CN117933828A - Closed loop quality feedback and process parameter self-adaptive adjustment method for fine burning process - Google Patents
Closed loop quality feedback and process parameter self-adaptive adjustment method for fine burning process Download PDFInfo
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Abstract
The invention relates to the technical field of quartz products, in particular to a closed-loop quality feedback and process parameter self-adaptive adjustment method for a fine burning process. Comprising the following steps: extracting a small amount of the primarily processed quartz semi-finished product for trial fine burning; detecting and testing the appearance and performance of the quartz product after being burned; setting an appearance qualification rate threshold value of the quartz product and setting a performance threshold value of the quartz product; dividing the performance grade of the quartz product, and counting the probability of the quartz product in each performance grade; and when the trial fine burning result does not meet the requirements, adjusting the process parameters during fine burning, and repeating the trial fine burning, and when the quartz product subjected to the trial fine burning meets the requirements, performing the formal fine burning. The invention solves the quality detection problem of quartz products after fine burning and the problem that the quality problem of quartz products cannot be fed back in time during fine burning, and the quartz quality grade distribution provided by the invention can be used for adjusting the technological parameters of quartz fine burning.
Description
Technical Field
The invention relates to the technical field of quartz products, in particular to a closed-loop quality feedback and process parameter self-adaptive adjustment method for a fine burning process.
Background
Quartz products are composed of a single silica component with Si-O bonds in a short-range ordered, long-range disordered arrangement and thus have incomparably superior physicochemical properties. The quartz product has the characteristics of high purity, chemical stability, wide spectral transmission, thermal shock resistance, high-temperature deformation resistance, cosmic ray resistance, radiation resistance, electric insulation and the like, and is widely applied to the fields of semiconductors, aerospace, laser nuclear technology, optical fiber communication, inertial navigation and the like. Due to the mineralogical characteristics of quartz and the complexity and variety of the process for preparing quartz products, no quality standard for fused quartz materials is currently established.
Firing of quartz products is a critical step in the production of quartz products, and since quartz materials are produced at high cost, if they are produced directly on a large scale, they cannot be adjusted when a problem is detected, and therefore, it is necessary to confirm that the quality is acceptable by small-scale trial firing and then to produce them on a large scale.
In view of this, the invention provides a closed loop quality feedback and process parameter self-adaptive adjustment method for the fine burning process.
Disclosure of Invention
The invention solves the technical problems that: the problems of appearance and quality detection after the quartz product is finished, feedback of the detected problems, and adjustment of the firing process according to the appearance and quality problems of the quartz product.
In order to achieve the above purpose, the present invention provides the following technical solutions:
extracting a small amount of the primarily processed quartz semi-finished product for trial fine burning;
detecting and testing the appearance and performance of the quartz product after being burned;
Setting an appearance qualification rate threshold value of the quartz product and setting a performance threshold value of the quartz product;
dividing the performance grade of the quartz product, and counting the probability of the quartz product in each performance grade;
And when the trial fine burning result does not meet the requirements, adjusting the process parameters during fine burning, and repeating the trial fine burning, and when the quartz product subjected to the trial fine burning meets the requirements, performing the formal fine burning.
Preferably, the trial fine sintering is to randomly extract a small part of quartz materials from the quartz materials to be sintered, wherein the sintering comprises a melting preparation process and an annealing flow;
performing appearance detection and performance detection on the quartz product, wherein the appearance detection is to detect whether flaws exist in the appearance of the quartz product, and the performance detection is to detect whether the performance of the quartz product reaches the standard;
And when the appearance qualification rate of the quartz product is detected to be more than or equal to the appearance qualification rate threshold value, performing performance detection again, otherwise stopping detection, and performing trial fine burning again.
Preferably, the appearance detection includes: training a deep learning model in advance, and detecting whether flaws exist in the appearance of the precisely burned quartz product through the deep learning model;
The training deep learning model comprises the steps of obtaining images of a burned historical quartz product, preprocessing the images, constructing a data set, constructing a model framework and training the model.
Preferably, the step of training the deep learning model is as follows: collecting image data of the quartz product; the label of the image of the artificially marked flaw quartz product is 0, and the image label of the flaw-free quartz product is 1; preprocessing an image, normalizing the image, and constructing an image data set; dividing the data set into a training set, a verification set and a detection set; selecting a binary cross entropy function as a loss function; training the minimum loss function; setting a loss function threshold, and finishing training of the deep learning model when the loss function value is smaller than the loss function threshold;
The training deep learning model is used for carrying out appearance detection on the quartz product subjected to trial firing, and the method comprises the following steps:
Erecting a plurality of cameras at fixed points, carrying out full-angle shooting on each quartz product subjected to finish firing and annealing, preprocessing the shot pictures, inputting the preprocessed images into a deep learning model subjected to training in real time, identifying and recording the input images by the deep learning model, setting the deep learning model to take the proportion of the flawless quartz products as an output target, and outputting the proportion of the flawless quartz products after identifying all the pictures of the trial firing quartz products;
And if the proportion of the flawless quartz products is greater than or equal to the appearance qualification rate threshold, the appearance of the quartz products subjected to the fine sintering is qualified, the performance detection of the quartz products subjected to the fine sintering is carried out, and if the proportion of the flawless quartz products is less than the appearance qualification rate threshold, the appearance of the quartz products subjected to the fine sintering is unqualified, and the current preparation process is adjusted and then the fine sintering is carried out.
Preferably, the performance detection includes: physical property detection and chemical property detection;
The physical property detection comprises mechanical property detection, thermal property detection and electrical property detection; the mechanical property detection comprises detecting the compressive strength and the bending strength of the quartz product; the thermal performance test includes testing thermal conductivity and coefficient of thermal expansion; the electrical property detection includes detecting resistivity;
The chemical property detection comprises metal impurity detection and hydroxyl detection; the detection of the metal impurities adopts a spectrum analysis method to analyze the components and the content of the metal impurities in the quartz product; the hydroxyl group detection is to detect the content of hydroxyl groups in the quartz product by adopting an infrared spectrometry and analyzing the intensity and the position of an absorption peak in the infrared spectrometry.
Preferably, the performance threshold includes: a first set of thresholds, a second set of thresholds, and a third set of thresholds;
the set of thresholds includes: compressive strength threshold, flexural strength threshold, thermal conductivity threshold, coefficient of expansion threshold, resistivity threshold, metal impurity content threshold, and hydroxyl content threshold;
Classifying the performance grade of the quartz product based on the threshold value set;
When any performance index of the quartz product subjected to the trial fine sintering is smaller than a corresponding threshold value in the first threshold value set, judging that the quartz product is a disqualified product;
When the performance index of the quartz product subjected to the trial fine burning is larger than or equal to a corresponding threshold value in the first threshold value set and smaller than a corresponding threshold value in the second threshold value set, judging that the quartz product is a qualified product;
when all performance indexes of the quartz product subjected to the trial fine sintering are larger than or equal to the corresponding threshold value in the second threshold value set and smaller than the corresponding threshold value in the third threshold value set, judging the quartz product to be an excellent product;
And when any performance index of the quartz product subjected to the trial fine sintering is larger than or equal to a corresponding threshold value in the third threshold value set, judging that the quartz product is a disqualified product.
Preferably, when detecting the quartz product subjected to the trial fine burning, stopping unfinished detection items when any index exceeds a qualified threshold range, and directly marking the quartz product as unqualified;
Counting the probability of each performance grade quartz product to obtain probability distribution, and starting formal large-scale fine burning if the probability of the qualified and excellent quartz products subjected to fine burning meets the requirements;
If the probability of the qualified and excellent quartz products after fine burning does not meet the requirements, carrying out fine burning again, analyzing single performance index distribution in a performance threshold set, improving the association process of the performance indexes with high reject ratio, and adjusting process parameters.
Preferably, when the process parameters are adjusted, the process parameters are adjusted according to probability results of the performance indexes.
A closed loop quality feedback and process parameter adaptive adjustment system for a fine burn process, the system comprising:
the system comprises a fine burning module, a detection module, a threshold module, a statistics module and a process adjustment module;
the fine burning module is used for extracting a small amount of primarily processed quartz semi-finished products to perform trial fine burning;
the detection module is used for detecting the appearance and performance of the quartz product after finish burning;
the threshold module is used for setting the appearance qualification rate threshold value of the quartz product and setting the performance threshold value of the quartz product;
The statistics module is used for dividing the performance grades of the quartz products and counting the probability of the quartz products in each performance grade;
And the process adjustment module is used for adjusting the process parameters during fine burning when the fine burning test is not in accordance with the requirements, and carrying out the fine burning test again, and carrying out the formal fine burning when the quartz products subjected to the fine burning test are in accordance with the requirements.
An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor is used for realizing the steps of a closed loop quality feedback and process parameter self-adaptive adjustment method of a fine burning process when executing the program.
A readable storage medium storing a computer program adapted to be loaded by a processor for performing the steps of a method of closed loop quality feedback and process parameter adaptive adjustment of a fine burn process.
The invention has the beneficial effects that: the invention solves the quality detection problem of the quartz product after finish burning and solves the problem that the quality problem of the quartz product can not be fed back in time when the quartz product is finish burned; the quartz quality grade distribution provided by the invention can be used for adjusting the technological parameters of quartz fine burning. By trial and fine sintering, trial and error cost of quartz product production is reduced, so that production cost of the quartz product is reduced, and production efficiency of the quartz product is improved.
Drawings
FIG. 1 is a flow chart of a method for closed loop quality feedback and process parameter adaptive adjustment of a fine burning process provided by the invention;
FIG. 2 is a block diagram of a closed loop quality feedback and process parameter adaptive adjustment system for a fine burning process according to the present invention;
FIG. 3 is a schematic diagram of an electronic device according to the present invention;
fig. 4 is a schematic diagram of a computer readable storage medium according to the present invention.
Detailed Description
For a better understanding of the application, various aspects of the application will be described in more detail with reference to the accompanying drawings. It should be understood that the detailed description is merely illustrative of exemplary embodiments of the application and is not intended to limit the scope of the application in any way. Like reference numerals refer to like elements throughout the specification. The expression "and/or" includes any and all combinations of one or more of the associated listed items.
In the drawings, the size, dimensions and shape of elements have been slightly adjusted for convenience of description. The figures are merely examples and are not drawn to scale. As used herein, the terms "about," "approximately," and the like are used as terms of a table approximation, not as terms of a table degree, and are intended to account for inherent deviations in measured or calculated values that will be recognized by one of ordinary skill in the art. In addition, in the present application, the order in which the steps are described does not necessarily indicate the order in which the steps occur in actual practice unless explicitly defined otherwise or the context may be inferred.
It will be further understood that terms such as "comprises," "comprising," "includes," "including," "having," "containing," "includes" and/or "including" are open-ended, rather than closed-ended, terms that specify the presence of the stated features, elements, and/or components, but do not preclude the presence or addition of one or more other features, elements, components, and/or groups thereof. Furthermore, when a statement such as "at least one of the following" appears after a list of features listed, it modifies the entire list of features rather than just modifying the individual elements in the list. Furthermore, when describing embodiments of the application, use of "may" means "one or more embodiments of the application. Also, the term "exemplary" is intended to refer to an example or illustration.
Unless otherwise defined, all terms (including engineering and technical terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In addition, the embodiments of the present application and the features of the embodiments may be combined with each other without collision. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
Referring to fig. 1, a method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process is provided in accordance with a first embodiment of the present invention.
S1: and extracting a small amount of the primarily processed quartz semi-finished product for trial fine burning.
The trial fine sintering is to randomly extract a small part of quartz materials from the quartz materials for sintering, wherein the sintering comprises a melting preparation process and an annealing flow.
S2: and detecting the appearance of the quartz product after the finish burning, and setting an appearance qualification rate threshold of the quartz product.
The appearance qualification rate of the quartz product is the proportion of the flaw-free quartz product; and carrying out appearance detection and performance detection on the quartz product, wherein the appearance detection is to detect whether flaws exist in the appearance of the quartz product, and the performance detection is to detect whether the performance of the quartz product reaches the standard.
The appearance detection includes: and training a deep learning model in advance, and detecting whether flaws exist in the appearance of the precisely burned quartz product through the deep learning model.
The training of the deep learning model comprises the steps of obtaining an image, preprocessing the image, constructing a data set, constructing a model framework and training the model.
The training steps of the deep learning model are as follows:
And (3) acquiring an image of the burned historical quartz product, and manually marking the image as a defective quartz product label and a label of a non-defective quartz product, wherein the image label of the defective quartz product is set to be 0, and the image label of the non-defective quartz product is set to be 1.
Preprocessing an image, adjusting the size of the image, enhancing the image, and normalizing the image.
Constructing a preprocessed image into a data set, wherein the data set is divided into a training set, a verification set and a detection set; the ratio of training set, validation set and detection set is 8:1:1.
The deep learning model can be selected as a convolutional neural network, and a convolutional layer, a pooling layer and a full-connection layer are configured according to actual requirements.
And selecting a binary cross entropy function as a loss function, and updating model parameters by adopting a random gradient descent method.
Illustratively, the binary crossover loss function is:
wherein, Is the value of the loss function,Is the number of samples that are to be taken,Is a quartz product image label, and is 0 or 1; Is the output of the model, representing the probability that the sample belongs to the corresponding label;
For each quartz article image sample, if image label Is 1, the loss value is; If image labelIs 0, the loss value is。
And setting a loss threshold, and when the training loss is reduced below the loss threshold and is verified by a verification set, finishing training when the performance of the deep learning model for recognizing the quartz product image is not obviously improved.
Illustratively, an Adam optimizer is selected to update the model parameters:
wherein, Is the rate of learning to be performed,AndIs a momentum estimation and a second order matrix estimation,A small constant added for numerical stability; For the weights and bias terms in the model, Is the weight and bias term for the step number t in the deep learning model.
Training the model by using a training set, and updating model parameters through repeated iterative training; evaluating the performance of the model by using the verification set, and adjusting the super parameters according to the verification result; the final performance of the model is evaluated using the test set.
Training a deep learning model for detecting and testing the appearance image of the precisely burned quartz product.
The training deep learning model is used for carrying out appearance detection on the quartz product subjected to trial firing, and the method comprises the following steps:
s21: erecting a plurality of cameras at fixed points, and carrying out full-angle shooting on each quartz product which is subjected to fine burning and the annealing process.
S22: preprocessing the shot pictures, splicing the pictures shot by a plurality of cameras into an image according to a fixed angle sequence, and preprocessing the image.
S33: inputting the preprocessed images into a trained deep learning model in real time, identifying and recording the input images by the deep learning model, setting the deep learning model to take the proportion of the defective quartz products as an output target, and respectively outputting the proportion of the non-defective quartz products and the defective quartz products after identifying all the pictures of the trial fine-burned quartz products.
Setting an appearance qualification rate threshold of the quartz product, if the proportion of the flaw-free quartz product is smaller than the appearance qualification rate threshold, failing to test the appearance of the finished-burned quartz product, and not detecting the performance of the finished-burned quartz product, and adjusting the current preparation process and then performing the finished-burned test.
And if the proportion of the flaw-free quartz products is greater than or equal to the appearance qualification rate threshold, the appearance of the quartz products subjected to the trial fine sintering is qualified, and the performance of the quartz products subjected to the trial fine sintering is detected.
S3: and detecting the appearance of the quartz product after the finish burning, and setting the performance threshold of the quartz product.
The performance test comprises: physical property detection and chemical property detection.
The physical property detection comprises mechanical property detection, thermal property detection and electrical property detection; the mechanical property detection comprises detecting the compressive strength and the bending strength of the quartz product.
The compressive strength is calculated by the following formula:
wherein, In order to achieve a compressive strength, the steel sheet is,Is the maximum compressive force to which the quartz product is subjected, and A is the area of the stressed cross section of the quartz product.
The flexural strength was calculated as:
wherein, Is the bending strength of the steel sheet, and the steel sheet,Is the load at which the sample breaks,Is the distance between the fulcra points,Is the width of the quartz article and,Is the height of the quartz article.
The thermal performance test includes testing thermal conductivity and coefficient of thermal expansion; the thermal conductivity is calculated by the following formula:
wherein, Is the thermal conductivity of the material,Is the heat flow through the quartz product, S is the area of the heat transfer cross section,Is the difference in temperature, and the temperature,Is the length of the thermally conductive path.
The calculation formula of the thermal expansion coefficient is as follows:
wherein, Is the data of the change of the size of the quartz product,Is the original size data of the quartz product; Is the temperature difference.
The electrical property detection comprises detecting the resistivity of the quartz product by adopting a Hall effect;
the Hall effect formula is as follows:
wherein, Is the hall voltage which is used to determine the voltage,Is the current flow which is to be measured,Is the concentration of the carriers which are present in the wafer,Is the meta-charge and B is the magnetic induction.
The chemical property detection comprises metal impurity detection and hydroxyl detection; the detection of the metal impurities adopts a spectrum analysis method to analyze the components and the content of the metal impurities in the quartz product; the hydroxyl group detection is to detect the content of hydroxyl groups in the quartz product by adopting an infrared spectrometry and analyzing the intensity and the position of an absorption peak in the infrared spectrometry.
The quartz article performance threshold comprises: a first set of thresholds, a second set of thresholds, and a third set of thresholds;
The set of thresholds includes: threshold compressive strength Threshold of flexural strengthThreshold of thermal conductivityThreshold of expansion coefficientResistivity thresholdThreshold value of metal impurity contentAnd a hydroxyl content threshold。
The first threshold value set is；
The second threshold value set is；
The third threshold value set is。
S4: the performance grade of the quartz article is divided.
Classifying the performance grade of the quartz product based on the threshold value set;
When any performance index of the quartz product subjected to the trial fine sintering is smaller than a corresponding threshold value in the first threshold value set, judging that the quartz product is a disqualified product;
When the performance index of the quartz product subjected to the trial fine burning is larger than or equal to a corresponding threshold value in the first threshold value set and smaller than a corresponding threshold value in the second threshold value set, judging that the quartz product is a qualified product;
when all performance indexes of the quartz product subjected to the trial fine sintering are larger than or equal to the corresponding threshold value in the second threshold value set and smaller than the corresponding threshold value in the third threshold value set, judging the quartz product to be an excellent product;
And when any performance index of the quartz product subjected to the trial fine sintering is larger than or equal to a corresponding threshold value in the third threshold value set, judging that the quartz product is a disqualified product.
When detecting the quartz product subjected to trial fine burning, stopping unfinished detection items when any index exceeds a qualified threshold range, and directly marking the quartz product as unqualified.
S5: and counting and feeding back the result of trial fine burning.
Based on the calculated probability of the quartz products with each performance grade, if the probability of the qualified quartz products and the probability of the excellent quartz products which are subjected to fine firing meet the requirements, starting formal large-scale fine firing;
If the probability of the qualified and excellent quartz products after fine burning does not meet the requirements, carrying out fine burning again, analyzing single performance index distribution in a performance threshold set, and improving the association process of the performance indexes with high reject ratio.
When the reject ratio of the single performance index is more than 10%, the related process is adjusted in a small range.
When the reject ratio of the single performance index is more than 10% but less than 20%, the correlation process is greatly adjusted.
When the reject ratio of the single performance index exceeds 20%, all processes are adjusted.
The appearance of the quartz product is related to the high-temperature homogenization process in the preparation process, and if the appearance problem causes disqualification of trial burning, the high-temperature homogenization time is required to be prolonged in the preparation process.
The compressive strength and the bending strength of the quartz product are related to the preparation temperature and the annealing time in the preparation process, and if the compressive strength and the bending strength are not qualified, the melting temperature is increased and the annealing is performed in a gradient cooling mode in the next trial firing process.
And if the thermal performance is unqualified, the material cooling and the cooling time of the quartz product are uniformly controlled in the next trial firing process.
The electrical properties of the quartz product are related to the preparation temperature of the quartz material, and if the resistivity of the quartz product is too small, the melting temperature needs to be raised in the next trial fine firing.
The hydroxyl content in the chemical property of the quartz product is related to the preparation temperature and the preparation environment, when the fused quartz product is prepared by adopting an electrofusion method, if the hydroxyl content of the quartz product is too large, the preparation temperature needs to be increased in the next trial firing; when the gas refining method is adopted to prepare the quartz product, if the hydroxyl content in the quartz product is too large, whether the heating chamber is vacuumized or not is detected in the next trial firing.
Example 2
Referring to fig. 2, a second embodiment of the present invention provides a closed loop quality feedback and process parameter adaptive adjustment system for a fine burn process.
The system comprises a fine burning module, a detection module, a threshold module, a statistics module and a process adjustment module.
And the fine burning module is used for extracting a small amount of the primarily processed quartz semi-finished product to perform trial fine burning.
The detection module is used for detecting the appearance and performance of the quartz product after finish burning;
The performance test comprises: physical property detection and chemical property detection.
The physical property detection comprises mechanical property detection, thermal property detection and electrical property detection; the chemical property detection comprises metal impurity content detection and hydroxyl content detection.
The threshold module is used for setting the appearance qualification rate threshold of the quartz product and setting the performance threshold of the quartz product.
The statistics module is used for dividing the performance grades of the quartz products and counting the probability of the quartz products in each performance grade.
And the process adjustment module is used for adjusting the process parameters during fine burning when the fine burning test is not in accordance with the requirements, and carrying out the fine burning test again, and carrying out the formal fine burning when the quartz products subjected to the fine burning test are in accordance with the requirements.
Example 3
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, there is also provided an electronic device according to still another aspect of the present application. The electronic device may include one or more processors and one or more memories. Wherein the memory has stored therein computer readable code which, when executed by the one or more processors, is operable to perform a fine burn process closed loop quality feedback and process parameter adaptive adjustment method as described above.
The method or system according to embodiments of the application may also be implemented by means of the architecture of the electronic device shown in fig. 3. As shown in fig. 3, the electronic device may include a bus, one or more CPU, ROM, RAM, a communication port connected to a network, input/output components, a hard disk, and the like. A storage device in the electronic device, such as a ROM or a hard disk, can store the closed-loop quality feedback and process parameter self-adaptive adjustment method for the fine burning process. The method for closed loop quality feedback and process parameter self-adaptive adjustment of the fine sintering process can comprise the following steps: extracting a small amount of the primarily processed quartz semi-finished product for trial fine burning; detecting and testing the appearance and performance of the quartz product after being burned; setting an appearance qualification rate threshold value of the quartz product and setting a performance threshold value of the quartz product; dividing the performance grade of the quartz product, and counting the probability of the quartz product in each performance grade; and when the trial fine burning result does not meet the requirements, adjusting the process parameters during fine burning, and repeating the trial fine burning, and when the quartz product subjected to the trial fine burning meets the requirements, performing the formal fine burning.
Further, the electronic device may also include a user interface. Of course, the architecture shown in fig. 3 is merely exemplary, and one or more components of the electronic device shown in fig. 3 may be omitted as may be practical in implementing different devices.
Example 4
FIG. 4 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present application. As shown in fig. 4, is a computer-readable storage medium according to one embodiment of the present application. The computer readable storage medium has computer readable instructions stored thereon. When the computer readable instructions are executed by the processor, a method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process according to an embodiment of the present application described with reference to the above figures may be performed. Storage media include, but are not limited to, for example, volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
In addition, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided by the present application, such as: extracting a small amount of the primarily processed quartz semi-finished product for trial fine burning; detecting and testing the appearance and performance of the quartz product after being burned; setting an appearance qualification rate threshold value of the quartz product and setting a performance threshold value of the quartz product; dividing the performance grade of the quartz product, and counting the probability of the quartz product in each performance grade; and when the trial fine burning result does not meet the requirements, adjusting the process parameters during fine burning, and repeating the trial fine burning, and when the quartz product subjected to the trial fine burning meets the requirements, performing the formal fine burning.
The methods and apparatus, devices of the present application may be implemented in numerous ways. For example, the methods and apparatus, devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present application may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present application. Thus, the present application also covers a recording medium storing a program for executing the method according to the present application.
In addition, in the foregoing technical solutions provided in the embodiments of the present application, parts consistent with implementation principles of corresponding technical solutions in the prior art are not described in detail, so that redundant descriptions are avoided.
The purpose, technical scheme and beneficial effects of the invention are further described in detail in the detailed description. It is to be understood that the above description is only of specific embodiments of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. A method for closed loop quality feedback and process parameter self-adaptive adjustment of a fine sintering process is characterized by comprising the following steps:
extracting a small amount of the primarily processed quartz semi-finished product for trial fine burning;
detecting and testing the appearance and performance of the quartz product after being burned;
Setting an appearance qualification rate threshold value of the quartz product and setting a performance threshold value of the quartz product;
dividing the performance grade of the quartz product, and counting the probability of the quartz product in each performance grade;
And when the trial fine burning result does not meet the requirements, adjusting the process parameters during fine burning, and repeating the trial fine burning, and when the quartz product subjected to the trial fine burning meets the requirements, performing the formal fine burning.
2. The method for closed-loop quality feedback and process parameter self-adaptive adjustment of a fine sintering process according to claim 1, wherein the fine sintering test is to randomly extract a small part of quartz material from the quartz material for sintering, and the sintering comprises a melting preparation process and an annealing flow;
performing appearance detection and performance detection on the quartz product, wherein the appearance detection is to detect whether flaws exist in the appearance of the quartz product, and the performance detection is to detect whether the performance of the quartz product reaches the standard;
And when the appearance qualification rate of the quartz product is detected to be more than or equal to the appearance qualification rate threshold value, performing performance detection again, otherwise stopping detection, and performing trial fine burning again.
3. The method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process of claim 2, wherein the appearance detection comprises: training a deep learning model in advance, and detecting whether flaws exist in the appearance of the precisely burned quartz product through the deep learning model;
The training deep learning model comprises the steps of obtaining images of a burned historical quartz product, preprocessing the images, constructing a data set, constructing a model framework and training the model.
4. A method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process according to claim 3, wherein the step of training a deep learning model is as follows: collecting image data of the quartz product; the label of the image of the artificially marked flaw quartz product is 0, and the image label of the flaw-free quartz product is 1; preprocessing an image, normalizing the image, and constructing an image data set; dividing the data set into a training set, a verification set and a detection set; selecting a binary cross entropy function as a loss function; training the minimum loss function; setting a loss function threshold, and finishing training of the deep learning model when the loss function value is smaller than the loss function threshold;
The training deep learning model is used for carrying out appearance detection on the quartz product subjected to trial firing, and the method comprises the following steps:
Erecting a plurality of cameras at fixed points, carrying out full-angle shooting on each quartz product subjected to finish firing and annealing, preprocessing the shot pictures, inputting the preprocessed images into a deep learning model subjected to training in real time, identifying and recording the input images by the deep learning model, setting the deep learning model to take the proportion of the flawless quartz products as an output target, and outputting the proportion of the flawless quartz products after identifying all the pictures of the trial firing quartz products;
And if the proportion of the flawless quartz products is greater than or equal to the appearance qualification rate threshold, the appearance of the quartz products subjected to the fine sintering is qualified, the performance detection of the quartz products subjected to the fine sintering is carried out, and if the proportion of the flawless quartz products is less than the appearance qualification rate threshold, the appearance of the quartz products subjected to the fine sintering is unqualified, and the current preparation process is adjusted and then the fine sintering is carried out.
5. The method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process of claim 4, wherein said performance detection comprises: physical property detection and chemical property detection;
The physical property detection comprises mechanical property detection, thermal property detection and electrical property detection; the mechanical property detection comprises detecting the compressive strength and the bending strength of the quartz product; the thermal performance test includes testing thermal conductivity and coefficient of thermal expansion; the electrical property detection includes detecting resistivity;
The chemical property detection comprises metal impurity detection and hydroxyl detection; the detection of the metal impurities adopts a spectrum analysis method to analyze the components and the content of the metal impurities in the quartz product; the hydroxyl group detection is to detect the content of hydroxyl groups in the quartz product by adopting an infrared spectrometry and analyzing the intensity and the position of an absorption peak in the infrared spectrometry.
6. The method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process of claim 5, wherein the performance threshold comprises: a first set of thresholds, a second set of thresholds, and a third set of thresholds;
the set of thresholds includes: compressive strength threshold, flexural strength threshold, thermal conductivity threshold, coefficient of expansion threshold, resistivity threshold, metal impurity content threshold, and hydroxyl content threshold;
Classifying the performance grade of the quartz product based on the threshold value set;
When any performance index of the quartz product subjected to the trial fine sintering is smaller than a corresponding threshold value in the first threshold value set, judging that the quartz product is a disqualified product;
When the performance index of the quartz product subjected to the trial fine burning is larger than or equal to a corresponding threshold value in the first threshold value set and smaller than a corresponding threshold value in the second threshold value set, judging that the quartz product is a qualified product;
when all performance indexes of the quartz product subjected to the trial fine sintering are larger than or equal to the corresponding threshold value in the second threshold value set and smaller than the corresponding threshold value in the third threshold value set, judging the quartz product to be an excellent product;
And when any performance index of the quartz product subjected to the trial fine sintering is larger than or equal to a corresponding threshold value in the third threshold value set, judging that the quartz product is a disqualified product.
7. The method for closed-loop quality feedback and process parameter self-adaptive adjustment of a fine burning process according to claim 6, wherein when any index exceeds a qualified threshold range during detection of a quartz product subjected to fine burning, stopping unfinished detection items, and directly marking the quartz product as unqualified;
Counting the probability of each performance grade quartz product to obtain probability distribution, and starting formal large-scale fine burning if the probability of the qualified and excellent quartz products subjected to fine burning meets the requirements;
If the probability of the qualified and excellent quartz products after fine burning does not meet the requirements, carrying out fine burning again, analyzing single performance index distribution in a performance threshold set, improving the association process of the performance indexes with high reject ratio, and adjusting process parameters.
8. The method for closed loop quality feedback and process parameter adaptive adjustment of a fine burn process of claim 7, wherein the process parameters are adjusted according to probability results of each performance indicator when the process parameters are adjusted.
9. A closed loop quality feedback and process parameter self-adaptive adjustment system for a fine sintering process, which is realized based on the closed loop quality feedback and process parameter self-adaptive adjustment method for the fine sintering process according to any one of claims 1-8, and is characterized in that: the system comprises a fine burning module, a detection module, a threshold module, a statistics module and a process adjustment module;
the fine burning module is used for extracting a small amount of primarily processed quartz semi-finished products to perform trial fine burning;
the detection module is used for detecting the appearance and performance of the quartz product after finish burning;
the threshold module is used for setting the appearance qualification rate threshold value of the quartz product and setting the performance threshold value of the quartz product;
The statistics module is used for dividing the performance grades of the quartz products and counting the probability of the quartz products in each performance grade;
And the process adjustment module is used for adjusting the process parameters during fine burning when the fine burning test is not in accordance with the requirements, and carrying out the fine burning test again, and carrying out the formal fine burning when the quartz products subjected to the fine burning test are in accordance with the requirements.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of a closed loop quality feedback and process parameter adaptive adjustment method of a fine burn process according to any of claims 1 to 8.
11. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of a method for closed loop quality feedback and process parameter adaptation adjustment of a fine burn process according to any of claims 1 to 8.
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