CN115373345A - Method and system for intelligent processing - Google Patents

Abstract

The present invention provides an intelligent processing method and system for integrating the on-line processing, detection and calibration of processing equipment. The system comprises a marking device, a rough judgment device, a fine judgment device, a processing information acquisition device and a measurement/parameter correction device. The marking device is used for marking the processing information on the processed object. The rough judgment device is an automatic vision detection device realized by an optical detection instrument, and can primarily screen the products with flaws. The precise determination device identifies the type of the defect through artificial intelligence. The processing information acquiring device is used for identifying the processing parameters of the processing information. The measuring/parameter correcting device can measure the size of the flaw and calculate the best result of adjusting the processing parameter according to the flaw size and the processing parameter.

Description

Method and system for intelligent processing

technical field

The present invention relates to the optimization of an automated processing flow, in particular to a method and system for intelligent processing.

Background technique

The production process is closely linked from processing, testing, and calibration of processing machines. Any mistake in any step will eventually lead to problems such as insufficient product yield and reduced production capacity. In the past, the above-mentioned production process was often divided into different specialized departments, and it was easy to waste a lot of time and cost in manual communication or cooperation. For example, although automatic optical inspection of products can replace manual general inspection, when defective products are found, manual re-inspection is still required to measure and identify defects, and then hand over the defect data to the production unit for production The unit recalibrates the parameters of the production machine. The above-mentioned process is not only incoherent and time-consuming, but also in most cases, it is impossible to obtain the ideal yield immediately by calibrating the parameters of the machine once, and actually requires multiple calibrations.

With the continuous development of technology, the term "Industry 4.0" was proposed in 2011. The main spirit is to automate the production process intelligently and actively eliminate production problems.

Therefore, in order to solve the above problems, the field should develop processing methods and systems in line with the spirit of "Industry 4.0" to improve the efficiency of the production process and continuously improve the yield of products.

Contents of the invention

In order to solve the above problems, the embodiment of the present invention develops a method and system for intelligent processing. The above system integrates technologies such as product traceability, optical instrument initial inspection, artificial intelligence-based re-inspection, virtual measurement, and automatic parameter optimization. The applicant of the present invention refers to the patents filed in the past, including: TW109132726, TW109124011, TW110107077, TW109212512 and TW109136592, as a reference for the technology of the present invention.

Specifically, an embodiment of the present invention provides an intelligent processing system, the above-mentioned intelligent processing system is applied to at least one processing device, and the above-mentioned processing device processes the object to be processed according to the processing parameters and generates the object to be processed. The above-mentioned intelligent processing system includes at least one marking device, a rough judgment device, a fine judgment device, a processing information acquisition device and a measurement/parameter correction device.

According to yet another embodiment, the above-mentioned marking device marks processing information on the above-mentioned workpiece, wherein the above-mentioned processing information includes at least one of the above-mentioned processing parameters. The recording methods of the processing information of the above-mentioned marking device include characters, numbers, symbols and image codes, wherein the above-mentioned image codes include one-dimensional barcodes and two-dimensional barcodes. The processing information of the above-mentioned marking device further includes manufacturing batch, product model and manufacturing date.

According to yet another embodiment, the rough judging device is for initially checking whether the processed object has at least one defect and generating at least one image of the processed object, wherein the rough judging device is an automatic visual inspection device. The preliminary inspection method of the above-mentioned automatic visual inspection device includes contour comparison, position coordinate comparison, 3D contour comparison, color luster comparison and brightness comparison. The above-mentioned automatic visual inspection device comprises at least one imaging unit, at least one distance measuring unit, a computing unit and a database. The above distance measuring unit is used to generate information on the imaging distance for capturing the processed object. The above-mentioned database is for storing a plurality of standard size information, and each of the above-mentioned standard size information includes the resolution per unit area, the imaging distance, the corresponding pixel matrix, and the corresponding actual size and film thickness. The calculation unit compares the standard size information in the database according to the resolution information of the processed object image and the imaging distance information, and calculates and generates the processed object image with actual size information. The above-mentioned standard size information in the above-mentioned database can be, for example, a two-dimensional size or a three-dimensional size, and the above-mentioned actual size having a two-dimensional size or a three-dimensional size is further calculated by the above-mentioned calculation unit. The image of the workpiece can be 2D or 3D.

According to yet another embodiment, the above-mentioned fine judging device is connected to the above-mentioned rough judging device for re-checking whether the image of the processed object has at least one defect, and identifying the type of defect contained in the image of the processed object, and generating Identification result. The fine-judgment device further includes a classification module, and the classification module is used to identify the type of the defect by using an artificial neural network. The training data of the above-mentioned classification module includes the above-mentioned recognition results and manual re-judgment results.

According to yet another embodiment, the above-mentioned processing information acquisition device is connected to the above-mentioned precise judging device with a signal to identify the above-mentioned processing information of the above-mentioned processed object image, and generate the above-mentioned processing parameters.

According to yet another embodiment, the above-mentioned measurement/parameter correction device is connected to the above-mentioned fine-judgment device for measuring the defect of the image of the processed object, and generates the size of the defect, and then calculates according to the size of the defect and the above-mentioned processing parameters. The optimal result of the above-mentioned processing parameters, and then generate the corrected processing parameters, and transmit the above-mentioned corrected processing parameters to the above-mentioned processing device. The above-mentioned measurement/parameter correction device further includes a parameter optimization module, and the above-mentioned parameter optimization module is to calculate the best result of the above-mentioned processing parameters by applying an algorithm. The above-mentioned flaw size may be, for example, a two-dimensional size or a three-dimensional size.

According to yet another embodiment, the above-mentioned measurement/parameter correction device, when the above-mentioned processed object has a design drawing, can further superimpose the above-mentioned design drawing and the above-mentioned processed object image to generate a superimposed image as a measure of the The basis for the size of the defect.

According to yet another embodiment, the above measurement/parameter correction device further includes providing at least one monitoring measurement area in the above design drawing in advance, and then measuring the above monitoring measurement area of the above superimposed image, and generating Monitor size information. The above-mentioned monitoring measurement area can be regarded as a region of interest or a key region (Region of interest; ROI for short) in terms of image processing.

According to yet another embodiment, the measurement/parameter correction device can further generate monitoring statistical information through statistics of the monitoring size information, and the monitoring statistical information will be used as calculation data for calculating the processing parameters. The above-mentioned monitoring size information may be, for example, two-dimensional or three-dimensional information.

According to yet another embodiment, when processing according to the above-mentioned processing parameters, the production yield corresponding to the above-mentioned processing parameters can be further calculated through the number of defects found by the above-mentioned precise judging device.

According to yet another embodiment, the qualification standard of the above-mentioned production yield rate can be adjusted according to the needs of users. If the yield rate generated by the above-mentioned processing parameters meets the above-mentioned standard, the above-mentioned processing parameters can be further used as a virtual metrology (Virtual Metrology; VM for short) ) reference parameters. The above-mentioned virtual measurement refers to estimating the production result (or quality) through processing parameters to achieve the purpose of full inspection.

According to yet another embodiment, the measurement/parameter correction device is configured to stop automatically correcting the processing parameters and generate a warning notification when the size of the defect exceeds a preset threshold.

According to yet another embodiment, upon receiving the warning notification, the above-mentioned fine judgment device will not use the identification result corresponding to the warning notification as the training data for training the classification module.

According to yet another embodiment, the above-mentioned warning notification can be notified to the system management personnel in real time through text messages or emails.

An embodiment of the present invention further provides an intelligent processing method, including the following steps. Default processing parameters in the processing device; process the object to be processed and produce the processed object; mark the processing information on the above-mentioned processed object, the above-mentioned processing information includes the above-mentioned processing parameters; use the rough judgment device to perform a preliminary inspection on the above-mentioned processed object, And capture at least one processed object image and imaging distance information, wherein the rough judging device can be an automatic visual inspection device; then compare the above-mentioned imaging distance through the multiple standard size information carried by the rough judging device Information, to generate the actual size information of the image of the processed object; re-inspect with the precise judgment device, and use the classification module to identify the type of defects contained in the image of the processed object, and generate the identification result, wherein the above classification module is applied artificially The neural network performs the above-mentioned identification of defect types; superimposes the design drawing of the above-mentioned processed object with the above-mentioned image of the processed object to generate a superimposed image, and then performs measurement of the defect on the above-mentioned superimposed image to generate the size of the defect; identifies the above-mentioned processing information to generate the above-mentioned processing parameters; use the measurement/parameter correction device through the parameter optimization module to calculate the best result of the processing parameters based on the above-mentioned defect size and the above-mentioned processing parameters, and generate corrected processing parameters, and transmit them to the above-mentioned processing device , wherein the above-mentioned parameter optimization module uses an artificial neural network to perform processing parameter optimization.

According to yet another embodiment, the above method further includes the step of quality monitoring, which is to set at least one monitoring measurement area in the design drawing of the above-mentioned processed object in the above-mentioned measurement/parameter correction device in advance, and then perform the above-mentioned The above-mentioned monitoring measurement area of the superimposed image is measured, and the monitoring size information is generated.

According to yet another embodiment, according to the above-mentioned measurement/parameter correction device, the above-mentioned monitoring size information can be further calculated to generate monitoring statistical information, and the above-mentioned monitoring statistical information will be used as calculation data for calculating the above-mentioned processing parameters.

According to yet another embodiment, when processing according to the above-mentioned method and the above-mentioned processing parameters, the production yield corresponding to the above-mentioned processing parameters is further calculated through the number of defects found by the above-mentioned precise judging device.

According to yet another embodiment, according to the above-mentioned method, the qualification standard of the above-mentioned production yield rate can be adjusted according to the needs of users. If the yield rate produced by the above-mentioned processing parameters meets the above-mentioned standard, the above-mentioned processing parameters can be further used as a virtual measurement (Virtual Measurement) Metrology; referred to as VM) reference parameters. The above-mentioned virtual measurement refers to estimating the production result (or quality) through processing parameters to achieve the purpose of full inspection.

According to yet another embodiment, the training data of the above-mentioned classification module according to the above-mentioned method includes the above-mentioned recognition results and manual re-judgment results.

According to yet another embodiment, when the defect size exceeds a threshold according to the above method, the measurement/parameter correction device will stop automatically correcting the processing parameters and generate a warning notification.

According to yet another embodiment, upon receiving the warning notification, the fine judgment device according to the above method will not use the identification result corresponding to the warning notification as the training data for training the classification module.

According to yet another embodiment, the preliminary detection method of the rough judgment device according to the above method includes 3D contour comparison, position coordinate comparison, color luster comparison and brightness comparison.

According to yet another embodiment, the plurality of standard size information according to the above method respectively include resolution per unit area, imaging distance, corresponding pixel matrix, and corresponding actual size and film thickness.

According to yet another embodiment, the recording method of the processing information according to the above method includes characters, numbers, symbols and image codes, wherein the image codes include one-dimensional barcodes and two-dimensional barcodes.

Based on the technical features of the above-mentioned embodiments, the following effects can be specifically claimed.

(1) This system is applied in the processing process to achieve the effect of intelligence and full automation. It can also connect processing equipment of several processing stations in series at the same time, so as to automatically perform the initial judgment and re-judgment of the processed objects of the processing equipment of each station. Judgment detection.

(2) For products processed through this system, the processing information on the product can be used to trace the processing parameters of each site in real time, which can quickly clarify which sites cause the insufficient yield rate of the product.

(3) The system can automatically classify the type of defect and perform measurement on the defect based on the above-mentioned preliminary judgment and re-judgment detection results, and through the artificial neural network, and obtain information on the size of the defect.

(4) After the system collects the above-mentioned processing parameters and defect sizes, it can further calculate the optimal result of the above-mentioned processing parameters through an algorithm, and generate corrected processing parameters, and then send the corrected processing parameters back to the above-mentioned processing equipment to achieve real-time The effect of optimizing processing parameters.

Description of drawings

In order to make the above and other objects, features, advantages and embodiments of the present invention more comprehensible, the accompanying drawings are described as follows:

FIG. 1 is a device schematic diagram of an intelligent processing system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of an intelligent processing system applied to multiple processing equipment according to an embodiment of the present invention.

FIG. 3 is a flowchart of an intelligent processing system according to an embodiment of the present invention.

FIG. 4 is a flowchart of quality monitoring of an intelligent processing system according to an embodiment of the present invention.

Detailed ways

In order to describe various embodiments of the present invention in more detail, the following description is supplemented with accompanying drawings.

Please refer to FIG. 1 . FIG. 1 is a device schematic diagram of an intelligent processing system according to an embodiment of the present invention. In FIG. 1 , an intelligent processing system 100 is provided according to an embodiment. The above-mentioned intelligent processing system 100 is applied to at least one processing device 110. The above-mentioned processing device 110 processes the object to be processed 200 according to the processing parameters, and produces the object to be processed 220. The above-mentioned system 100 includes: a marking device 120, a rough judgment device 130, a fine Judgment device 140, processing information acquisition device 160, and measurement/parameter correction device 180. Each component in the above-mentioned intelligent processing system 100 can be realized through software plus hardware, or through pure hardware, and the present invention is not limited thereto.

The marking device 120 marks processing information on the workpiece 220, wherein the processing information includes at least one processing parameter. The recording methods of the processing information of the above-mentioned marking device 120 include characters, numbers, symbols and image codes, wherein the above-mentioned image codes include one-dimensional barcodes and two-dimensional barcodes. The processing information of the above-mentioned marking device 120 further includes manufacturing batch, product model and manufacturing date. For example: print a two-dimensional image code on the finished or semi-finished printed circuit board, and provide a record of historical processing parameters by scanning or identifying the two-dimensional image code in the future.

The rough judging device 130 is for initially checking whether the processed object 220 has at least one flaw, and generates at least one image of the processed object, wherein the rough judging device is an automatic visual inspection device. The preliminary inspection method of the above-mentioned automatic visual inspection device includes 3D contour comparison, position coordinate comparison, color luster comparison and brightness comparison. The rough judging device 130 of this system is usually stricter in the initial judging criteria to avoid ignoring products that are really defective, but misjudgments often occur, for example, if there is fine dust somewhere in the printed circuit board, it is judged as a defect, so Although it can ensure the yield rate of the product, it is relatively easy to identify false defects. In the past, the above-mentioned false defects could only be eliminated through manual re-judgment, but in the present invention, the re-judgment and classification of defects can be carried out through the above-mentioned precise judgment device 140, which can greatly reduce false defects and the need for manual re-judgment number of judgments.

According to another embodiment of the present invention, the above-mentioned automatic visual inspection device comprises at least one imaging unit, at least one distance measuring unit, a computing unit and a database. The above distance measuring unit is used to generate information on the imaging distance for capturing the processed object. The above-mentioned database is for storing a plurality of standard size information, and each of the above-mentioned standard size information includes the resolution per unit area, the imaging distance, the corresponding pixel matrix, and the corresponding actual size and film thickness. The calculation unit compares the standard size information in the database according to the resolution information of the image of the processed object 220 and the imaging distance information, and calculates and generates the image of the processed object with actual size information. The above-mentioned rough judgment device 130 can be, for example, a computer device (equipped with CPU and GPU) that cooperates with image recognition devices.

The fine judging device 140 is connected to the rough judging device 130 for rechecking whether the image of the processed object has at least one defect, identifying the type of defect contained in the image of the processed object, and generating a recognition result. The fine-judgment device further includes a classification module 142, and the classification module 142 is used to identify the above-mentioned defect types by using an artificial neural network. The training data of the above-mentioned classification module 142 includes the above-mentioned recognition results and manual re-judgment results. The artificial neural network applied by the classification module 142 includes a convolutional neural network model (Convolutional Neural Network; CNN for short). The above CNN model can be selected from one of R-CNN (Region-based Convolutional Neural Network), Fast R-CNN, Faster R-CNN, RPN (Region Proposal Network) and Mask R-CNN, FCN (Fully Convolutional Network) Identify and classify defects. In addition, besides the convolutional neural network model, other precise algorithms for classifying parts in images can also be used to realize the precise judgment device 140, and the present invention does not rely on the precise judgment device 140. The implementation is limited. The precise judgment device 140 can effectively reduce the number of false defects and the number of manual re-judgment. Whether manual re-judgment is required can be judged by the default threshold value of the measurement/parameter correction device 180 . Taking the printed circuit board as an example, the above-mentioned false defects may include: false spots on the edge of the board, fine board scraps, dust, and differences between the sub-board to be tested and the mother board. The above-mentioned fine judgment device 140 can be, for example, a calculator (equipped with a CPU) with computing power.

The processing information acquiring device 160 is connected to the fine judging device 140 with a signal to identify the processing information of the image of the processed object and generate the processing parameters. The identification methods can be text identification, image code identification, and induction tags such as RFID. The aforementioned processing information acquiring device 160 can be, for example, a computer device (equipped with a CPU and a GPU) that cooperates with an image recognition device.

The measurement/parameter correction device 180 is connected to the precise judgment device 140 for the purpose of measuring the defect of the image of the processed object and generating the size of the defect, and then calculating the processing parameters based on the size of the defect and the processing parameters. For the best result, corrected processing parameters are then generated, and the corrected processing parameters are transmitted to the processing device 110 . The measurement/parameter correction device 180 further includes a parameter optimization module 182, and the parameter optimization module 182 calculates the best result of the above-mentioned processing parameters by applying an algorithm. The algorithm applied by the above-mentioned parameter optimization module 182 can apply any algorithm that can calculate or summarize the best result of the above-mentioned processing parameters, such algorithm can be: gradient descent method, Newton method, conjugate gradient method, linear search, confidence region method , neural network, particle swarm optimization, simulated annealing, support vector machine, ant colony algorithm, differential evolution algorithm, K-nearest neighbor algorithm (K-nearest neighbor). The measurement/parameter correction device 180 mentioned above can be, for example, a calculator (equipped with a CPU) with computing power.

According to another embodiment of the present invention, the measurement/parameter correction device 180, when the workpiece 220 has a design drawing, can further superimpose the design drawing with the image of the workpiece 220, which can be used as a measurement The basis for the size of the above defects.

According to another embodiment of the present invention, the above-mentioned measurement/parameter correction device 180 further has a process of performing quality monitoring, which is to set at least one place in the design diagram of the above-mentioned processed object in the above-mentioned measurement/parameter correction device in advance. The measurement area is monitored, and then the above-mentioned monitoring measurement area of the above-mentioned superimposed image is measured, and the monitoring size information is generated. The above-mentioned quality monitoring process can be carried out in the form of general inspection or random inspection. Taking the detection of printed circuit boards as an example, the above-mentioned monitoring and measurement area can be frame-selected for specific soldering positions of the printed circuit board.

According to another embodiment of the present invention, the above-mentioned monitoring size information can be passed through the above-mentioned measurement/parameter correction device 180 to generate monitoring statistical information through statistics. And the above-mentioned monitoring statistical information will be used as the calculation data for calculating the above-mentioned processing parameters.

According to another embodiment of the present invention, when the size of the defect exceeds a threshold, the measurement/parameter correction device 180 will stop automatically correcting the processing parameters and generate a warning notification.

According to yet another embodiment, the above-mentioned fine judgment device 140 will not use the recognition result corresponding to the above-mentioned warning notification as the above-mentioned training data for training the above-mentioned classification module after receiving the above-mentioned warning notification.

According to yet another embodiment, the above-mentioned warning notification can notify the system management personnel in real time through text messages or emails, and remind the above-mentioned system management personnel that the identification results corresponding to the above-mentioned warning notification will need manual re-judgment. For example, if it is found that the exposed nickel area on the printed circuit board is greater than 1.0cm2, if the above-mentioned processing parameters are adjusted according to the defect size, the adjustment may be too much, which will seriously affect the subsequent processing process. Therefore, the defect size exceeding the threshold is not used as a data source for adjusting the above-mentioned processing parameters and the above-mentioned training data.

According to yet another embodiment, the present invention can be applied in various stages of the processing process, and please refer to FIG. 2, which shows an intelligent processing system applied to multiple processing equipment according to an embodiment of the present invention Schematic diagram of the device.

According to the embodiment of the present invention, please refer to FIG. 3 , which is a flowchart of an intelligent processing system according to an embodiment of the present invention.

In FIG. 3, step 300 is to start processing.

In step 302, the processing parameters of the processing device 110 are set.

In step 304 , the processing device 110 executes a processing operation and produces a processed object 220 .

In step 306 , mark the processing information on the workpiece 220 . The recording methods of the above-mentioned processing information include characters, numbers, symbols and image codes, wherein the image codes include one-dimensional barcodes and two-dimensional barcodes.

In step 308, the processed object 220 is initially judged by the rough judgment device 130, and if it is judged that the processed object 220 has at least one defect, the image of the processed object 220 is captured to generate at least one processed object image. The rough judging device 130 may further calculate the actual size information of the image of the processed object. The above calculation method is as described above, and will not be repeated here.

In step 310 , it is determined that the processed object 220 has at least one flaw through the rough judgment device 130 , and it is judged to be a flaw, and then step 312 is continued. If the rough judging device 130 preliminarily judges that no defect is found, go to step 322 .

In step 312, the image of the workpiece is re-judged by the precise judging device 140, and the type of defect is identified.

In step 314, after the fine judgment device 140 conducts a re-judgment to determine that the image of the workpiece does have at least one defect, the type of the defect is further identified. The method for identifying the defect is as described above and will not be repeated here. If the fine judging device 140 judges again that the image of the processed object is flawless, then skip to step 322, indicating that the above-mentioned flaw determined by the rough judging device 130 is a false flaw.

In step 316a, the measurement/parameter correction device 180 is used to measure the defect and generate the size of the defect. The method of the above measurement is as described above and will not be repeated here.

In step 316b, the processing information is identified through the processing information acquisition device 160, and the processing parameters are generated. The method for identifying the processing information is as described above, and will not be repeated here.

In step 318, the algorithm of the measurement/parameter correction device 180 calculates the best result of correcting the above-mentioned processing parameters according to the above-mentioned flaw size and the above-mentioned processing parameters, generates the corrected processing parameters, and transmits the above-mentioned corrected processing parameters to the above-mentioned processing device .

In step 320, the processing equipment 110 adjusts the original processing parameters according to the above-mentioned corrected processing parameters, and jumps back to step 304 to continue processing.

In step 322, it is found that there is at least one defect on the processed product 220 in the preliminary judgment of the rough judging device 130 or the fine judgment of the fine judging device 140, so the processing continues.

According to another embodiment of the present invention, please refer to FIG. 4 , which is a flow chart of quality monitoring of an intelligent processing system according to an embodiment of the present invention.

The process of the embodiment in FIG. 3 of the present invention can cooperate with the quality monitoring process in the embodiment of FIG. 4 to increase the accuracy of calculating the above-mentioned corrected processing parameters, wherein the processes in the embodiments of FIG. 3 and FIG. 4 can be executed simultaneously or separately.

In step 402 , the measurement area to be monitored in the design drawing of the workpiece 220 is set (or framed) in the measurement/parameter correction device 180 in advance.

In step 404 , an image of the workpiece 220 is captured through the rough judgment device 130 .

In step 406 , the design drawing of the workpiece 220 is superimposed on the processed image to generate a superimposed image.

In step 408 , measure the size of the preset monitoring measurement area on the superimposed image.

In step 410, monitor dimension information is generated according to the measurement result, and monitor statistics information is further calculated.

In step 412, the monitoring statistical information may be used as calculation data for the measurement/parameter calibration device 180 to calculate and correct processing parameters.

According to another embodiment of the present invention, in step 414, the processing parameters adjusted in the above steps 402-412 can correspond to the number of defects found in the above steps 300-314 at the same time, and the production corresponding to the above processing parameters can be further calculated. yield. It can also check whether the above-mentioned production yield rate meets the qualification standard set by the user (for example, the yield rate needs to reach 95.0%)

In step 416 , the above-mentioned processing parameters can be further passed through virtual metrology (Virtual Metrology, VM for short) to increase the above-mentioned production yield. For example: through virtual measurement and calculation, in the process of laying out the metal copper layer on the substrate during the production of printed circuit boards, reducing the copper sulfate bath electroplating time by 0.5 seconds can reduce the probability of circuit short circuit by 1%. Therefore, adjusting production parameters through virtual measurement can further increase production yield. Through the above-mentioned virtual measurement, it is no longer necessary to measure the monitoring measurement area through the above-mentioned steps 402-412. Instead, the virtual measurement is used to calculate the yield rate of the product, so as to achieve the purpose of full inspection.

To sum up the above, the embodiment of the present invention is a method and system for intelligent processing, which can process the processing and detection of the production line in real time. And further implement traceability, defect determination and calibration of processing equipment with automation.

The present invention is only disclosed in preferred embodiments herein, but anyone skilled in the art should understand that the above embodiments are only used to describe the present invention, and are not intended to limit the scope of patent rights claimed by the present invention. All changes or substitutions that are equal or equivalent to the above-mentioned embodiments should be interpreted as falling within the spirit or scope of the present invention. Therefore, the scope of protection of the present invention should be defined by the scope of the following patent application.

Symbol Description

100: Intelligent processing system, 110: Processing device, 120: Marking device, 130: Rough judgment device, 140: Fine judgment device, 142: Classification module, 160: Processing information acquisition device, 180: Measurement/parameter correction device, 182: parameter optimization module, 200: object to be processed, 220: object to be processed, 300-322: steps, 402-416: steps.

Claims (27)

1. A method for intelligent processing, the method comprising the following steps: Default a processing parameter in a processing device; Processing an object to be processed and producing a processed object; marking a piece of processing information on the object to be processed, the processing information including the processing parameters; Preliminarily inspect the processed object with a rough judgment device, and capture at least one image of the processed object and an image-taking distance information, wherein the rough judgment device is an automatic visual detection device; Then compare the imaging distance information with the plurality of standard size information contained in the rough judgment device to generate the actual size information of the processed object image; Perform re-inspection with an accurate judgment device, and use a classification module to identify the type of flaw contained in the image of the processed object, and generate an identification result, wherein the classification module uses an artificial neural network to perform the identification of the type of flaw; superimposing the design drawing of the processed object with the image of the processed object to generate a superimposed image, and then measuring the defect on the superimposed image to generate a defect size; identifying the processing information to generate the processing parameters; and Using a measurement/parameter correction device through a parameter optimization module, according to the defect size and the processing parameter, calculate the best result of the processing parameter, and generate a corrected processing parameter, and transmit it to the processing device, wherein the parameter The optimization module applies algorithms to perform machining parameter optimization. 2. The intelligent processing method as claimed in claim 1, wherein the method further includes the step of quality monitoring, which is to set at least one monitoring measurement in the design drawing of the processed object in the measurement/parameter calibration device area, and then measure the monitoring measurement area of the superimposed image, and generate at least one monitoring size information. 3. The intelligent processing method according to any one of claim 1 or claim 2, wherein the measurement/parameter correction device further includes generating a monitoring statistical information through statistics of the monitoring size information, and the monitoring statistical information will As the calculation data for calculating the processing parameters. 4. The intelligent processing method according to claim 3, wherein the processing parameter calculated through the monitoring statistics information is used as a reference parameter of a virtual measurement for performing the virtual measurement. 5. The intelligent processing method according to claim 1, wherein the training data of the classification module includes the recognition result and the manual re-judgment result. 6. The intelligent processing method as claimed in claim 1, wherein when the defect size exceeds a threshold value, the measurement/parameter correction device will stop automatically correcting the processing parameters and generate a warning notification. 7. The intelligent processing method as claimed in any one of claim 1 or claim 6, wherein the fine judgment device receives the warning notification, and will not use the identification result corresponding to the warning notification as training the classification module of the training data. 8. The intelligent processing method according to claim 1, wherein the preliminary detection method of the rough judgment device includes 3D contour comparison, position coordinate comparison, color luster comparison and brightness comparison. 9. The intelligent processing method according to claim 1, wherein the standard size information includes resolution per unit area, imaging distance, corresponding pixel matrix, and corresponding actual size and film thickness. 10. The intelligent processing method according to claim 1, wherein the recording method of the processing information includes characters, numbers, symbols and image codes, wherein the image codes include one-dimensional barcodes and two-dimensional barcodes. 11. An intelligent processing system, the system is applied to at least one processing device, and the processing device processes an object to be processed according to a processing parameter to generate a processed object, the system comprising: at least one marking device, marking a processing information on the processed object, wherein the processing information includes at least one processing parameter; A rough judgment device, which is used to initially check whether the processed object has at least one defect, and generate at least one processed object image, wherein the rough judgment device is an automatic visual inspection device; A fine judging device, connected to the rough judging device with a signal, for rechecking whether the image of the processed object has at least one defect, and identifying the type of the defect contained in the image of the processed object to generate a recognition result; A processing information acquisition device, connected to the fine judging device with a signal, to identify the processing information of the image of the processed object and generate the processing parameters; and A measurement/parameter correction device, connected to the fine judgment device for measuring the defect of the image of the processed object, and generating a defect size, and then calculating the processing parameter according to the defect size and the processing parameter For optimal results, a corrected processing parameter is generated and transmitted to the processing device. 12. The intelligent processing system according to claim 11, wherein the initial inspection method of the automatic visual inspection device includes contour comparison, position coordinate comparison, color luster comparison and brightness comparison. 13. The intelligent processing system according to claim 11, wherein the automatic visual inspection device comprises at least one imaging unit, at least one distance measuring unit, a calculation unit and a database. 14. The intelligent processing system according to claim 11, wherein the ranging unit is used to generate imaging distance information for capturing the processed object. 15. The intelligent processing system as claimed in claim 11, wherein the database is used to store a plurality of standard size information, and each of the standard size information includes resolution per unit area, imaging distance, and corresponding pixel matrix And the corresponding actual size and film thickness height. 16. The intelligent processing system according to any one of claims 11 to 15, wherein the calculation unit compares the standard size in the database according to the resolution information of the processed object image and the imaging distance information Information, calculate and generate the image of the processed object with actual size information. 17. The intelligent processing system as claimed in claim 11, wherein the measurement/parameter calibration device further comprises a parameter optimization module, and the parameter optimization module calculates the best result of the processing parameters for the application algorithm. 18. The intelligent processing system according to claim 11, wherein the measurement/parameter correction device, when the processed object has a design drawing, further superimposes the design drawing and the image of the processed object to generate a stack of Combined images are used as the basis for measuring the size of the defect. 19. The intelligent processing system as claimed in any one of claim 11 or claim 18, wherein the measurement/parameter correction device further includes providing at least one monitoring measurement area in the design diagram set in advance, and then The monitoring measurement area of the superimposed image is measured, and at least one monitoring size information is generated. 20. The intelligent processing system as claimed in claim 19, wherein the measurement/parameter correction device further includes the monitoring dimensional information to generate a monitoring statistical information through statistics, and the monitoring statistical information will be used as the calculation for calculating the processing parameters data. 21. The intelligent processing system according to claim 20, wherein the processing parameters calculated through the monitoring statistical information are used as reference parameters for virtual measurement to perform the virtual measurement. 22. The intelligent processing system according to claim 11, wherein the recording method of the processing information of the marking device includes characters, numbers, symbols and image codes, wherein the image codes include one-dimensional barcodes and two-dimensional barcodes. 23. The intelligent processing system according to claim 11, wherein the processing information of the marking device further includes manufacturing batch, product model and manufacturing date. 24. The intelligent processing system as claimed in claim 11, wherein the precise judging device further comprises a classification module, and the classification module is used to identify the defect type by using an artificial neural network. 25. The intelligent processing system according to claim 24, wherein the training data of the classification module includes the recognition result and the manual re-judgment result. 26. The intelligent processing system as claimed in claim 11, wherein when the defect size exceeds a threshold value, the measurement/parameter correction device will stop automatically correcting the processing parameters and generate a warning notification. 27. The intelligent processing system according to any one of claim 11 or claim 26, wherein the fine judgment device receives the warning notification, and will not use the identification result corresponding to the warning notification as a training method for the classification module the training data.

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