Automatic fitting method for guiding data to reject abnormal point locations

Abstract

The invention relates to the auxiliary technical field of the 3C manufacturing industry, and discloses a guide data elimination abnormal point location automatic fitting method, which comprises the following steps: scanning a standard block through line laser to obtain standard contour data, and storing the standard contour data as a standard template; performing full contour scanning on the to-be-processed product to obtain point location data of the scanned contour of the product; checking and comparing the product scanning contour point location data with the standard template point by point, setting a deviation threshold value, and eliminating abnormal point locations with deviation exceeding the deviation threshold value; according to the scanning track trend presented by the remaining effective point locations after the abnormal point locations are removed, new supplementary point locations are generated through a data fitting algorithm, and the new supplementary point locations and the remaining effective point locations jointly form real contour point location data of the product. According to the scheme, abnormal point positions are effectively and accurately removed, a real contour is fitted, and the dispensing and mounting accuracy of 3C products is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of auxiliary technology in the 3C manufacturing industry, specifically to a method for automatically fitting outdated data points. Background Technology

[0002] In the 3C manufacturing industry, the dispensing and mounting processes for mobile phone frames require extremely high positional accuracy. Line laser scanning technology is typically used to acquire the contour data of the mobile phone frame, guiding the dispensing or mounting equipment. However, in actual production, line laser scanning is susceptible to interference from several factors: first, obstructions or reflections from impurities, tooling fixtures, or other materials around the product; second, inconsistent reflective properties of the mobile phone frame material itself, leading to signal distortion in some areas; and third, noise interference from the scanning equipment itself or environmental noise. These interferences cause numerous abnormal points in the acquired contour data. Directly fitting data containing these abnormal points results in a deviation between the fitted contour and the actual product contour, leading to subsequent dispensing position misalignment, insufficient mounting accuracy, product defects, or even damage, severely impacting production efficiency and product quality.

[0003] In existing technologies, outlier handling in scanned data often employs simple threshold removal or local smoothing algorithms. While these methods can improve data quality to some extent when there are few outliers, they become problematic when there is significant surface interference and a large number of outliers. Simple threshold removal can easily delete valid outliers, while local smoothing algorithms can distort the contours, failing to accurately recreate the product's true outline and thus failing to meet the high-precision processing requirements of the 3C manufacturing industry. Therefore, a guided data-driven automatic outlier removal method is needed to accurately remove outliers. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a novel method for automatically fitting outlier points in guided data, which solves the problem that existing line laser scanning data processing methods cannot accurately remove outlier points when there are many outlier points.

[0005] To achieve the above objectives, the present invention employs the following technical solution: An automatic fitting method for guiding data to remove outliers, used to accurately remove outliers and fit contours on 3C products, includes a vision device, the vision device including a line laser, and the automatic fitting method for guiding data to remove outliers includes the following steps: Step S1: Establish a standard template, select a standard block that matches the specifications of the product to be processed, use a line laser to perform a full contour scan on the standard block, obtain the point data of the standard contour, and store the point data as a standard template. Step S2: Collect product data. Using the same line laser scanning parameters as in Step S1, perform a full contour scan of the product to be processed to obtain the point data of the product scan contour. Step S3: Remove abnormal points. The product scan outline point data obtained in step S2 is compared with the standard template established in step S1 point by point. A deviation threshold is set. When the deviation between a point in the product scan outline and the corresponding point in the standard template exceeds the deviation threshold, the point is determined to be an abnormal point and is automatically removed by the software algorithm. Step S4: Generate contour fitting. Based on the scanning trajectory trend of the remaining valid points after removing abnormal points in Step S3, new supplementary points are generated through a data fitting algorithm. The new supplementary points and the remaining valid points together constitute the true contour point data of the product.

[0006] Furthermore, in the aforementioned method for automatically fitting abnormal points by removing guided data, in step S1, the standard block is a standard 3C product frame reference component that is free from interference, reflective deviation, and noise.

[0007] Furthermore, in the aforementioned method for automatically fitting outlier points in the guided data, the path, scanning speed, and laser power parameters of the line laser scanning remain consistent in steps S1 and S2.

[0008] Furthermore, in the aforementioned method for automatically fitting outlier points in guided data, in step S3, the deviation threshold is determined by the dispersion analysis of the point data of the standard template, specifically 1.5-3 times the standard deviation of the point data of the standard template.

[0009] Furthermore, in the aforementioned method for automatically fitting outlier points in guided data, step S3 involves identifying outlier points using dynamic thresholds or statistical analysis models.

[0010] Furthermore, in the aforementioned method for automatically fitting outlier points in guided data, in step S4, the data fitting algorithm is a polynomial fitting algorithm, spline interpolation, or Bézier curve fitting algorithm.

[0011] Furthermore, in the aforementioned method for automatically fitting abnormal points by removing data from the guide data, in step S4, if continuous segment data is missing, compensation points can be generated by extrapolating the trends of adjacent points to ensure the continuity of the contour.

[0012] Compared with existing technologies, the advantages of this invention are that, by adopting the above-mentioned solution, it is applicable to the removal and automatic fitting of abnormal points in laser scanning contour data in 3C manufacturing. By pre-establishing a standard contour data template, the real-time scanning data is compared with the template to automatically identify and remove abnormal points caused by interference. Then, based on the effective data points, the true contour is restored through a fitting algorithm, which effectively improves the accuracy of the contour data, ensures the reliability of subsequent processes such as dispensing and mounting, greatly improves the equipment precision, and has great market application value. Attached Figure Description

[0013] Figure 1 This is a flowchart of an embodiment of the present invention for an automatic fitting method for removing outlier points from guided data; Figure 2 This is a schematic diagram of the abnormal point removal and contour fitting process in one embodiment of the present invention. Detailed Implementation

[0014] To facilitate understanding of the present invention by those skilled in the art, specific embodiments of the present invention are described below with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the present invention.

[0015] It should be noted that when a component is referred to as being "fixed to" another component, it can be directly attached to the other component or there may be an intervening component. When a component is referred to as being "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "mounted," "fixed," "front," "rear," and similar expressions used in this specification are for illustrative purposes only.

[0016] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0017] One embodiment of the present invention is as follows: Figure 1 , 2 As shown, the guided data outlier removal and automatic fitting method is used to accurately remove outliers and fit contours on 3C products. It includes a vision device, which includes a line laser. The guided data outlier removal and automatic fitting method includes the following steps: Step S1: Establish a standard template, select a standard block that matches the specifications of the product to be processed, use a line laser to perform a full contour scan on the standard block, obtain the point data of the standard contour, and store the point data as a standard template. Step S2: Collect product data. Using the same line laser scanning parameters as in Step S1, perform a full contour scan of the product to be processed to obtain the point data of the product scan contour. Step S3: Remove abnormal points. The product scan outline point data obtained in step S2 is compared with the standard template established in step S1 point by point. A deviation threshold is set. When the deviation between a point in the product scan outline and the corresponding point in the standard template exceeds the deviation threshold, the point is determined to be an abnormal point and is automatically removed by the software algorithm. Step S4: Generate contour fitting. Based on the scanning trajectory trend of the remaining valid points after removing abnormal points in Step S3, new supplementary points are generated through a data fitting algorithm. The new supplementary points and the remaining valid points together constitute the true contour point data of the product.

[0018] The automatic fitting method for removing abnormal points from the guided data in this embodiment is suitable for semiconductor tilt mounting and is applicable to the removal and automatic fitting of abnormal points in the laser scanning contour data in 3C manufacturing. By pre-establishing a standard contour data template, the real-time scanning data is compared with the template to automatically identify and remove abnormal points caused by interference. Then, based on the effective data points, the fitting algorithm restores the true contour, thereby effectively improving the accuracy of the contour data and ensuring the reliability of subsequent processes such as dispensing and mounting.

[0019] In this embodiment, in step S1, the standard block is a standard 3C product frame reference component that is free from interference, reflective deviation, and noise.

[0020] Specifically, a standard block with specifications exactly the same as the mobile phone frame to be processed is selected. This standard block must meet the requirements of no surface impurities, no reflective deviation, and no scanning noise, and is used as the contour reference part. A line laser scanning device is used to perform a full contour scan on the standard block. The scanning path covers all key contour areas of the mobile phone frame. During the scanning process, the three-dimensional coordinate data of each scanning point is recorded.

[0021] Specifically, scanning parameters include scanning speed, laser power, sampling frequency, etc., which can be preset according to product characteristics. A set of high-precision point data is selected, integrated, and used as a standard template, which is then stored in the system backend.

[0022] In this embodiment, the path, scanning speed, and laser power parameters of the line laser scanning remain consistent in steps S1 and S2.

[0023] Specifically, for each mobile phone frame to be processed, the same line laser scanning parameters and scanning path as when the standard template was created are used to perform a full contour scan and obtain the scan contour point data of the product in real time.

[0024] In this embodiment, in step S3, the deviation threshold is determined by the dispersion analysis of the point data of the standard template, specifically 1.5-3 times the standard deviation of the point data of the standard template.

[0025] Specifically, each product under test is subjected to line laser scanning, and the real-time data is compared point by point with the coordinates or feature values ​​of the corresponding points in the standard template to calculate the deviation.

[0026] In this embodiment, in step S3, anomaly identification uses dynamic thresholds or statistical analysis models.

[0027] Specifically, the real-time collected product scan outline point data is compared and verified point by point with the standard template stored in the software backend. By analyzing the dispersion of the point data in the standard template, the standard deviation of the standard template point data is calculated, and 1.5-3 times the standard deviation is set as the deviation threshold. For each point in the product scan outline, the three-dimensional coordinate deviation between it and the corresponding point in the standard template is calculated. If the deviation exceeds the set deviation threshold, the point is determined to be an abnormal point and is automatically removed from the dataset by the software algorithm.

[0028] In this embodiment, in step S4, the data fitting algorithm is a polynomial fitting algorithm, spline interpolation, or Bézier curve fitting algorithm.

[0029] Specifically, based on the remaining valid points after removing abnormal points, the scanning trajectory trend is analyzed. According to the curvature characteristics of the product contour, a new supplementary point is generated based on the distribution law of the valid points using a polynomial fitting algorithm. The supplementary points and the remaining valid points together constitute complete and continuous real contour point data of the product, which guides the dispensing equipment or mounting equipment to perform operations.

[0030] In this embodiment, if continuous segment data is missing in step S4, compensation points can be generated by extrapolating the trends of adjacent points to ensure the continuity of the contour.

[0031] Specifically, based on the remaining valid points, a trend fitting algorithm is used to reconstruct the complete contour trajectory. The fitted contour data is then converted into instructions that can be read by the dispensing or mounting equipment, driving the actuator to complete the processing operation.

[0032] The automatic fitting method for removing outlier points in the guided data described in this embodiment establishes a standard template as a benchmark, adopts a point-by-point verification and comparison method, and combines a deviation threshold determined based on the dispersion of standard data. This can accurately distinguish between valid points and outlier points, avoids the accidental deletion of valid points or the omission of outlier points, and can achieve accurate removal even when there are many outlier points. The fitting is based on the trajectory trend of the effective points after removing outliers, and the contour is improved by adding points to ensure that the fitted contour is highly consistent with the real contour of the product, effectively solving the problem of contour fitting distortion in existing methods. Precise contour data can provide reliable guidance for dispensing and placement equipment, significantly reducing defects such as dispensing deviation and placement misalignment, reducing product damage rate, and improving production efficiency and product qualification rate. By adjusting the deviation threshold and fitting algorithm type, it can be adapted to the processing of frame scanning data of 3C products with different specifications and materials, and has a wide range of application scenarios.

[0033] The aforementioned method for automatically fitting outlier points using guided data aims to solve the problems of subsequent dispensing and mounting defects caused by abnormal line laser scanning data in the 3C manufacturing industry (such as mobile phone frame processing). Its working principle is as follows: First, select a standard 3C product frame reference component that is free from interference, reflective deviation, and noise. Use a line laser scanning device to scan its entire outline, accurately collect a series of outline point position data, and save them to the software system. Secondly, the product outline is scanned using the same line laser to obtain a set of real-time outline point data. The system compares and verifies this set of real-time data with the standard template data in the background point by point, and calculates the deviation of each corresponding point in the coordinate position. Due to the interference of surrounding materials (such as debris, foreign matter), uneven reflection of the product surface or its own noise, some abnormal points with large deviations from the standard outline will be mixed in the real-time scanned data. Subsequently, the system sets a difference threshold through an algorithm. When the deviation between a data point and the corresponding point in the standard template exceeds this threshold, it is marked as an "abnormal point".

[0034] Next, the system automatically removes data points marked as abnormal from the current dataset. Based on the remaining valid data points after noise removal and the scanning trajectory trends formed by these valid data points, the system uses a fitting algorithm to intelligently "fill in" or "correct" the gaps in the removed data points, generating a new set of corrected contour data points. This new data should represent "new contour data points" that are closer to the actual physical contour of the product after eliminating interference.

[0035] This method can reliably reconstruct the true contour line even when there is a lot of interference on the product surface and a considerable number of outliers.

[0036] This embodiment uses laser scanning data processing of the mobile phone frame line as an example to illustrate the implementation process of the guided data removal and automatic fitting method for abnormal points: The first step involves selecting a standard mobile phone frame component (free from surface scratches and impurities, with uniform reflective properties). Using a line laser scanning device, the scanning speed is set to 5mm / s, the laser power to 30mW, and the sampling frequency to 1000Hz. A full contour scan of the standard component is performed, acquiring 3D coordinate data (X, Y, Z) for 1000 scanning points. This data is stored in the software backend as a standard template. By calculating the standard deviation of the standard template point data, the standard deviation in the Z-axis direction is found to be 0.02mm. A deviation threshold of 0.04mm (i.e., twice the standard deviation) is set.

[0037] The second step involves selecting a mobile phone frame of the same model to be processed and performing a full contour scan of the frame using the same scanning parameters as in the previous step (scanning speed 5mm / s, laser power 30mW, sampling frequency 1000Hz) to obtain 1000 original scan point data.

[0038] The third step involves comparing the original scanned point data of the frame to be processed with the standard template point by point, and calculating the deviation value of each point in the Z-axis direction. Statistically, 86 points had a deviation value exceeding 0.04mm, which were identified as abnormal points. These 86 abnormal points were automatically removed by the software algorithm, leaving 914 valid points.

[0039] The fourth step involves using a third-order polynomial fitting algorithm to generate 86 supplementary points based on the trajectory trends of the remaining 914 valid points. These supplementary points, together with the valid points, constitute the complete outline data of the phone's mid-frame. This outline data is then sent to the dispensing equipment to guide the dispensing operation.

[0040] Testing showed that the deviation between the contour data obtained by the method in this embodiment and the actual contour of the mobile phone frame was no more than 0.01mm. The accuracy of the glue dispensing position was improved by 40% compared with the existing method, and the product defect rate was reduced from 3.2% to 0.5%, which significantly improved production quality and efficiency.

[0041] It should be noted that the above-mentioned technical features can be combined with each other to form various embodiments not listed above, all of which are considered to be within the scope of this invention specification; and, for those skilled in the art, improvements or modifications can be made based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A guided data removal and automatic fitting method for outlier points, used to accurately remove outliers and fit contours on 3C products, characterized in that: The method includes a vision device, which includes a line laser, and the automatic fitting method for removing outlier points from the guided data includes the following steps: Step S1: Establish a standard template, select a standard block that matches the specifications of the product to be processed, use a line laser to perform a full contour scan on the standard block, obtain the point data of the standard contour, and store the point data as a standard template. Step S2: Collect product data. Using the same line laser scanning parameters as in Step S1, perform a full contour scan of the product to be processed to obtain the point data of the product scan contour. Step S3: Remove abnormal points. The product scan outline point data obtained in step S2 is compared with the standard template established in step S1 point by point. A deviation threshold is set. When the deviation between a point in the product scan outline and the corresponding point in the standard template exceeds the deviation threshold, the point is determined to be an abnormal point and is automatically removed by the software algorithm. Step S4: Generate contour fitting. Based on the scanning trajectory trend of the remaining valid points after removing abnormal points in Step S3, new supplementary points are generated through a data fitting algorithm. The new supplementary points and the remaining valid points together constitute the true contour point data of the product.

2. The method for automatically fitting outlier points in guided data according to claim 1, characterized in that, In step S1, the standard block is a standard 3C product frame reference component that is free from interference, reflective deviation, and noise.

3. The method for automatically fitting outlier points in guided data according to claim 1, characterized in that, In steps S1 and S2, the path, scanning speed, and laser power parameters of the line laser scanning remain consistent.

4. The method for automatically fitting outlier points in guided data according to claim 1, characterized in that, In step S3, the deviation threshold is determined by the dispersion analysis of the point data of the standard template, specifically 1.5-3 times the standard deviation of the point data of the standard template.

5. The method for automatically fitting outlier points in guided data according to claim 4, characterized in that, In step S3, anomaly identification uses dynamic thresholds or statistical analysis models.

6. The method for automatically fitting outlier points in guided data according to claim 1, characterized in that, In step S4, the data fitting algorithm is a polynomial fitting algorithm, spline interpolation, or Bézier curve fitting algorithm.

7. The method for automatically fitting outlier points in guided data according to claim 6, characterized in that, In step S4, if continuous segment data is missing, compensation points can be generated by extrapolating the trends of adjacent points to ensure the continuity of the outline.

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