected to apply the least squares method to calculate the flatness, parallelism and point-to-surface distance, and the results are verified and corrected. The calculation results are verified, and when abnormal values ​​or deviations are found, corrections or re-measurements are performed. The least squares method is a mathematical tool that is widely used in many disciplines of data processing such as error estimation, uncertainty, system identification, prediction, and forecasting. It is a mathematical optimization technique. It finds the best function match for the data by minimizing the sum of squares of the errors, and applies the least squares method to calculate parameters such as flatness, parallelism and point-to-surface distance, and verifies and corrects the calculation results. When abnormal values ​​or deviations are found, corrections or re-measurements are performed to ensure the accuracy and reliability of the data.

[0062] Furthermore, in step S7, the final result is synthesized including the product's size, geometrical quantity, and whether it is qualified, and the determination result and raw data are sent to the controller to realize automatic sorting of defective products, and at the same time, key information and data in the detection process are recorded for subsequent analysis and improvement;

[0063] Key information and data during the inspection process include product number, measurement time or defect reason.

[0064] Furthermore, in step S7, the determination result and the original data are output to the controller or display screen in an intuitive manner, so as to facilitate the operator to view and analyze.

[0065] Furthermore, in step S7, an intelligent sorting system is introduced to automatically classify products into different areas according to the detection results, thereby improving production efficiency.

[0066] At the same time, before the point laser product detection method of this application is officially carried out, the point laser equipment and measurement system need to be calibrated and verified to ensure the accuracy and reliability of the measurement results.

[0067] It also includes exception handling: during the detection process, if abnormal data or equipment failure occurs, the detection should be stopped immediately and appropriate handling measures should be taken.

[0068] The present invention is programmed with a preset measurement template according to the actual product and the corresponding measurement requirements. The height data and encoder position information of the product cross section are obtained by point laser dynamic scanning of the position to be measured on the product, and the obtained data are distributed to each measurement template. The waveform is accurately divided into corresponding product waveform data by the encoder position information. The size of the product is accurately measured by filtering algorithms such as height, width and average value, the rate of change is calculated by differential derivation, the peak and trough positions of the product are searched, and then the position to be measured of the product is located, the height difference is calculated, the data of different measurement lines are synthesized by additional templates, the flatness, parallelism and point-to-surface distance are calculated, and finally all calculated sizes are judged by tolerance, the final result is synthesized, and the result and data are sent to the controller to realize automatic sorting of defective products.

[0069] The point laser measurement method of the present invention can realize product parameter detection more comprehensively and efficiently, with higher measurement accuracy, and meet the needs of actual production. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the preferred embodiments, ordinary technicians in this field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be covered in the scope of the claims of the present invention.

[0070] The standard parts used in the present invention can all be purchased from the market, and special-shaped parts can be customized according to the description in the specification and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. Machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here.

[0071] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

Claims

1. A point laser product detection method, characterized in that: The following steps are involved: S1. Write a preset template. According to the actual product and the corresponding measurement requirements, write the preset template information for comparison and analysis of the actual measurement product; S2, point laser dynamic scanning and data acquisition, obtain the height data of the product cross section and the encoder position information by point laser dynamic scanning the required position of the product; S3, data allocation and waveform segmentation, allocate the acquired data to each measurement template, and accurately segment the waveform into corresponding product waveform data through the encoder position information; S4, filtering algorithm processing, through filtering algorithm to eliminate interference signals brought by the instrument, to achieve accurate measurement of product size; S5. Peak and trough positioning and height difference calculation: the height difference is calculated by the peak and trough positions of the product; S6, data synthesis and flatness and parallelism calculation, the data of different measurement lines are synthesized through additional templates to calculate flatness, parallelism and point-to-surface distance; S7, tolerance judgment and result output, the calculated dimensions are judged through tolerance, the final result is synthesized, and the result and data are sent to the controller to realize automatic sorting of defective products.

2. A point laser product detection method according to claim 1, characterized in that: In step S1, the template information includes the standard size of the product, the location of the measuring point, the measuring range, and key parameters of the measuring accuracy.

3. A point laser product detection method according to claim 1, characterized in that: In step S2, during the scanning process, the height data of the product cross section and the encoder position information are recorded simultaneously to ensure that the parameters such as the scanning speed, laser intensity and sampling frequency are set reasonably, and the collected data is preliminarily processed such as denoising or smoothing; In step S2, according to product characteristics and measurement requirements, efficient scanning strategies are designed, such as spiral scanning, grid scanning, etc., to cover all key measurement areas.

4. A point laser product detection method according to claim 1, characterized in that: Step S3 also includes the following steps: S301, allocating the acquired data to each preset measurement template according to the encoder position information; S302, using the accuracy of the encoder position information, accurately dividing the waveform into corresponding product waveform data; S303: further verify and correct the segmented waveform data.

5. The point laser product detection method according to claim 1, characterized in that: In step S4, the height and width filtering algorithms are applied to eliminate the interference signals brought by the instrument, and the average value filtering method algorithm is used to further smooth the data to remove the interference signals. At the same time, according to the changes in product characteristics and measurement environment, the filtering parameters are dynamically adjusted to achieve the best filtering effect.

6. A point laser product detection method according to claim 1, characterized in that: Step S5 also includes the following steps: S501, calculating the rate of change of the waveform data by using a difference derivative algorithm, and searching for the peak and trough positions of the product; S502, accurately locating specific locations of wave peaks and wave troughs according to preset template information; S503. Calculate the height difference between the wave crest and the wave trough according to the positioning result to obtain the required dimension data, and apply the verification algorithm to verify the accuracy of the calculation result.

7. A point laser product detection method according to claim 1, characterized in that: In step S6, the data of different measurement lines are synthesized through additional templates, the data of different measurement lines are efficiently fused to form a complete three-dimensional model of the product, the difference or height dimension is calculated, and multiple measurement areas are selected to apply the least squares method to calculate the flatness, parallelism and point-to-surface distance, and the results are verified and corrected. The calculation results are verified, and when abnormal values ​​or deviations are found, corrections or re-measurements are performed.

8. The point laser product detection method according to claim 1, characterized in that: In step S7, the final result is synthesized including the product's size, geometry, and whether it is qualified, and the judgment result and raw data are sent to the controller to realize automatic sorting of defective products. At the same time, the key information and data of the detection process are recorded for subsequent analysis and improvement; Key information and data during the inspection process include product number, measurement time or defect reason.

9. A point laser product detection method according to claim 8, characterized in that: In step S7, the determination result and raw data are output to the controller or display screen in an intuitive manner to facilitate viewing and analysis by operators.

10. A point laser product detection method according to claim 8, characterized in that: In step S7, an intelligent sorting system is introduced to automatically classify products into different areas according to the detection results to improve production efficiency.

Images