CN109447403A - A kind of welding defect analysis system and method based on big data - Google Patents
A kind of welding defect analysis system and method based on big data Download PDFInfo
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Abstract
The invention belongs to welding quality control technology fields.In order to solve the detection mode used in existing welding process and quality controling mode there are hysteresis quality and blindness, the welding defect analysis system based on big data that the invention discloses a kind of.The system includes parameter acquisition unit, input unit, Database Unit and output unit connected to the processor simultaneously, wherein parameter acquisition unit is for being acquired welding process parameter and being transmitted to processor, input unit is transmitted to processor for information parameter before welding, Database Unit wraps for storing data, and output unit is used for the comparison result of output processor.The auxiliary operation welded using present system welding quality can quickly be judged after the completion of welding process and welding, hysteresis quality and blindness in the prior art be overcome, to improve welding efficiency.
Description
Technical Field
The invention belongs to the technical field of welding quality control, and particularly relates to a welding defect analysis system and method based on big data.
Background
With the emphasis on products in various industries and the higher requirements on the product quality (especially the welding quality) in China, the overall level of the welding technology in China is greatly improved at the present stage, but a larger promotion space is provided in the aspects of real-time welding quality monitoring and welding defect reason analysis.
Wherein, in present welding process, the reason that appears to welding defect is difficult to seek and can't really realize the error control of preventing to welding quality, do not accomplish online real-time detection to the welding defect of product very much, rely on operating personnel's observation and experience among the welding process to control the quality of welding process, dissect the test with the help of the later stage after the welding is accomplished simultaneously, radiographic inspection, methods such as ultrasonic wave detect after welding, and there are certain hysteresis quality and blindness in above-mentioned current detection mode and quality control mode, the further promotion of welding operation efficiency and quality has been restricted.
Disclosure of Invention
In order to solve the problems of hysteresis and blindness of a detection mode and a quality control mode adopted in the existing welding process, the invention provides a welding defect analysis system based on big data, and further improvement of welding quality and efficiency is realized. The welding defect analysis system based on the big data comprises a parameter acquisition unit, an input unit, a processor, a database unit and an output unit; wherein,
the parameter acquisition unit is connected with the processor, consists of a plurality of different sensors and is used for acquiring different welding process parameters on welding equipment and a welding piece in the welding process and transmitting the parameters to the processor;
the input unit is connected with the processor and is used for inputting relevant welding information to the processor;
the database unit is connected with the processor and used for storing the information integrated by the processor and outputting the related information to the processor;
the output unit is connected with the processor and used for outputting the relevant information processed by the processor;
the processor is used for receiving the data in the parameter acquisition unit and the input unit, integrating and outputting the data to the database unit for storage, calling the data in the database unit, comparing and analyzing the data with the data in the parameter acquisition unit and the input unit, and outputting the result through the output unit.
Preferably, the input unit is configured to input the pre-welding information parameter and the post-welding detection result to the processor.
Further preferably, the processor associates and integrates the pre-welding information parameters, the welding process parameters and the post-welding detection results into a data packet and stores the data packet into the database unit.
Further preferably, the processor uses the pre-welding information parameter and the welding process parameter as index tags of the corresponding data packets.
Further preferably, the processor retrieves and calls the data packet in the database unit according to the pre-welding information parameter and the welding process parameter.
A welding defect analysis method based on big data adopts any one of the analysis systems to analyze welding defects, and comprises the following specific steps:
step S1, establishing a database; inputting pre-welding information parameters, welding process parameters and post-welding detection results into a processor through an input unit and a parameter acquisition unit, integrating the three parts of information in the same welding operation into a data packet by the processor, and storing the data packet into a database unit;
step S2, carrying out defect analysis in the welding process; in the welding process, the processor calls the existing data packet in the database unit according to the pre-welding information parameters and the real-time welding process parameters acquired in the welding process, and outputs the existing welding detection result in the data packet through the output unit;
step S3, analyzing the welded defects; after the welding operation is finished, the processor calls the existing data packet in the database unit according to the pre-welding information parameters and the welding process parameters collected in the whole welding process, and outputs the existing welding detection result in the data packet through the output unit.
Preferably, in the step S1, the preliminary database is established by directly inputting the parameters of the pre-welding information, the parameters of the welding process and the results of the post-welding detection, which are reserved in the completed welding operation, into the processor and integrating the parameters into the data packet to be stored in the database unit.
Preferably, in the step S1, the pre-welding information parameters and the welding process parameters collected and recorded in the welding operation are stored as temporary data packets, and after the post-welding detection result of the welding operation is obtained in a later stage, the post-welding detection result is input into the corresponding temporary data packet to form a final data packet, which is stored in the database unit, so as to complete the expansion and establishment of the database.
Preferably, in step S2, the data packets in the database unit are primarily screened and retrieved according to the pre-welding information parameters, and then the primarily screened data packets are selected and compared in real time according to the change of the welding parameters during the welding process, and the comparison result is fed back to the operator in real time through the output unit.
Preferably, in step S3, after the welding operation is completed, firstly, related data packets existing in the database unit are retrieved according to the pre-welding information parameters and the welding process parameters collected in the whole welding process, and all the parameters in the corresponding data packets are output through the output unit, and then the operator pre-determines the welding result according to the post-welding detection result in the corresponding data packet, and performs post-welding detection accordingly.
When the welding defect analysis system based on big data is adopted to carry out auxiliary welding operation, the invention has the following beneficial effects:
in the invention, the pre-welding information parameters, the welding process parameters and the post-welding detection results are integrated by the parameter acquisition unit, the input unit, the processor and the database unit to form a data packet which is stored in the database unit, thereby establishing a welding database containing respective welding conditions. Based on the data packets in the welding database, the corresponding data packets are called according to the real-time welding process parameters in the welding process, and the welding quality at the moment is evaluated in real time by means of the detection results after welding in the data packets, so that the real-time online analysis and judgment of the welding process are realized, the final welding defects are reduced, and the welding quality is improved. Meanwhile, after welding is finished, the data packets are screened again according to the pre-welding information parameters and the welding process parameters, and the welding defect is analyzed and judged according to the detection result after welding in the data packets with the similar parameters, so that operators can perform rapid and accurate detection treatment after welding conveniently, and the overall welding efficiency is improved.
Drawings
FIG. 1 is a block diagram of a big data based weld defect analysis system according to the present invention;
FIG. 2 is a schematic flow chart of weld defect analysis using the method of the present invention.
Detailed Description
The technical scheme of the invention is further described below by combining the attached drawings.
Referring to fig. 1, the welding defect analysis system based on big data of the present invention includes a parameter acquisition unit 1, an input unit 2, a processor 3, a database unit 4, and an output unit 5.
The parameter acquisition unit 1 is connected with the processor 3, and is composed of a plurality of different sensors, such as a voltage sensor, a current sensor, an ambient temperature sensor, an ambient humidity sensor, an interlayer temperature sensor, a welding speed sensor, a wire feeding speed sensor, and the like, and is connected with the welding equipment and the welding piece to realize acquisition of different welding process parameters on the welding equipment and the welding piece in the welding process and transmit the parameters to the processor 3. In addition, the parameter acquisition unit 1 is also provided with a position positioning module and a high-speed camera for accurately positioning the welding position and recording the welding image, for example, when the pipe fitting is butt-welded, the welding angle position can be accurately positioned.
The input unit 2 is connected to the processor 3 for inputting relevant welding information into the processor 3. For example, the pre-welding information parameters such as the base material, the base material thickness, the welding material, the weld groove form, the groove angle, and the welding method of the workpiece are recorded and temporarily stored as temporary data, and the post-welding detection result after welding is input to the processor 3.
The database unit 4 is connected to the processor 3 and is used for storing the information integrated by the processor 3 and outputting the related information to the processor 3. The information integrated and output by the processor 3 includes pre-welding information parameters, welding process parameters and post-welding detection information.
The output unit 5 is connected to the processor 3 and is configured to output the related information processed by the processor 3. For example, the data stored in the database unit 4 is directly output or the data stored in the database unit 4 is output as a result of comparison with the welding process parameters collected by the welding process.
The processor 3 is used for receiving the respective data obtained by the parameter acquisition unit 1 and the input unit 2, integrating the data and then transmitting the data to the database unit 4 for storage. Meanwhile, the processor 3 is also used for calling the stored data in the database unit 4, comparing and analyzing the data with the data obtained in the parameter acquisition unit 1, and finally outputting the result through the output unit 5.
The output unit 5 may be a display unit, such as a display, for directly presenting relevant data parameters to an operator for reference to assist the operator in performing subsequent processing operations, or an alarm unit, such as an alarm lamp or an alarm, for directly reminding the operator of attention, and performing auxiliary control on welding operations to suspend operations of the automatic welding equipment.
Preferably, the input unit 2 is mainly used for inputting the pre-welding information parameters and the post-welding detection results into the processor 3, so that the pre-welding information parameters and the post-welding detection results can be integrated with the welding process parameters acquired by the parameter acquisition unit 1 in the welding process to form a complete data packet including the pre-welding information, the in-welding information and the post-welding information in the whole welding operation process. The input unit 2 can adopt a scanning input mode, so that the information parameters before welding can be made into scanning codes, such as bar codes or two-dimensional codes, the data input speed is improved, and various icon data detected after welding can be quickly scanned and recorded, so that the data input efficiency and feasibility are improved.
Preferably, when the processor 3 calls the data packets stored in the database unit 4, the pre-welding information parameters and the welding process parameters in each data packet can be used as standards for screening and calling, so that the post-welding detection result under the corresponding welding condition can be quickly obtained, and an operator is assisted to quickly judge the risk of the welding quality.
In the process of establishing the data packet by the processor 3, the pre-welding information parameters and the welding process parameters can be set as the index tags of the corresponding data packet in advance, and each pre-welding information parameter and each welding process parameter are set as an independent index tag, so that each pre-welding information parameter and each welding process parameter can be used as an independent index target to screen and call the data packet, and the calling accuracy and the processing speed of the processor 3 on the data packet in the database unit 4 are improved.
In addition, although only one parameter acquisition unit 1 and one output unit 5 are provided in the module diagram shown in fig. 1, a plurality of parallel parameter acquisition units 1 and a plurality of output units 5 may be provided at the same time, so as to perform synchronous analysis processing on a plurality of welding operations. Therefore, the processor 3 can be placed at a certain fixed position and is far away from a construction site, and then data transmission between the parameter acquisition unit 1 and the output unit 5 and the processor 3 is carried out by means of wired or wireless transmission equipment, so that the processor 3 with stronger structure, function and data processing capacity can be used for carrying out synchronous processing on multiple groups of data, and further the processing efficiency of the data is improved.
With reference to fig. 1 and 2, the analysis process of the welding defect by using the big data based welding defect analysis system of the present invention is as follows:
step S1, a database is established. The pre-welding information parameters, the welding process parameters and the post-welding detection results are input into the processor 3 through the input unit 2 and the parameter acquisition unit 1, the processor 3 integrates the three parts of information in the same welding operation into one data packet and stores the data packet into the database unit 4, and thus a database consisting of data packets comprising different parameters is formed.
Preferably, in step S1, the pre-welding information parameters, the welding process parameters and the post-welding detection results that remain after the welding operation has been completed may be directly input into the processor 3 through the input unit 2 and integrated into a data packet stored in the database unit 4 for initial establishment of the database. Meanwhile, the pre-welding information parameters and the welding process parameters collected and recorded in the welding operation process can be input into the processor 3 as temporary data packets, and then after the post-welding detection result of the welding operation is obtained in the later period, the post-welding detection result is input into the processor 3 through the input unit 2 to be integrated with the corresponding temporary data packet, so that a new data packet is formed and stored into the database unit, and the expansion and establishment of the database are realized. Therefore, the database can be gradually enlarged along with the implementation of a large number of welding operations, more welding defect analysis data can be obtained, and the analysis accuracy is improved.
In step S2, a defect analysis of the welding process is performed. In the welding process, the processor 3 calls the existing data packet in the database unit 4 according to the pre-welding information parameters and the real-time welding process parameters acquired in the welding process, and outputs the corresponding welding detection result in the data packet through the output unit 5.
Preferably, the specific process for analyzing the defects in the welding process is as follows:
firstly, the existing complete data packets in the database unit 4 are preliminarily screened according to the pre-welding information parameters, and the data packets which have a certain matching degree with the pre-welding information parameters of the current welding operation are taken out. The matching degree can be adjusted according to specific conditions, for example, 95%, 90% or 85% of the matching degree can be adjusted, different parameters in the information parameters before welding can be screened and sorted to determine priorities, so that the information parameters before welding can be sequentially screened according to a certain sequence, for example, the base material thickness, the welding material, the welding seam groove form and the groove angle sequence are subjected to priority sorting, and other sequence sorting and other parameters can be selected as the basis for primary screening of the data packets.
And then, according to the change of welding parameters in the welding process, selecting the preliminarily screened data packets in real time, extracting the post-welding detection results in the corresponding data packets, and analyzing and comparing the post-welding detection results. For example, the primarily screened data packets are secondarily screened according to the welding current value, the welding voltage value or the welding position in the welding process parameters, and the post-welding detection results in the corresponding data packets are extracted. When the welding process parameters are not too many, all the welding process parameters can be used as selection basis for secondary screening of the data packets, if the welding process parameters are more, priority ranking and matching degree setting can be carried out on different welding process parameters, secondary screening of the data packets is carried out according to the priority and the matching degree, and therefore the data packets with the most appropriate matching degree are obtained, and the most reliable post-welding detection result is obtained.
And finally, the comparison result is fed back to an operator in real time through the output unit 5, so that the operator is assisted to adjust the welding parameters in the welding process in real time, and the final welding effect is ensured. In the process, if the analysis result of the processor 3 is ideal, for example, the probability of generating welding defects in the welding operation at this time is 0, that is, all the post-welding detection results displayed by the secondarily screened data packets are free of welding defects, the output unit 5 may prompt the operator to continue the welding operation, otherwise, if the analysis result of the processor 3 is not ideal, for example, the probability of generating welding defects in the welding operation at this time is 50%, that is, the post-welding detection results displayed by the secondarily screened data packets include a large number of welding defects, which may be the same or different, the output unit 5 may prompt the operator to pay attention and the operator determines whether to suspend the welding for the temporary welding detection, and further, if the analysis result shows that the probability of generating welding defects at this time is as high as 90%, the processor 3 can now perform auxiliary control on the welding equipment to directly suspend the continued welding operation of the welding equipment, thereby avoiding the occurrence of defects due to the continued welding.
In step S3, defect analysis after soldering is performed. After the welding operation is completed, the processor 3 calls the existing data packet in the database unit 4 according to the pre-welding information parameters and the welding process parameters acquired in the whole welding process, and outputs the existing welding detection result in the data packet through the output unit 5, so as to assist the operator in judging how to perform the post-welding detection on the welding seam obtained in the welding process.
Preferably, the specific process for analyzing the defects after welding is as follows:
firstly, the processor 3 calls the existing related data packet in the database unit 4 according to the pre-welding information parameters and the welding process parameters collected in the whole welding process, and outputs all the parameters in the corresponding data packet through the output unit 5 to be displayed to the operator. When the data packets are screened, the pre-welding information parameters and the welding process parameters need to be comprehensively considered and screened to select the data packet which is most matched with the current welding operation, so that the most accurate defect analysis and judgment on the current welding operation are ensured.
Then, the operator can analyze and judge the defects possibly generated by the current welding according to the data in all the data packets presented by the output unit 5, so as to facilitate the subsequent accurate post-welding detection. For example, if the processor 3 selects five data packets with the highest matching degree according to the matching priorities of different parameters in the pre-welding information parameters and the welding process parameters, the output unit 5 directly develops and presents all the parameters in the five data packets, the operator approves and compares the parameters of the five data packets with the parameters of the current welding, and performs initial judgment on the welding result according to different post-welding detection results in different data packets, and does not need post-welding detection? What type of weld defect detection is performed? What location to do defect detection? Therefore, prejudgment of detection operation after welding is realized, and the efficiency and the precision of treatment after welding are improved.
Claims (10)
1. A welding defect analysis system based on big data is characterized by comprising a parameter acquisition unit, an input unit, a processor, a database unit and an output unit; wherein,
the parameter acquisition unit is connected with the processor, consists of a plurality of different sensors and is used for acquiring welding process parameters on welding equipment and welding pieces in the welding process and transmitting the welding process parameters to the processor;
the input unit is connected with the processor and is used for inputting relevant welding information to the processor;
the database unit is connected with the processor and used for storing the information integrated by the processor and outputting the related information to the processor;
the output unit is connected with the processor and used for outputting the relevant information processed by the processor;
the processor is used for receiving the data in the parameter acquisition unit and the input unit, integrating and outputting the data to the database unit for storage, calling the data in the database unit, comparing and analyzing the data with the data in the parameter acquisition unit and the input unit, and outputting the result through the output unit.
2. The analysis system of claim 1, wherein the input unit is configured to input pre-weld information parameters and post-weld detection results to the processor.
3. The analytical system of claim 2, wherein the processor associates and integrates the pre-weld information parameters, the welding process parameters, and the post-weld inspection results into a data package and stores the data package to the database unit.
4. The analysis system of claim 3, wherein the processor identifies the pre-weld information parameter and the welding process parameter as index tags for the corresponding data packets.
5. The analysis system of claim 3, wherein the processor retrieves and recalls the data packets from the database unit based on the pre-weld information parameters and the welding process parameters.
6. A welding defect analysis method based on big data is characterized in that the analysis system of any one of claims 1 to 5 is adopted to carry out welding defect analysis, and the method comprises the following specific steps:
step S1, establishing a database; inputting pre-welding information parameters, welding process parameters and post-welding detection results into a processor through an input unit and a parameter acquisition unit, integrating the three parts of information in the same welding operation into a data packet by the processor, and storing the data packet into a database unit;
step S2, carrying out defect analysis in the welding process; in the welding process, the processor calls the existing data packet in the database unit according to the pre-welding information parameters and the real-time welding process parameters acquired in the welding process, and outputs the existing welding detection result in the data packet through the output unit;
step S3, analyzing the welded defects; after the welding operation is finished, the processor calls the existing data packet in the database unit according to the pre-welding information parameters and the welding process parameters collected in the whole welding process, and outputs the existing welding detection result in the data packet through the output unit.
7. The analysis method according to claim 6, wherein in the step S1, the primary database is established by directly inputting the pre-welding information parameters, the welding process parameters and the post-welding detection results which are reserved in the completed welding operation into the processor and integrating the pre-welding information parameters, the welding process parameters and the post-welding detection results into a data packet to be stored in the database unit.
8. The analysis method according to claim 6, wherein in step S1, the expansion establishment of the database is completed by storing the pre-welding information parameters and the welding process parameters collected and recorded in the welding operation as temporary data packets, and inputting the post-welding detection results into the corresponding temporary data packets to form the final data packets to be stored in the database unit after the post-welding detection results of the welding operation are obtained at a later stage.
9. The analysis method according to claim 6, wherein in step S2, the data packets in the database unit are primarily screened and retrieved according to the pre-welding information parameters, and then the primarily screened data packets are selected and compared in real time according to the change of the welding parameters during the welding process, and the comparison result is fed back to the operator in real time through the output unit.
10. The analysis method according to claim 6, wherein in step S3, after the welding operation is completed, firstly, according to the pre-welding information parameters and the welding process parameters collected in the whole welding process, the related data packet existing in the database unit is retrieved and all the parameters in the corresponding data packet are output through the output unit, and then the operator pre-judges the welding result according to the post-welding detection result in the corresponding data packet, and performs the post-welding detection accordingly.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111069819A (en) * | 2019-11-27 | 2020-04-28 | 广州明珞汽车装备有限公司 | Welding quality prediction system and method based on artificial intelligence |
| CN111716052A (en) * | 2020-06-19 | 2020-09-29 | 渤海造船厂集团有限公司 | Welding-following rapid detection method for internal defects in welding process |
| CN115194366A (en) * | 2022-05-19 | 2022-10-18 | 广州东焊智能装备有限公司 | An intelligent welding equipment based on big data |
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| CN101364106A (en) * | 2008-09-19 | 2009-02-11 | 广州（从化）亨龙机电制造实业有限公司 | Welding quality control system and method for resistance welding |
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| CN111069819A (en) * | 2019-11-27 | 2020-04-28 | 广州明珞汽车装备有限公司 | Welding quality prediction system and method based on artificial intelligence |
| CN111716052A (en) * | 2020-06-19 | 2020-09-29 | 渤海造船厂集团有限公司 | Welding-following rapid detection method for internal defects in welding process |
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Application publication date: 20190308 |