Online detection method and system for high-purity quartz tube

Abstract

The invention relates to the technical field of quartz material detection, in particular to a high-purity quartz tube online detection method and system.The method comprises the steps that an online detection platform is built, and appearance images, internal defect signals and component content data of a high-purity quartz tube are collected in real time through a multi-dimensional sensing unit and sent to a data processing center; the data processing center preprocesses the collected data and eliminates environmental interference and equipment errors; based on a deep learning model and a feature fusion algorithm, defect identification, purity analysis and size precision detection are carried out on the preprocessed data; and generating a quartz tube quality evaluation report, and carrying out real-time marking and sorting control on unqualified products. By integrating a multi-sensor detection technology, an intelligent data analysis algorithm and online closed-loop control logic, real-time monitoring, accurate detection and quick response in the production process of the high-purity quartz tube are realized, and the product quality qualification rate and the production efficiency of the high-purity quartz tube are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of quartz material testing technology, specifically to an online testing method and system for high-purity quartz tubes. Background Technology

[0002] High-purity quartz tubes are widely used in high-end fields such as semiconductor chip manufacturing, optical instrument processing, and photovoltaic cell production due to their excellent properties, including high temperature resistance, corrosion resistance, good light transmittance, and strong chemical stability. These applications place extremely high demands on the purity of quartz tubes (total impurity content is typically required to be ≤5ppm), appearance quality (no obvious scratches or dents), internal defects (no cracks or bubbles), and dimensional accuracy (uniformity of inner diameter, outer diameter, and wall thickness).

[0003] Currently, the methods for testing high-purity quartz tubes have many shortcomings: The detection method is mainly offline sampling inspection, which cannot achieve real-time monitoring during the production process, resulting in a large number of unqualified products flowing into subsequent processes, increasing production losses and costs; Appearance inspection mainly relies on manual visual inspection, which is highly subjective, inefficient, and difficult to detect tiny scratches (≤0.1mm) and minor stains, resulting in a high rate of missed detection. Internal defect detection often uses a single ultrasonic testing technique, which is not accurate enough in identifying tiny bubbles (≤0.3mm) and shallow surface cracks, and is easily affected by environmental noise. Purity detection mainly uses inductively coupled plasma mass spectrometry (ICP-MS), which has a long detection cycle (more than 30 minutes for a single detection) and cannot meet the needs of online rapid detection. Dimensional accuracy inspection often uses contact measuring tools, which can easily damage the surface of quartz tubes and has low measurement efficiency, making it impossible to match the speed of the production line.

[0004] Therefore, there is an urgent need for a high-purity quartz tube online testing technology that can achieve real-time, accurate, multi-dimensional, and non-contact testing to solve the problems of offline testing lag, low accuracy, poor efficiency, and easy damage to products in existing testing methods. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, this invention provides an online detection method and system for high-purity quartz tubes. By integrating multi-sensor detection technology, intelligent data analysis algorithms, and online closed-loop control logic, it achieves real-time, accurate, and non-destructive testing during the production process of high-purity quartz tubes.

[0006] This invention is achieved through the following technical solution: A method for online detection of high-purity quartz tubes is provided, the method comprising the following steps: Step S10: Build an online inspection platform and deploy multi-dimensional sensing units at key inspection nodes of the quartz tube production line to collect real-time appearance image data, internal defect sensing signals and chemical composition content data of high-purity quartz tubes, and send the collected data to the data processing center synchronously through a high-speed communication link. Step S20: After receiving the data, the data processing center performs targeted preprocessing on different types of test data to eliminate environmental interference, equipment system errors and data redundancy, and obtain standardized test data. Step S30: Based on a deep learning model, the appearance image data and internal defect signals in the standardized inspection data are fused and analyzed to identify surface defects, internal cracks and bubbles in the quartz tube. The content of impurity elements in the quartz tube is detected and the purity is determined by combining laser-induced breakdown spectroscopy with characteristic spectral analysis. The dimensions of the inner diameter, outer diameter, wall thickness and straightness of the quartz tube are detected based on machine vision measurement algorithms. Step S40: The data processing center integrates defect identification results, purity test data, and dimensional accuracy data. Based on the preset high-purity quartz tube quality standards, it generates a real-time quality assessment report, triggers an online sorting signal for non-conforming products, controls the sorting device to automatically separate non-conforming products, and simultaneously synchronizes the test data and quality report to the production control platform.

[0007] Preferably, the multi-dimensional sensing unit in step S10 includes an appearance inspection component, an internal defect detection component, and a composition detection component. The appearance inspection component includes a high-definition industrial camera, a ring LED light source, and a polarizing filter. A multi-angle light source layout eliminates surface reflection interference from the quartz tube. The polarizing filter filters stray light, ensuring the clarity of scratches, stains, and other defects in the appearance image. The internal defect detection component includes an ultrasonic flaw detector, an infrared thermal imager, and a laser interferometer. It is attached to the quartz tube surface with a coupling agent to collect ultrasonic reflection signals from internal cracks and bubbles in real time. The ultrasonic flaw detector transmits and receives ultrasonic... The system employs several technologies: an acoustic signal detector to detect the location, size, and morphology of internal defects such as cracks and bubbles in quartz tubes, and to identify defect features based on amplitude and phase changes of ultrasonic reflected signals; an infrared thermal imager to detect abnormal heat conduction caused by defects inside the quartz tube, locating shallow surface cracks and micro-impurities by measuring temperature distribution differences; a laser interferometer to generate interference fringes by emitting laser beams, analyzing the degree of fringe distortion to identify areas of non-uniform refractive index inside the quartz tube, and accurately identifying micro-bubbles, impurity agglomerations, and hidden cracks; and a laser-induced breakdown spectroscopy (LAS) component to excite micro-area plasma on the surface of the quartz tube and collect characteristic spectral data.

[0008] Preferably, the step S20, which involves targeted preprocessing of different types of detection data, includes: Preprocessing of appearance images: Denoising, image enhancement, and region of interest extraction are performed on appearance images acquired by high-definition industrial cameras; Sensor signal preprocessing: Filtering, detrending, and peak extraction are performed on the signals acquired by the ultrasonic flaw detection sensor; Spectral data preprocessing: Background subtraction, spectral line smoothing, and normalization are performed on the laser-induced breakdown spectral data.

[0009] Preferably, the step S30, which involves fusing and analyzing the appearance image data and internal defect signals in the standardized inspection data based on a deep learning model, includes: Defect feature extraction: Based on the improved YOLOv9 deep learning model, feature extraction is performed on the preprocessed appearance image to identify surface defects such as scratches, pits and stains on the quartz tube surface. The location, size, shape and grayscale features of the defects are extracted. The preprocessed ultrasonic signal and infrared thermal imaging data are fused to extract feature parameters such as ultrasonic reflection intensity, depth and area of ​​abnormal heat conduction region of internal defects. Defect Classification and Judgment: A multi-feature fusion classifier is constructed to fuse surface defect features and internal defect features. The defect type is classified by support vector machine algorithm, including surface scratches, surface pits, internal cracks, internal bubbles and impurity inclusions. A defect judgment threshold is set, and defects exceeding the threshold are judged as defects exceeding the standard. Purity testing: Based on laser-induced breakdown spectral data, the characteristic spectral line intensities of impurity elements in the quartz tube are extracted, and the content of each impurity element is calculated by calibration curve method; Dimensional accuracy inspection: Based on machine vision measurement algorithms, the inner diameter, outer diameter, wall thickness, and straightness of the quartz tube are calculated by combining the outline edge of the quartz tube in the appearance image with the distance data collected by the laser interferometer.

[0010] Preferably, the calibration curve establishment process includes preparing a series of quartz standard samples with known impurity element contents, collecting the characteristic spectral intensity of each standard sample using a laser-induced breakdown spectrometer, and using the impurity element content as the abscissa and the characteristic spectral intensity as the ordinate to fit the calibration curve using the least squares method.

[0011] Preferably, in step S40, the quality assessment report includes the product number, testing time, testing items, various testing results, qualification status judgment, and explanation of non-conformities. The testing items include surface defects, internal defects, purity, and dimensional accuracy. The preset high-purity quartz tube quality standards are as follows: total impurity content ≤ 5 ppm; no excessive surface defects, scratches ≤ 0.1 mm × 0.05 mm, pits ≤ 0.2 mm × 0.1 mm; no excessive internal defects, cracks ≤ 5 mm × 0.03 mm, bubbles ≤ 0.3 mm and ≤ 3 per 10 cm, no impurities; dimensional accuracy meets the following requirements: inner diameter error ± 0.01 mm, outer diameter error ± 0.01 mm, wall thickness uniformity error ≤ 0.02 mm, and straightness error ≤ 0.1 mm / m.

[0012] When the test results meet all the above standards, the product is judged as qualified and the production line continues to transport it normally. If any one of the standards is not met, the product is judged as unqualified, and the data processing center immediately sends a sorting signal to the sorting device. The sorting device adopts a pneumatic pushing structure with a response time of ≤0.5s. Based on the position signal of the unqualified product, it accurately pushes it into the unqualified product collection box, and marks the unqualified item in the quality assessment report for subsequent traceability and process optimization. The test data and quality report are synchronized to the production control platform through a high-speed communication link. The platform displays the test status of each quartz tube on the production line in real time and generates quality statistical reports, including pass rate, distribution of unqualified items, etc., providing data support for production process adjustments.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes an online detection system for high-purity quartz tubes, wherein the online detection system for high-purity quartz tubes comprises: Online testing platform construction module: Used to build an online testing platform, deploy multi-dimensional sensing units at key testing nodes of the quartz tube production line, collect appearance image data, internal defect sensing signals and chemical composition content data of high-purity quartz tubes in real time, and send the collected data to the data processing center synchronously through the communication link; Data preprocessing module: After receiving data, the data processing center performs targeted preprocessing on different types of test data to eliminate environmental interference, equipment system errors and data redundancy, and obtain standardized test data. Multi-dimensional detection and analysis module: It is used to fuse and analyze the appearance image data and internal defect signals in the standardized detection data based on the deep learning model, identify surface defects, internal cracks and bubbles in quartz tubes, detect the content of impurity elements and determine the purity in quartz tubes by combining laser-induced breakdown spectroscopy technology with characteristic spectral analysis, and detect the dimensions of inner diameter, outer diameter, wall thickness and straightness of quartz tubes based on machine vision measurement algorithms. Quality assessment and sorting control module: This module integrates defect identification results, purity test data, and dimensional accuracy data in the data processing center. Based on the preset high-purity quartz tube quality standards, it generates a real-time quality assessment report, triggers an online sorting signal for non-conforming products, controls the sorting device to automatically separate non-conforming products, and simultaneously synchronizes the test data and quality report to the production management platform.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes an online detection device for high-purity quartz tubes. The device includes: a memory, a processor, and an online detection program for high-purity quartz tubes stored in the memory and executable on the processor. The online detection program for high-purity quartz tubes implements the steps of the online detection method for high-purity quartz tubes as described above.

[0015] In addition, to achieve the above objectives, the present invention also provides a computer program product, which includes an online detection program for high-purity quartz tubes. When the online detection program for high-purity quartz tubes is executed by a processor, it implements the online detection method for high-purity quartz tubes as described above.

[0016] The advantages and effects of this invention are: This invention proposes an online inspection method and system for high-purity quartz tubes. By establishing an online inspection platform that operates synchronously with the production line, it achieves a transformation from offline sampling inspection to online full-volume inspection. This enables real-time detection of defective products during quartz tube production, preventing them from flowing into subsequent processes and significantly reducing production losses and costs. Employing multi-dimensional sensing units working collaboratively, combined with deep learning models and multi-feature fusion algorithms, it achieves multi-dimensional synchronous detection of surface defects, internal defects, purity, and dimensional accuracy, effectively overcoming the limitations of traditional single-detection technologies and significantly improving inspection accuracy. Using non-contact inspection methods, including optical, ultrasonic, and spectral non-destructive testing technologies, it avoids damage to the quartz tube surface caused by contact measurements, ensuring product quality. The entire inspection process is highly automated, with inspection efficiency matching the production line speed, requiring no manual intervention, thus reducing labor costs and human error. Through the linkage between the data processing center and the production control platform, inspection results can be fed back in real time, providing data support for production process adjustments and forming a closed-loop control of inspection, evaluation, and regulation. This helps to continuously optimize the production process and improve the overall product quality pass rate of high-purity quartz tubes. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an online detection method for high-purity quartz tubes according to the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an online detection system for high-purity quartz tubes according to the present invention.

[0020] Figure 3 This is a schematic block diagram of an online detection electronic device for high-purity quartz tubes according to the present invention. Detailed Implementation

[0021] 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.

[0022] like Figure 1 As shown, in one embodiment of the present invention, an online detection method for high-purity quartz tubes includes the following steps: Step S10: Build an online inspection platform and deploy multi-dimensional sensing units at key inspection nodes of the quartz tube production line to collect real-time appearance image data, internal defect sensing signals and chemical composition data of high-purity quartz tubes, and send the collected data to the data processing center synchronously through a high-speed communication link.

[0023] Specifically, the multi-dimensional sensing unit in step S10 includes an appearance inspection component, an internal defect detection component, and a composition detection component. The appearance inspection component includes a high-definition industrial camera, a ring LED light source, and a polarizing filter. The high-definition industrial camera has a resolution of at least 20 megapixels and a frame rate of at least 30fps. The ring LED light source uses a cold light source with a color temperature of 5500K. A multi-angle light source layout eliminates surface reflection interference from the quartz tube. The polarizing filter filters stray light, ensuring the clarity of scratches, stains, and other defects in the appearance image. The internal defect detection component includes an ultrasonic flaw detector, an infrared thermal imager, and a laser interferometer. The ultrasonic flaw detector has a detection frequency of 2-10MHz and is bonded to the surface of the quartz tube using a coupling agent. It collects ultrasonic reflection signals from internal cracks and bubbles in real time. The ultrasonic flaw detector detects internal cracks and bubbles in the quartz tube by emitting and receiving ultrasonic signals. The location, size, and morphology of defects such as bubbles are identified based on the amplitude and phase changes of ultrasonic reflection signals. An infrared thermal imager with a temperature resolution ≤0.05℃ is used to detect abnormal heat conduction caused by defects inside the quartz tube, locating shallow surface cracks and micro-impurities through temperature distribution differences. A laser interferometer generates interference fringes by emitting a laser beam; its measurement accuracy is ±0.1μm. Interference fringes are used to analyze the non-uniform refractive index regions inside the quartz tube, and the degree of fringe distortion is used to accurately identify internal micro-bubbles, impurity agglomerations, and hidden cracks. The component detection component is a laser-induced breakdown spectroscopy (LAS) detector with a laser output energy of 100-500mJ, a pulse width of 5-10ns, and a spectral detection range of 200-800nm. This detector is used to excite micro-area plasma on the surface of the quartz tube and collect characteristic spectral data.

[0024] Step S20: After receiving the data, the data processing center performs targeted preprocessing on different types of test data to eliminate environmental interference, equipment system errors and data redundancy, and obtain standardized test data.

[0025] Specifically, step S20 includes the following steps for targeted preprocessing of different types of detection data: Preprocessing of appearance images: Denoising, image enhancement and region of interest extraction are performed on appearance images acquired by high-definition industrial cameras. Denoising is performed by bilateral filtering algorithm to remove Gaussian noise and salt-and-pepper noise in the image. Image enhancement is performed by adaptive histogram equalization to improve the contrast between defect areas and background. Region of interest extraction is based on the geometric contour of quartz tube, and irrelevant background areas in the image are cropped to retain only the main body area of ​​quartz tube, thereby reducing the amount of data processing. Sensor signal preprocessing: The signals acquired by the ultrasonic flaw detection sensor are filtered, detrended, and peak extracted. Wavelet threshold filtering algorithm is used to remove environmental noise and equipment interference signals in the ultrasonic signal. Detrending processing eliminates signal baseline drift through linear fitting. Peak extraction is used to locate feature points of ultrasonic reflection signals and corresponding internal defect location information. Temperature calibration and artifact removal are performed on the infrared thermal imaging data. Temperature calibration is completed based on blackbody radiation source. Median filtering is used to remove false defect signals in the thermal imaging image. Spectral data preprocessing: Background subtraction, spectral smoothing, and normalization are performed on the laser-induced breakdown spectral data. Background subtraction uses a polynomial fitting algorithm to eliminate continuous spectral background. Spectral smoothing uses the Savitzky-Golay filtering algorithm to reduce spectral noise. Normalization is based on the characteristic spectral intensity of Si element in the quartz tube to eliminate detection errors caused by laser energy fluctuations.

[0026] Step S30: Based on a deep learning model, the appearance image data and internal defect signals in the standardized inspection data are fused and analyzed to identify surface defects, internal cracks and bubbles in the quartz tube. The content of impurity elements in the quartz tube is detected and the purity is determined by laser-induced breakdown spectroscopy combined with characteristic spectral analysis. The dimensions of the inner diameter, outer diameter, wall thickness and straightness of the quartz tube are detected based on machine vision measurement algorithms.

[0027] Specifically, step S30, which involves fusing and analyzing the appearance image data and internal defect signals in the standardized inspection data based on a deep learning model, includes: Defect feature extraction: Based on the improved YOLOv9 deep learning model, feature extraction is performed on the preprocessed appearance image to identify surface defects such as scratches, pits and stains on the quartz tube surface. The location, size, shape and grayscale features of the defects are extracted. The preprocessed ultrasonic signal and infrared thermal imaging data are fused to extract feature parameters such as ultrasonic reflection intensity, depth and area of ​​abnormal heat conduction region of internal defects. Defect Classification and Judgment: A multi-feature fusion classifier is constructed to fuse surface defect features and internal defect features. The defect type is classified by support vector machine algorithm, including surface scratches, surface pits, internal cracks, internal bubbles and impurity inclusions. A defect judgment threshold is set, and defects exceeding the threshold are judged as defects exceeding the standard. For example, when the maximum size of the surface defect exceeds 0.1 mm, or the length of the internal crack exceeds 5 mm, or the diameter of the internal bubble exceeds 0.3 mm, it is judged as a defect exceeding the standard. Purity testing: Based on laser-induced breakdown spectral data, the characteristic spectral line intensities of impurity elements in the quartz tube are extracted, including Al, Fe, Ca, Mg and Ti, etc. The content of each impurity element is calculated by calibration curve method. When the total impurity content exceeds 5 ppm, it is judged as unqualified in purity. Dimensional accuracy inspection: Based on machine vision measurement algorithms, the inner diameter, outer diameter, wall thickness, and straightness of the quartz tube are calculated by combining the outline edge of the quartz tube in the appearance image with the distance data collected by the laser interferometer. The measurement error of the inner diameter and outer diameter is controlled within ±0.01mm, the wall thickness uniformity error is ≤0.02mm, and the straightness error is ≤0.1mm / m. If the error exceeds the above range, the dimensional accuracy is determined to be unqualified.

[0028] Specifically, the calibration curve establishment process includes preparing a series of quartz standard samples with known impurity element contents, ranging from 0.1 to 10 ppm; collecting the characteristic spectral intensities of each standard sample using a laser-induced breakdown spectroscopy (LAS) instrument; plotting the impurity element content on the x-axis and the characteristic spectral intensities on the y-axis; and using the least squares method to fit the calibration curve, with a goodness of fit R0. 2 ≥0.995.

[0029] Step S40: The data processing center integrates defect identification results, purity test data, and dimensional accuracy data. Based on the preset high-purity quartz tube quality standards, it generates a real-time quality assessment report, triggers an online sorting signal for non-conforming products, controls the sorting device to automatically separate non-conforming products, and simultaneously synchronizes the test data and quality report to the production control platform.

[0030] Specifically, in step S40, the quality assessment report includes the product number, testing time, testing items, test results, pass / fail status determination, and explanation of non-conformities. The testing items include surface defects, internal defects, purity, and dimensional accuracy. The preset quality standards for high-purity quartz tubes are as follows: total impurity content ≤ 5 ppm; no excessive surface defects, scratches ≤ 0.1 mm × 0.05 mm, pits ≤ 0.2 mm × 0.1 mm; no excessive internal defects, cracks ≤ 5 mm × 0.03 mm, bubbles ≤ 0.3 mm and ≤ 3 per 10 cm, no impurities; dimensional accuracy meets the following requirements: inner diameter error ± 0.01 mm, outer diameter error ± 0.01 mm, wall thickness uniformity error ≤ 0.02 mm, and straightness error ≤ 0.1 mm / m.

[0031] When the test results meet all the above standards, the product is judged as qualified and the production line continues to transport it normally. If any one of the standards is not met, the product is judged as unqualified, and the data processing center immediately sends a sorting signal to the sorting device. The sorting device adopts a pneumatic pushing structure with a response time of ≤0.5s. Based on the position signal of the unqualified product, it accurately pushes it into the unqualified product collection box, and marks the unqualified item in the quality assessment report for subsequent traceability and process optimization. The test data and quality report are synchronized to the production control platform through a high-speed communication link. The platform displays the test status of each quartz tube on the production line in real time and generates quality statistical reports, including pass rate, distribution of unqualified items, etc., providing data support for production process adjustments.

[0032] In addition, such as Figure 2 As shown, in one embodiment of the present invention, an online detection system for high-purity quartz tubes is proposed. The online detection system for high-purity quartz tubes includes: Online testing platform construction module: Used to build an online testing platform, deploy multi-dimensional sensing units at key testing nodes of the quartz tube production line, collect appearance image data, internal defect sensing signals and chemical composition content data of high-purity quartz tubes in real time, and send the collected data to the data processing center synchronously through a high-speed communication link; Data preprocessing module: After receiving data, the data processing center performs targeted preprocessing on different types of test data to eliminate environmental interference, equipment system errors and data redundancy, and obtain standardized test data. Multi-dimensional detection and analysis module: It is used to fuse and analyze the appearance image data and internal defect signals in the standardized detection data based on the deep learning model, identify surface defects, internal cracks and bubbles in quartz tubes, detect the content of impurity elements and determine the purity in quartz tubes by combining laser-induced breakdown spectroscopy technology with characteristic spectral analysis, and detect the dimensions of inner diameter, outer diameter, wall thickness and straightness of quartz tubes based on machine vision measurement algorithms. Quality assessment and sorting control module: This module integrates defect identification results, purity test data, and dimensional accuracy data in the data processing center. Based on the preset high-purity quartz tube quality standards, it generates a real-time quality assessment report, triggers an online sorting signal for non-conforming products, controls the sorting device to automatically separate non-conforming products, and simultaneously synchronizes the test data and quality report to the production management platform.

[0033] This application provides an online detection system for high-purity quartz tubes, employing an online detection method for high-purity quartz tubes as described in the above embodiments. This system solves the technical problems of low efficiency, large errors, and inability to perform online synchronous detection using traditional methods. Compared with the prior art, the beneficial effects of the online detection system for high-purity quartz tubes provided in this application are the same as those of the online detection method for high-purity quartz tubes provided in the above embodiments. Furthermore, other technical features of the online detection system for high-purity quartz tubes are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0034] This application provides an online testing device for high-purity quartz tubes. The online testing device for high-purity quartz tubes includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform an online testing method for high-purity quartz tubes as described in Embodiment 1 above.

[0035] like Figure 3 As shown in the illustration, in one embodiment of the present invention, a structural schematic diagram of a high-purity quartz tube online testing device suitable for implementing the embodiments of this application is presented. The high-purity quartz tube online testing device in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The online testing device for high-purity quartz tubes shown is merely an example and should not be construed as limiting the functionality and scope of application of the embodiments in this application.

[0036] Figure 3The illustrated online testing device for high-purity quartz tubes may include a processor 1001 (e.g., a central processing unit, graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a machine-readable storage medium (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the online testing device for high-purity quartz tubes. The processor 1001, the read-only memory 1002, and the machine-readable storage medium 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and a communication unit 1009. Communication unit 1009 allows a high-purity quartz tube online testing device to exchange data wirelessly or via wired communication with other devices. Although an online testing device for high-purity quartz tubes with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0037] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication unit, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processor 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0038] This application provides an online testing device for high-purity quartz tubes, employing an online testing method for high-purity quartz tubes as described in the above embodiments. This method solves the technical problems of low efficiency, large errors, and inability to perform online synchronous testing in traditional methods. Compared with the prior art, the beneficial effects of the online testing device for high-purity quartz tubes provided in this application are the same as those of the online testing method for high-purity quartz tubes provided in the above embodiments. Furthermore, other technical features of this online testing device for high-purity quartz tubes are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0039] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0040] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the online detection method for high-purity quartz tubes as described above.

[0041] The computer program product provided in this application can solve the technical problems of low efficiency, large error, and inability to perform online synchronous detection in traditional detection methods. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the online detection method for high-purity quartz tubes provided in the above embodiments, and will not be repeated here.

[0042] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for online detection of high-purity quartz tubes, characterized in that, The method includes the following steps: Step S10: Build an online inspection platform and deploy multi-dimensional sensing units at key inspection nodes of the quartz tube production line to collect real-time appearance image data, internal defect sensing signals and chemical composition content data of high-purity quartz tubes, and send the collected data to the data processing center through the communication link. Step S20: After receiving the data, the data processing center performs targeted preprocessing on different types of test data to eliminate environmental interference, equipment system errors and data redundancy, and obtain standardized test data. Step S30: Based on a deep learning model, the appearance image data and internal defect signals in the standardized inspection data are fused and analyzed to identify surface defects, internal cracks and bubbles in the quartz tube. The content of impurity elements in the quartz tube is detected and the purity is determined by combining laser-induced breakdown spectroscopy with characteristic spectral analysis. The dimensions of the inner diameter, outer diameter, wall thickness and straightness of the quartz tube are detected based on machine vision measurement algorithms. Step S40: The data processing center integrates defect identification results, purity test data, and dimensional accuracy data. Based on the preset high-purity quartz tube quality standards, it generates a real-time quality assessment report, triggers an online sorting signal for non-conforming products, controls the sorting device to automatically separate non-conforming products, and simultaneously synchronizes the test data and quality report to the production control platform.

2. The online detection method for high-purity quartz tubes according to claim 1, characterized in that, The multi-dimensional sensing unit in step S10 includes an appearance inspection component, an internal defect detection component, and a composition detection component. The appearance inspection component includes an industrial camera, a ring LED light source, and a polarizing filter. The internal defect detection component includes an ultrasonic flaw detector, an infrared thermal imager, and a laser interferometer. The ultrasonic flaw detector detects the location, size, and shape of cracks and bubbles inside the quartz tube by emitting and receiving ultrasonic signals, and identifies defect features based on the amplitude and phase changes of the ultrasonic reflected signals. The infrared thermal imager is used to detect abnormal heat conduction caused by defects inside the quartz tube. The laser interferometer forms interference fringes by emitting a laser beam, and analyzes the non-uniform refractive index region inside the quartz tube based on the degree of fringe distortion, identifying internal bubbles, impurity agglomerations, and microcracks. The composition detection component is a laser-induced breakdown spectroscopy (LAS) detector, used to excite micro-area plasma on the surface of the quartz tube and collect characteristic spectral data.

3. The online detection method for high-purity quartz tubes according to claim 1, characterized in that, The steps in step S20 for targeted preprocessing of different types of detection data include: Preprocessing of appearance images: Denoising, image enhancement, and region of interest extraction are performed on appearance images acquired by industrial cameras; Sensor signal preprocessing: Filtering, detrending, and peak extraction are performed on the signals acquired by the ultrasonic flaw detection sensor; Spectral data preprocessing: Background subtraction, spectral line smoothing, and normalization are performed on the laser-induced breakdown spectral data.

4. The online detection method for high-purity quartz tubes according to claim 1, characterized in that, The step S30, which involves fusing and analyzing the appearance image data and internal defect signals in the standardized inspection data based on a deep learning model, includes: Defect feature extraction: Based on the improved YOLOv9 deep learning model, feature extraction is performed on the preprocessed appearance image to identify scratches, pits and stains on the surface of the quartz tube. The location, size, shape and grayscale features of the defects are extracted. The preprocessed ultrasonic signal and infrared thermal imaging data are fused to extract the ultrasonic reflection intensity, depth and area of ​​the abnormal heat conduction region of the internal defects. Defect Classification and Judgment: Construct a multi-feature fusion classifier to fuse surface defect features and internal defect features, classify defect types using a support vector machine algorithm, set a defect judgment threshold, and determine defects exceeding the threshold as exceeding the standard. Purity testing: Based on laser-induced breakdown spectral data, the characteristic spectral line intensities of impurity elements in the quartz tube are extracted, and the content of each impurity element is calculated by calibration curve method; Dimensional accuracy inspection: Based on machine vision measurement algorithms, the inner diameter, outer diameter, wall thickness, and straightness of the quartz tube are calculated by combining the outline edge of the quartz tube in the appearance image with the distance data collected by the laser interferometer.

5. The online detection method for high-purity quartz tubes according to claim 4, characterized in that, The calibration curve establishment process includes preparing a series of quartz standard samples with known impurity element contents, collecting the characteristic spectral intensity of each standard sample using a laser-induced breakdown spectroscopy detector, and using the impurity element content as the abscissa and the characteristic spectral intensity as the ordinate to fit the calibration curve using the least squares method.

6. An online detection system for high-purity quartz tubes, characterized in that, The system performs the online detection method for high-purity quartz tubes as described in claim 1, comprising: Online testing platform construction module: Used to build an online testing platform, deploy multi-dimensional sensing units at key testing nodes of the quartz tube production line, collect appearance image data, internal defect sensing signals and chemical composition content data of high-purity quartz tubes in real time, and send the collected data to the data processing center synchronously through the communication link; Data preprocessing module: After receiving data, the data processing center performs targeted preprocessing on different types of test data to eliminate environmental interference, equipment system errors and data redundancy, and obtain standardized test data. Multi-dimensional detection and analysis module: It is used to fuse and analyze the appearance image data and internal defect signals in the standardized detection data based on the deep learning model, identify surface defects, internal cracks and bubbles in quartz tubes, detect the content of impurity elements and determine the purity in quartz tubes by combining laser-induced breakdown spectroscopy technology with characteristic spectral analysis, and detect the dimensions of inner diameter, outer diameter, wall thickness and straightness of quartz tubes based on machine vision measurement algorithms. Quality assessment and sorting control module: This module integrates defect identification results, purity test data, and dimensional accuracy data in the data processing center. Based on the preset high-purity quartz tube quality standards, it generates a real-time quality assessment report, triggers an online sorting signal for non-conforming products, controls the sorting device to automatically separate non-conforming products, and simultaneously synchronizes the test data and quality report to the production management platform.

7. An online testing device for high-purity quartz tubes, characterized in that, include: The invention includes a memory, a processor, and an online detection program for high-purity quartz tubes stored in the memory and executable on the processor. When executed by the processor, the online detection program for high-purity quartz tubes implements an online detection method for high-purity quartz tubes as described in any one of claims 1 to 5.

8. A computer program product, characterized in that, The computer program product includes an online detection program for high-purity quartz tubes, which, when executed by a processor, implements an online detection method for high-purity quartz tubes as described in any one of claims 1 to 5.

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