A process monitoring method, device and medium based on plastic liner welding

Abstract

The application discloses a kind of based on the process monitoring method, equipment and medium of plastic liner welding, belong to plastic welding process technical field, for solving the technical problem of the existing plastic liner welding process, due to the actual deviation factor of weld, it is easy to cause certain error influence to the overall plastic liner after welding, to reduce the qualified rate of product.Method includes: the ultrasonic detection processing of relevant phased radar is carried out to the plastic liner weld area to be detected after welding, and the weld area ultrasonic data is obtained;Weld area ultrasonic data is carried out defect evaluation processing, and the weld ultrasonic evaluation score of weld area is obtained;If weld ultrasonic evaluation score is greater than first standard score, then the image calculation processing of relevant edge pixel slope is carried out to weld area, and weld flat evaluation score is determined;If weld flat evaluation score is greater than second standard score, the overall configuration of plastic liner is monitored in all directions, and overall configuration evaluation score is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plastic welding process, in particular to a process monitoring method and device based on plastic liner welding and a medium. BACKGROUND

[0002] In the existing plastic liner welding process, a plastic (PA) liner welding device using an infrared lamp as a heating source can be used to weld two incomplete plastic liners to form a complete plastic liner.

[0003] In actual plastic liner welding, tail seat assemblies, positioning assemblies, chuck assemblies, correction mechanisms, and heating mechanisms can be used to realize the overall welding process. During device operation, all welding assemblies are on the same two guide rails, which can ensure high coaxiality and straightness of movement. The operation is stable, high-speed and efficient. The chuck assembly is used for positioning the welding openings on both sides, the tail seat mechanism is used for pressing during welding on both sides, the correction mechanism is used for positioning and calibrating the welding end face, the positioning assembly is used for positioning the end of the workpiece welding piece, and the heating mechanism is used for welding and heating.

[0004] However, in the production process of an enterprise, the welding process of the plastic liner tends to gradually increase the welding error over time, which reduces the yield of the plastic liner. Since the welding seam is melted and then solidified, it is not controllable, and accidental operation errors of the machine can also cause defective products, thereby affecting the overall product qualification rate of the plastic liner. Therefore, monitoring the welding process of the plastic liner is very important. Real-time monitoring of the welding seam can quickly and accurately identify unqualified welding products or remove products with unqualified parameters, further ensuring the overall product qualification rate and improving the production efficiency of good products. SUMMARY

[0005] The embodiments of the present application provide a process monitoring method and device based on plastic liner welding and a medium, which are used to solve the following technical problems: in the existing plastic liner welding process, due to the actual deviation of the welding seam, the overall plastic liner after welding is easily affected by certain errors, thereby reducing the product qualification rate.

[0006] The embodiments of the present application adopt the following technical solutions:

[0007] In one aspect, the embodiment of the present application provides a process monitoring method based on plastic liner welding, comprising: performing ultrasonic detection processing on the plastic liner weld area to be detected after welding under the relative phased radar to obtain weld area ultrasonic data; performing defect evaluation processing on the weld area ultrasonic data by a preset diffraction parallax method to obtain a weld ultrasonic evaluation score of the weld area; if the weld ultrasonic evaluation score is greater than a first standard score, performing image calculation processing on the weld area under the edge pixel slope to obtain a weld joint flatness of the weld area; and determining a weld flatness evaluation score based on the weld joint flatness; if the weld flatness evaluation score is greater than a second standard score, performing omnidirectional monitoring on the overall configuration of the plastic liner by an infrared ray instrument to obtain an overall configuration evaluation score; determining a comprehensive evaluation score of the plastic liner based on the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score; and performing classification processing on the detected plastic liners by the comprehensive evaluation score to obtain a plastic liner quality grade set.

[0008] The embodiment of the present application can more accurately obtain ultrasonic data of the weld area through ultrasonic detection under the relative phased radar, thereby improving the detection accuracy of weld defects. The ultrasonic data is processed by the preset diffraction parallax method to realize automatic defect evaluation, reduce manual intervention, and improve evaluation efficiency. The flatness of the weld joint can be evaluated by performing image calculation on the weld area under the edge pixel slope, which is an index difficult to accurately measure in traditional methods. Combined with the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score, the plastic liner can be comprehensively evaluated to provide more comprehensive quality information. By setting the standard score, the detected plastic liners can be classified according to the quality grade, which is helpful for quality control in the production process. The automatic and standardized detection process can reduce the detection time and improve the production efficiency. The subjectivity and human error in the manual detection process are reduced, and the consistency and reliability of the detection are improved.

[0009] In a feasible implementation, the weld area of the plastic liner to be detected after welding is subjected to ultrasonic detection processing under phased radar to obtain weld area ultrasonic data, specifically including: testing and detecting key parameters of a standard plastic liner weld area by phased array detection technology to obtain basic detection parameters; wherein the key parameters at least include: scanning mode, scanning coverage, matching degree of probe and wedge, focusing parameters and fan scanning angle range; matching the basic detection parameters with an ultrasonic radar device for detecting the plastic liner weld area to obtain a special ultrasonic radar device; exciting ultrasonic signals under full coverage of the weld area by the special ultrasonic radar device, and automatically controlling the gain of the excited ultrasonic signals within a pulse repetition frequency period according to key technical indicators in the special ultrasonic radar device to obtain effective ultrasonic excitation signals; wherein the key technical indicators include: channel number, sampling frequency, digitization bit number and time gain; recovering ultrasonic reflection signals corresponding to the effective ultrasonic excitation signals; converting the ultrasonic reflection signals into digital signals to obtain the weld area ultrasonic data based on the current plastic liner weld area.

[0010] In a feasible implementation, the weld area ultrasonic data is subjected to defect evaluation processing by a preset diffraction parallax method to obtain a weld ultrasonic evaluation score of the weld area, specifically including: identifying time delay data and intensity change data in the weld area ultrasonic data; identifying the defect type of the weld area based on the time delay data and the intensity change data by a preset defect information template to obtain defect basic data; wherein the defect basic data includes: noise, position, deviation, depth, height, length and defect property type; generating an image under diffraction effect based on the time-amplitude waveform data between the time delay data and the intensity change data by the diffraction parallax method to obtain a defect image; wherein the defect image is a cross-sectional image of the weld area; evaluating the defect level of the defect image to obtain an unusable defect level and a usable defect level; wherein the unusable defect level is a defect level with serious defects and cannot be used, and the usable defect level is a defect level with slight defects and can be used; performing decision evaluation on defect parameters in the defect image under the usable defect level according to a preset expert evaluation system to obtain the weld ultrasonic evaluation score of the weld area; wherein the defect parameters include: defect size, defect position, defect type, defect property, defect structure stress and defect area size.

[0011] In an implementable embodiment, if the weld ultrasonic evaluation score is greater than the first standard score, an image calculation process on the edge pixel slope of the weld region is performed to obtain the weld joint flatness of the weld region, specifically comprising: if the weld ultrasonic evaluation score is greater than the first standard score, an initial weld region image is obtained by image acquisition processing of the weld region under uniform rotation through an industrial camera; noise reduction and grayscale processing is performed on the initial weld region image to obtain a weld region image; wherein the weld region is an extended image of the outer surface of the plastic liner; an edge contour pixel region perpendicular to the weld in the weld region image is identified; wherein the edge contour pixel region includes: weld region pixels, weld left side plastic liner region pixels, and weld right side plastic liner region pixels; the weld region pixels and the weld left side plastic liner region pixels are jointly determined as left side weld joint region pixels, and the weld region pixels and the weld right side plastic liner region pixels are jointly determined as right side weld joint region pixels; background pixel separation processing is performed on the left side weld joint region pixels and the right side weld joint region pixels respectively according to foreground and background separation technology to obtain left side weld edge pixels and right side weld edge pixels respectively; slope calculation of the edge pixels is performed on the left side weld edge pixels and the right side weld edge pixels respectively through a curve slope calculation algorithm to obtain left side weld joint slope and right side joint slope respectively; based on the left side weld joint slope and the right side weld joint slope, left side weld joint flatness and right side weld joint flatness are determined; wherein the weld joint flatness includes the left side weld joint flatness and the right side weld joint flatness.

[0012] In an implementable embodiment, based on the weld joint flatness, a weld flatness evaluation score is determined, specifically comprising: if the difference between the left side weld joint flatness and the right side weld joint flatness is greater than a first preset threshold, the current plastic liner is determined as a defective product mark processing; if the difference between the left side weld joint flatness and the right side weld joint flatness is less than or equal to the first preset threshold, a ratio calculation is performed between the left side weld joint flatness and the right side weld joint flatness to obtain a left and right side flatness ratio; based on the left and right side flatness ratio, a weld flatness evaluation process is performed on the plastic liner weld region according to a preset expert evaluation system to obtain the weld flatness evaluation score.

[0013] In an implementable embodiment, if the weld flatness evaluation score is greater than the second standard score, the overall configuration of the plastic liner is monitored in an all-round way by an infrared ray instrument to obtain an overall configuration evaluation score, specifically including: if the weld flatness evaluation score is greater than the second standard score, the length of the outer surface of the current plastic liner is measured by the infrared ray instrument to obtain a length parameter; the width of the outer surface of the current plastic liner is measured by the infrared ray instrument to obtain a width parameter; and the height of the outer surface of the current plastic liner is measured by the infrared ray instrument to obtain a height parameter; wherein the length parameter, the width parameter and the height parameter are all static configuration parameters; the current plastic liner is rotated and the current plastic liner in the rotating state is measured for rotational symmetry to obtain a dynamic configuration parameter; the dynamic configuration parameter and the static configuration parameter are combined to obtain an overall configuration parameter; and the overall configuration parameter is evaluated by a preset expert evaluation system to obtain the overall configuration evaluation score.

[0014] In an implementable embodiment, based on the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score, a comprehensive evaluation score of the plastic liner is determined, specifically including: the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score are divided by a proportion coefficient by a plastic liner demand proportion degree to obtain a first weight coefficient, a second weight coefficient and a third weight coefficient respectively; the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score are calculated for evaluation score by the first weight coefficient, the second weight coefficient and the third weight coefficient to obtain the comprehensive evaluation score of the current plastic liner.

[0015] In an implementable embodiment, the several plastic liners after detection are classified by the comprehensive evaluation score to obtain a plastic liner quality grade set, specifically including: the comprehensive evaluation score is divided by a range by a preset plastic liner quality grade range to determine the quality grade of each comprehensive evaluation score; and the corresponding plastic liner is classified and set according to the quality grade of each comprehensive evaluation score to determine the plastic liner quality grade set.

[0016] In a second aspect, the embodiments of the present application further provide a process monitoring device based on plastic liner welding, the device comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to execute the process monitoring method based on plastic liner welding of any of the above-mentioned embodiments.

[0017] In a third aspect, the embodiments of the present application further provide a non-volatile computer storage medium, which is a non-volatile computer readable storage medium, and stores at least one program, each of which includes instructions that, when executed by a terminal, cause the terminal to perform the process monitoring method based on plastic liner welding described in any of the above embodiments.

[0018] The present application provides a process monitoring method, device and medium based on plastic liner welding. Compared with the prior art, the embodiments of the present application have the following beneficial technical effects:

[0019] 1. Improve detection accuracy: through ultrasonic detection under phased array radar, ultrasonic data of the weld area can be more accurately obtained, thereby improving the detection accuracy of weld defects.

[0020] 2. Automatic defect evaluation: the ultrasonic data is processed using the preset diffraction parallax method to realize automatic defect evaluation, reducing manual intervention and improving evaluation efficiency.

[0021] 3. Weld flatness evaluation: image calculation under edge pixel slope of the weld area can evaluate the flatness of the weld joint, which is an index difficult to accurately measure in traditional methods.

[0022] 4. Comprehensive evaluation capability: combining weld ultrasonic evaluation score, weld flatness evaluation score and overall configuration evaluation score, the plastic liner can be comprehensively evaluated to provide more comprehensive quality information.

[0023] 5. Standardized quality level classification: by setting a standard score, the detected plastic liner can be classified according to quality level, which helps quality control in the production process.

[0024] 6. Improve production efficiency: automated and standardized detection process can reduce detection time and improve production efficiency.

[0025] 7. Reduce human error: reduces subjectivity and human error in manual detection process, improves detection consistency and reliability.

[0026] 8. Real-time monitoring and feedback: the method can monitor the quality of the welding process in real time and provide feedback when a certain standard is reached, which helps to adjust the production process in time.

[0027] 9. Reduce costs: by improving detection efficiency and reducing defect rate, product rework and repair costs can be reduced.

[0028] 10. Improve product reliability: by ensuring the quality of plastic liner welding, improve product reliability and service life. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only represent some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without any creative effort. In the drawings:

[0030] Figure 1 A process monitoring method based on plastic liner welding is provided for the embodiments of the present application;

[0031] Figure 2 A plastic liner weld area welding error schematic diagram is provided for the embodiments of the present application;

[0032] Figure 3 A structure schematic diagram of a process monitoring device based on plastic liner welding is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0033] In order to make the person skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only represent some embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the scope of protection of the present application.

[0034] The embodiments of the present application provide a process monitoring method based on plastic liner welding, as shown in Figure 1 The process monitoring method based on plastic liner welding specifically includes steps S101-S106:

[0035] S101, the ultrasonic detection processing of the plastic liner weld area to be detected after welding under the phased array radar is performed, and the weld area ultrasonic data is obtained.

[0036] Specifically, the key parameters of the standard plastic liner weld area need to be tested and detected by the phased array detection technology first, and the basic detection parameters are obtained. The key parameters at least include: scanning mode, scanning coverage, matching degree of probe and wedge, focusing parameter and fan scanning angle range.

[0037] Further, the basic detection parameters are matched with the ultrasonic radar equipment for detecting the plastic liner weld area, and the special ultrasonic radar equipment is obtained.

[0038] Further, the weld area is subjected to ultrasonic signal excitation processing under full coverage by a special ultrasonic radar device, and according to key technical indicators in the special ultrasonic radar device, the effective ultrasonic excitation signal is obtained by automatic gain control of the excited ultrasonic signal within the time period of the pulse repetition frequency. The key technical indicators include: channel number, sampling frequency, digitization bit number, and time gain.

[0039] Further, the ultrasonic reflection signal corresponding to the effective ultrasonic excitation signal is recovered. The ultrasonic reflection signal is then subjected to digital signal conversion processing to obtain weld area ultrasonic data based on the current plastic liner weld area.

[0040] In one embodiment, during the detection of the standard plastic liner weld area using the phased array detection device, test key parameters such as scan mode (linear, circumferential, etc.), scan coverage (weld width), probe and wedge matching (to ensure good coupling of the probe and the detection medium), focusing parameters (focusing depth and width), and fan scan angle range (detection angle). Compare the test basic detection parameters with the existing ultrasonic radar device. According to the matching result, customize or adjust the ultrasonic radar device to match the detection requirements of the standard plastic liner weld area. Use the special ultrasonic radar device to excite the ultrasonic signal of the weld area. Within the time period of the pulse repetition frequency, according to the key technical indicators (channel number, sampling frequency, digitization bit number, time gain), automatic gain control is performed to ensure signal quality. Receive the reflection signal of the excited ultrasonic signal. The recovered ultrasonic reflection signal is subjected to digital processing to obtain the ultrasonic data of the weld area.

[0041] As a feasible implementation, by optimizing the focusing parameters and fan scan angle, the detection depth and resolution are improved, and the weld defects can be more clearly identified. The application of automatic gain control and other technical indicators improves the automation degree of signal processing and reduces manual intervention. Combined with phased array technology and special equipment, the weld defects can be more accurately identified, and the missed detection and false detection are reduced. Moreover, the customized ultrasonic radar device is more suitable for specific types of plastic liner weld detection, improving the applicability and detection effect of the device.

[0042] S102, by the preset diffraction parallax method, the weld area ultrasonic data is subjected to defect evaluation processing to obtain a weld ultrasonic evaluation score of the weld area.

[0043] Specifically, first, identify the time delay data and intensity change data in the weld area ultrasonic data.

[0044] Further, the weld area is subjected to defect type identification based on the time delay data and intensity variation data, using a pre-defined defect information template, to obtain defect base data. The defect base data includes noise, location, deviation, depth, height, length, and defect property type.

[0045] Further, the defect base data is subjected to image generation processing under diffraction effect, using a diffraction parallax method, based on time-amplitude waveform data between the time delay data and the intensity variation data, to obtain a defect image. The defect image is a cross-sectional image of the weld area.

[0046] Further, defect level assessment is performed on the defect image to obtain unusable defect levels and usable defect levels. The unusable defect level is a defect level with severe defects that cannot be used, and the usable defect level is a defect level with slight defects that can be used.

[0047] Further, a pre-defined expert evaluation system is used to perform decision evaluation on defect parameters in the defect image under the usable defect level, to obtain a weld ultrasonic evaluation score of the weld area. The defect parameters include defect size, defect location, defect type, defect property, defect structural stress, and defect area size.

[0048] In one real-time example, Figure 2 A plastic liner weld area welding error schematic diagram is provided for the embodiments of the present application, as shown in Figure 2 Ultrasonic data of the weld area is collected using an ultrasonic detection device. Time delay data and intensity variation data are extracted from the ultrasonic data through signal processing technology. Defect type identification is performed by designing or using an existing defect information template, inputting the extracted time delay data and intensity variation data into a defect identification algorithm, and the algorithm identifies the defect type based on these data and generates defect base data, including noise, location, deviation, depth, height, length, and defect property type. Meanwhile, time-amplitude waveform data is generated by applying a diffraction parallax method in combination with the time delay data and the intensity variation data. Defect images, i.e., cross-sectional images of the weld area, are generated using these data. The generated defect images can also be evaluated to determine defect levels. Unusable defect levels (severe defects) and usable defect levels (slight defects) are distinguished. Moreover, a pre-defined expert evaluation system is used to analyze the defect images under the usable defect level. The system performs decision evaluation based on parameters such as defect size, location, type, property, structural stress, and area size. A weld ultrasonic evaluation score of the weld area is generated.

[0049] As a feasible implementation, the type of defect in the weld area can be accurately identified by the time delay and intensity change data, the defect image generated by the diffraction parallax method provides intuitive defect information, which helps further analysis. Through defect level evaluation, the severity of the defect can be quickly distinguished, which helps to decide whether to continue using the part. Finally, the use of expert evaluation system improves the efficiency and accuracy of defect parameter evaluation.

[0050] S103, if the weld ultrasonic evaluation score is greater than the first standard score, the image calculation process of the edge pixel slope in the weld area is performed to obtain the weld joint flatness of the weld area. Based on the weld joint flatness, the weld flatness evaluation score is determined.

[0051] Specifically, if the weld ultrasonic evaluation score is greater than the first standard score, the initial weld area image is obtained by image acquisition processing of the weld area under uniform rotation through an industrial camera. The weld area image is obtained by noise reduction and gray processing of the initial weld area image. The weld area is the extension image of the outer surface of the plastic liner.

[0052] Further, the edge contour pixel area perpendicular to the weld in the weld area image is identified. The edge contour pixel area includes: weld area pixels, weld left plastic liner area pixels and weld right plastic liner area pixels.

[0053] Further, the weld area pixels and the weld left plastic liner area pixels are determined as left weld joint area pixels, and the weld area pixels and the weld right plastic liner area pixels are determined as right weld joint area pixels.

[0054] Further, according to the foreground and background separation technology, the left weld joint area pixels and the right weld joint area pixels are respectively separated from the background pixels to obtain the left weld edge pixels and the right weld edge pixels.

[0055] Further, the left weld edge pixels and the right weld edge pixels are respectively calculated by the curve slope calculation algorithm to obtain the left weld joint slope and the right joint slope.

[0056] Further, the left weld joint slope and the right weld joint slope are combined to determine the left weld joint flatness and the right weld joint flatness. The weld joint flatness includes: left weld joint flatness and right weld joint flatness.

[0057] Further, if the difference between the left weld joint flatness and the right weld joint flatness is greater than a first preset threshold, the current plastic liner is determined as a defective product marking processing. If the difference between the left weld joint flatness and the right weld joint flatness is less than or equal to the first preset threshold, the ratio between the left weld joint flatness and the right weld joint flatness is calculated to obtain a left-right flatness ratio. Finally, in combination with a preset expert evaluation system and based on the left-right flatness ratio, the weld joint area of the plastic liner is evaluated for weld joint flatness to obtain a weld joint flatness evaluation score.

[0058] In one embodiment, as shown in Figure 2 the image acquisition and processing are first performed: an industrial camera is used to capture images of the weld joint area under uniform rotation. The collected images are processed to reduce noise. The denoised images are processed to simplify the image data. Then the edge contour pixel recognition is realized: the image processing algorithm is used to identify the edge contour pixels in the weld joint area image perpendicular to the weld joint. The weld joint area pixels, the left plastic liner area pixels of the weld joint, and the right plastic liner area pixels of the weld joint are determined. Then the weld joint area pixel determination is needed: the weld joint area pixels and the left plastic liner area pixels of the weld joint are combined to determine the left weld joint connection area pixels. The weld joint area pixels and the right plastic liner area pixels of the weld joint are combined to determine the right weld joint connection area pixels. Then the background pixel separation is used: the foreground and background separation technology is used to separate the background pixels from the left and right weld joint connection area pixels respectively. The left weld joint edge pixels and the right weld joint edge pixels are obtained. The curve slope calculation is also needed: the curve slope calculation algorithm is applied to calculate the slope of the left and right weld joint edge pixels respectively. The left weld joint connection slope and the right weld joint connection slope are obtained. Then the weld joint flatness evaluation is performed: the left and right weld joint connection flatness is determined in combination with the left and right weld joint connection slopes. It is checked whether the flatness difference is greater than a preset threshold to determine whether it is a defective product. If the difference is less than or equal to the threshold, the left-right flatness ratio is calculated. Finally, the expert evaluation system is used: the preset expert evaluation system is used in combination with the left-right flatness ratio to evaluate the flatness of the weld joint area. The weld joint flatness evaluation score is generated.

[0059] As a feasible implementation, the image quality is improved through denoising and grayscale processing, which facilitates subsequent processing. Accurate identification of the weld joint area and its edge contours on both sides facilitates subsequent analysis. By calculating the slope of the weld joint connection, the flatness of the weld joint connection can be evaluated. By comparing the difference between the flatness of the weld joint connection on both sides, defective products that may not meet the requirements can be quickly identified. The automated evaluation process reduces the need for manual detection, improving production efficiency. The number of defective products can also be reduced, reducing the increase in costs caused by defective products. Further, the flatness of the weld joint connection is ensured, improving the overall quality of the plastic liner.

[0060] S104, if the weld flatness evaluation score is greater than the second standard score, the overall configuration of the plastic liner is monitored in all directions by the infrared ray instrument to obtain an overall configuration evaluation score.

[0061] Specifically, if the weld flatness evaluation score is greater than the second standard score, the length of the outer surface of the current plastic liner is measured by laser through the infrared ray instrument to obtain a length parameter.

[0062] Further, the width of the outer surface of the current plastic liner is measured by laser to obtain a width parameter, and the height of the outer surface of the current plastic liner is measured by laser to obtain a height parameter. The length parameter, the width parameter, and the height parameter are all static configuration parameters.

[0063] Further, the current plastic liner is rotated and the dynamic configuration parameter is obtained by measuring the rotational symmetry of the current plastic liner in the rotating state. The dynamic configuration parameter and the static configuration parameter are combined to obtain the overall configuration parameter.

[0064] Further, the overall configuration parameter is evaluated by the preset expert evaluation system to obtain an overall configuration evaluation score.

[0065] In one embodiment, first, the weld flatness evaluation score is calculated according to the previous weld flatness evaluation process. The calculated weld flatness evaluation score is compared with the preset second standard score. If the weld flatness evaluation score is greater than the second standard score, the outer surface of the plastic liner is measured by laser using the infrared ray instrument. The length of the outer surface of the plastic liner is measured using the infrared ray instrument to obtain a length parameter. Then, the width of the outer surface of the plastic liner is measured to obtain a width parameter. Finally, the height of the outer surface of the plastic liner is measured to obtain a height parameter. Then, the obtained length parameter, width parameter, and height parameter are recorded, which represent the static configuration of the plastic liner. Then, the plastic liner needs to be rotated to be in a rotating state. The dynamic configuration parameter is obtained by measuring the rotational symmetry of the plastic liner in the rotating state using the infrared ray instrument. Then, the static configuration parameter and the dynamic configuration parameter are combined to obtain the overall configuration parameter of the plastic liner. Finally, the overall configuration parameter is evaluated using the preset expert evaluation system. The overall configuration evaluation score is generated according to the overall configuration parameter.

[0066] S105, based on the weld ultrasonic evaluation score, the weld flatness evaluation score, and the overall configuration evaluation score, a comprehensive evaluation score of the plastic liner is determined.

[0067] Specifically, the welding ultrasonic evaluation score, the welding flatness evaluation score and the overall configuration evaluation score are divided by a proportion coefficient according to the proportion degree of the plastic liner demand, to obtain a first weight coefficient, a second weight coefficient and a third weight coefficient respectively.

[0068] Further, the welding ultrasonic evaluation score, the welding flatness evaluation score and the overall configuration evaluation score are weighted according to the first weight coefficient, the second weight coefficient and the third weight coefficient, to obtain a comprehensive evaluation score of the current plastic liner.

[0069] In one embodiment, the weights are dynamically allocated according to the product technical standards: 1) the quantification basis (proportion degree of demand) of the welding ultrasonic evaluation score can be the pressure rating (70 MPa→60% of air tightness proportion), and the weight coefficient is a=0.6; 2) the quantification basis (proportion degree of demand) of the overall configuration evaluation score can be the cycle life (5000 times→30% of structural strength proportion), and the weight coefficient is β=0.3; 3) the quantification basis (proportion degree of demand) of the welding flatness evaluation score can be the surface defect tolerance (10% of appearance proportion), and the weight coefficient is γ=0.1. Then, a comprehensive evaluation score calculation model is used: S_{comprehensive}=(S_{ultrasonic}\times\alpha)+(S_{configuration}\times\beta)+(S_{flatness}\times\gamma). For example: the ultrasonic detection score S_ultrasonic=85 points (air tightness meets the standard); the configuration analysis score S_configuration=90 points (no stress concentration); the flatness score S_flatness=70 points (there are slight scratches). Finally, the calculation result is S_{comprehensive}=(85\times0.6)+(90\times0.3)+(70\times0.1)=51+27+7=85 points, so the comprehensive evaluation score of the current plastic liner is determined to be qualified.

[0070] S106, classify the plastic liners after detection according to the comprehensive evaluation score, to obtain a plastic liner quality level set.

[0071] Specifically, the comprehensive evaluation score is divided into a range according to a preset plastic liner quality level range, to determine the quality level of each comprehensive evaluation score. According to the quality level of each comprehensive evaluation score, the corresponding plastic liner is classified and set, to determine the plastic liner quality level set.

[0072] In addition, the embodiment of the present application also provides a process monitoring device based on plastic liner welding, as shown in Figure 3 As shown in the figure, the process monitoring device based on plastic liner welding 300 specifically comprises:

[0073] At least one processor 301. And, the memory 302 connected in communication with the at least one processor 301. Wherein the memory 302 stores instructions capable of being executed by the at least one processor 301, to enable the at least one processor 301 to execute:

[0074] The ultrasonic detection process of the plastic liner weld area to be detected after welding is carried out under the phased radar, and the weld area ultrasonic data is obtained.

[0075] The weld area ultrasonic data is processed by the preset diffraction parallax method to obtain the weld area ultrasonic evaluation score.

[0076] If the weld ultrasonic evaluation score is greater than the first standard score, the weld area is subjected to image calculation processing related to the edge pixel slope to obtain the weld connection flatness of the weld area; and based on the weld connection flatness, the weld flatness evaluation score is determined.

[0077] If the weld flatness evaluation score is greater than the second standard score, the overall configuration of the plastic liner is monitored in all directions by the infrared ray instrument to obtain the overall configuration evaluation score.

[0078] Based on the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score, the comprehensive evaluation score of the plastic liner is determined.

[0079] The detected plastic liners are classified by the comprehensive evaluation score to obtain a plastic liner quality grade set.

[0080] The application embodiment can more accurately obtain the ultrasonic data of the weld area through ultrasonic detection under the phased radar, thereby improving the detection accuracy of the weld defects. The ultrasonic data is processed by the preset diffraction parallax method to realize automatic defect evaluation, reduce manual intervention and improve evaluation efficiency. The image calculation of the weld area under the edge pixel slope can evaluate the flatness of the weld connection, which is an index difficult to accurately measure in traditional methods. Combined with the weld ultrasonic evaluation score, the weld flatness evaluation score and the overall configuration evaluation score, the plastic liner can be comprehensively evaluated to provide more comprehensive quality information. By setting the standard score, the detected plastic liners can be classified according to the quality grade, which is helpful for quality control in the production process. The automatic and standardized detection process can reduce the detection time and improve the production efficiency. The subjectivity and human error in the manual detection process are reduced, and the consistency and reliability of the detection are improved.

[0081] The various embodiments in the present application are described in a progressive manner, and the same or similar parts among the various embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, the device and medium embodiments are described simply because they are substantially similar to the method embodiments, and the relevant parts can be referred to the description of the method embodiments.

[0082] The device and medium provided by the embodiments of the present application are one-to-one corresponding to the method, and therefore, the device and medium also have the similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0083] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. In addition, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0084] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer or other programmable data processing apparatus produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the function specified in the flow or block.

[0085] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the function specified in the flow or block.

[0086] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0087] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0088] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory is an example of computer readable storage media.

[0089] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to encompass a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0091] ​​The above merely provides an example of the present application, but is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for monitoring a process based on welding of a plastic liner, characterized in that, The method includes: Ultrasonic testing was performed on the weld area of ​​the plastic inner liner after welding to obtain ultrasonic data of the weld area under phased radar. The ultrasonic data of the weld area is processed for defect assessment using a preset diffraction parallax method to obtain the ultrasonic assessment score of the weld area. If the ultrasonic evaluation score of the weld is greater than the first standard score, then image calculation processing is performed on the weld area under the slope of the relevant edge pixels to obtain the weld connection flatness of the weld area; and based on the weld connection flatness, the weld flatness evaluation score is determined. If the weld flatness evaluation score is greater than the second standard score, the overall configuration of the plastic liner will be monitored in all directions by an infrared ray instrument to obtain the overall configuration evaluation score. Based on the ultrasonic evaluation score of the weld, the weld smoothness evaluation score, and the overall configuration evaluation score, the comprehensive evaluation score of the plastic liner is determined. Based on the comprehensive evaluation score, the tested plastic liners are classified to obtain a set of plastic liner quality grades.

2. The method of claim 1, wherein the method is a process monitoring method for a plastic liner welding process. The weld area of ​​the plastic inner liner to be inspected after welding is subjected to ultrasonic testing under phased-array radar to obtain ultrasonic data of the weld area, specifically including: Using phased array detection technology, key parameters of the weld area of ​​a standard plastic liner are tested to obtain basic detection parameters; among which, the key parameters include at least: scanning mode, scanning coverage, probe and wedge matching degree, focusing parameters, and sector scanning angle range; The basic detection parameters are matched with those of the ultrasonic radar device used to detect the weld area of ​​the plastic liner to obtain a dedicated ultrasonic radar device. The dedicated ultrasonic radar equipment is used to excite ultrasonic signals over the entire weld area. Based on the key technical indicators of the dedicated ultrasonic radar equipment, the excited ultrasonic signal is automatically gain-controlled within the pulse repetition frequency time period to obtain an effective ultrasonic excitation signal. The key technical indicators include: number of channels, sampling frequency, digitization bit depth, and time gain. Recover the ultrasonic reflected signal corresponding to the effective ultrasonic excitation signal; The ultrasonic reflected signal is converted into a digital signal to obtain ultrasonic data of the weld area based on the current weld area of ​​the plastic liner.

3. The method of claim 1, wherein the method is used for monitoring a process of welding a plastic liner. By using a preset diffraction parallax method, the ultrasonic data of the weld area is processed for defect assessment to obtain the ultrasonic assessment score of the weld area, specifically including: Identify the time delay data and intensity change data in the ultrasonic data of the weld area; Using a preset defect information template and based on the time delay data and the intensity change data, the defect type of the weld area is identified to obtain basic defect data; wherein, the basic defect data includes: noise, location, deviation, depth, height, length and defect nature type; The defect basic data is subjected to image generation processing under diffraction effect based on time-amplitude waveform data between the time delay data and the intensity change data, to obtain a defect image; wherein the defect image is a cross-sectional image of the weld area; The defect image is subjected to defect level evaluation, to obtain an unusable defect level and a usable defect level; wherein the unusable defect level is a defect level with serious defects and cannot be used, and the usable defect level is a defect level with slight defects and can be used; According to a preset expert evaluation system, a defect parameter in the defect image under the usable defect level is subjected to decision evaluation, to obtain a weld ultrasonic evaluation score of the weld area; wherein the defect parameter includes: defect size, defect position, defect type, defect nature, defect structure stress, and defect area size.

4. The method of claim 1, wherein the method is a process monitoring method for a plastic liner welding process. If the weld ultrasonic evaluation score is greater than a first standard score, the weld area is subjected to image calculation processing under an edge pixel slope, to obtain a weld joint flatness of the weld area, specifically including: If the weld ultrasonic evaluation score is greater than a first standard score, the weld area under uniform rotation is subjected to image acquisition processing by an industrial camera, to obtain an initial weld area image; The initial weld area image is subjected to noise reduction and grayscale processing, to obtain a weld area image; wherein the weld area is an extended image of the outer surface of the plastic liner; An edge contour pixel area perpendicular to the weld in the weld area image is identified; wherein the edge contour pixel area includes: a weld area pixel, a plastic liner area pixel on the left side of the weld, and a plastic liner area pixel on the right side of the weld; The weld area pixel and the plastic liner area pixel on the left side of the weld are collectively determined as a left side weld joint area pixel, and the weld area pixel and the plastic liner area pixel on the right side of the weld are collectively determined as a right side weld joint area pixel; According to foreground and background separation technology, the left side weld joint area pixel and the right side weld joint area pixel are respectively subjected to background pixel separation processing, to respectively obtain a left side weld edge pixel and a right side weld edge pixel; The left side weld edge pixel and the right side weld edge pixel are respectively subjected to edge pixel slope calculation by a curve slope calculation algorithm, to respectively obtain a left side weld joint slope and a right side weld joint slope; Based on the left side weld joint slope and the right side weld joint slope, a left side weld joint flatness and a right side weld joint flatness are determined; wherein the weld joint flatness includes the left side weld joint flatness and the right side weld joint flatness.

5. The method of claim 4, wherein the method further comprises: Based on the weld joint flatness, a weld flatness evaluation score is determined, specifically including: If a difference between the left side weld joint flatness and the right side weld joint flatness is greater than a first preset threshold, the current plastic liner is determined as a substandard mark. if the difference between the left-side weld joint flatness and the right-side weld joint flatness is less than or equal to the first preset threshold, a ratio calculation is performed between the left-side weld joint flatness and the right-side weld joint flatness to obtain a left-right side flatness ratio; according to a preset expert evaluation system and based on the left-right side flatness ratio, an evaluation processing is performed on the plastic liner weld joint area regarding weld joint flatness to obtain a weld joint flatness evaluation score.

6. The method of claim 1, wherein the method is a process monitoring method for a plastic liner welding process. if the weld joint flatness evaluation score is greater than a second standard score, a full-range monitoring is performed on the overall configuration of the plastic liner by an infrared ray instrument to obtain an overall configuration evaluation score, specifically including: if the weld joint flatness evaluation score is greater than the second standard score, a laser measurement is performed on the length of the outer surface of the current plastic liner by the infrared ray instrument to obtain a length parameter; a laser measurement is performed on the width of the outer surface of the current plastic liner to obtain a width parameter, and a laser measurement is performed on the height of the outer surface of the current plastic liner to obtain a height parameter; wherein the length parameter, the width parameter, and the height parameter are all static configuration parameters; a rotation processing is performed on the current plastic liner, and a rotation symmetry measurement is performed on the current plastic liner in a rotating state to obtain a dynamic configuration parameter; the dynamic configuration parameter and the static configuration parameter are combined to obtain an overall configuration parameter; an evaluation processing is performed on the overall configuration parameter regarding the overall configuration by a preset expert evaluation system to obtain the overall configuration evaluation score.

7. The method of claim 1, wherein the method is a process monitoring method for a plastic liner welding process. based on the weld joint ultrasonic evaluation score, the weld joint flatness evaluation score, and the overall configuration evaluation score, a comprehensive evaluation score of the plastic liner is determined, specifically including: by a plastic liner demand proportion degree, a proportion coefficient division processing is performed on the weld joint ultrasonic evaluation score, the weld joint flatness evaluation score, and the overall configuration evaluation score to respectively obtain a first weight coefficient, a second weight coefficient, and a third weight coefficient; according to the first weight coefficient, the second weight coefficient, and the third weight coefficient, a weight calculation is performed on the weld joint ultrasonic evaluation score, the weld joint flatness evaluation score, and the overall configuration evaluation score regarding the evaluation score to obtain the comprehensive evaluation score of the current plastic liner.

8. The method of claim 1, wherein the method is a process monitoring method for a plastic liner welding process. by the comprehensive evaluation score, a classification processing is performed on a plurality of plastic liners after detection to obtain a plastic liner quality grade set, specifically including: by a preset plastic liner quality grade range, a range division is performed on the comprehensive evaluation score to determine the quality grade of each comprehensive evaluation score; according to the quality grade of each comprehensive evaluation score, a corresponding plastic liner is processed in a classification set to determine the plastic liner quality grade set.

9. A process monitoring device based on welding of a plastic liner, characterized by the device includes: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a process monitoring method based on plastic liner welding according to any one of claims 1-8.

10. A non-transitory computer storage medium, comprising, The storage medium is a nonvolatile computer readable storage medium, and the nonvolatile computer readable storage medium stores at least one program, and each program includes instructions, which, when executed by a terminal, causes the terminal to execute a process monitoring method based on plastic liner welding according to any one of claims 1-8.

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