CN112015149A - Lightweight vehicle body connection quality auxiliary judgment method - Google Patents
Lightweight vehicle body connection quality auxiliary judgment method Download PDFInfo
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- CN112015149A CN112015149A CN202010737257.0A CN202010737257A CN112015149A CN 112015149 A CN112015149 A CN 112015149A CN 202010737257 A CN202010737257 A CN 202010737257A CN 112015149 A CN112015149 A CN 112015149A
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Abstract
The invention discloses a lightweight vehicle body connection quality auxiliary judgment method, which comprises the following steps: s1, setting a quality monitoring server; s2, the quality monitoring server collects the process data and the equipment state data of each point position of the SPR equipment or the FDS equipment in real time in the working process; s3, the quality monitoring server executes a corresponding SPR quality defect judgment program or an FDS quality defect judgment program, carries out quality defect judgment on the collected process data and equipment state data corresponding to each point location, and gives the type and reason of the quality defect of the point location if the quality defect is judged to exist; and S4, the quality monitoring server executes a quality early warning judgment program to judge whether the point position statistical condition with the quality defect meets the quality early warning condition, and if so, the server performs early warning and stops the corresponding equipment from producing. The invention can carry out full detection on the connection points in real time, avoids the condition that the quality defect points are missed to be detected, and does not generate a scrapped vehicle due to quality detection.
Description
Technical Field
The invention relates to the technical field of vehicle body connection processes, in particular to a lightweight vehicle body connection quality auxiliary judgment method.
Background
Compared with the traditional welding process, when quality defects occur in the production process of a novel connecting process, namely a self-piercing riveting process SPR (surface plasma resonance) (hereinafter abbreviated as SPR) and a flow drilling screwing process FDS (hereinafter abbreviated as FDS), the failure condition is complex, the root cause is difficult to find, a large amount of time and manpower are consumed for troubleshooting, the utilization rate of equipment is low, and the capacity target cannot be achieved.
The common defects of SPR comprise edge cracking, unqualified residual plate thickness, unqualified interlocking value, unqualified rivet head height and the like, the common defects of FDS comprise screw sliding teeth and screw non-seating, and the following two methods are generally adopted for checking the connection quality of SPR and FDS at present:
1. and (3) appearance inspection: the joints are inspected visually and by means of instruments without damaging the joints, for example, whether they are cracked or not, and the height of the rivet head is measured.
2. And (3) metallographic examination: this is a destructive examination and requires accurate measurement by electron microscopy of whether the data relating to the joint meets standards.
The two inspection modes can only adopt a sampling inspection mode in batch automatic production, can not realize full inspection, and are easy to cause quality defect points to miss inspection. Moreover, appearance inspection can only inspect appearance defects; the metallographic examination process is complicated, and a scrapping car can be generated after examination, so that the time and the labor are consumed, and the efficiency is low.
In order to prevent the body-in-white having failed connection points from continuing to flow into the next process, a more accurate real-time on-line quality determination method must be used.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a light vehicle body connection quality auxiliary judgment method to avoid quality defect point missing detection and generation of scrapped vehicles.
Therefore, the invention adopts the following technical scheme:
a lightweight vehicle body connection quality auxiliary judgment method comprises the following steps: s1, setting a quality monitoring server; s2, the quality monitoring server collects process data and equipment state data of each point position of the SPR equipment or the FDS equipment in real time in the working process; s3, the quality monitoring server executes a corresponding SPR quality defect judgment program or an FDS quality defect judgment program, carries out quality defect judgment on the collected process data and equipment state data corresponding to each point location, and gives the type of the quality defect of the point location and the reason causing the quality defect if the quality defect is judged to exist; and S4, the quality monitoring server executes a quality early warning judgment program, judges whether the point position statistical condition with the quality defect meets the quality early warning condition, and if so, performs early warning and stops the production of corresponding equipment.
Further, in step S3, when it is determined that the current point location has a quality defect in real time, the quality monitoring server records information of the current point location, including a vehicle body number, a point location number, a point time, point process data, a defect type, and a defect reason, and stores the information into a database of the quality monitoring server for subsequent review and rework.
Further, the quality warning determination program, when executed, implements the steps of: when the number of the counted samples is less than or equal to X, early warning is carried out at the Y-th defect point; when the number of the counted samples is equal to X and the number of the defect points is less than Y, no early warning is generated, the next counting period is started, and the counted points are not repeatedly counted; after the early warning is generated, when the production is recovered, when the Y + n point position is a defect point, the early warning is continuously sent out, when the Y + n point position is a normal point, the early warning is started to remove statistics, then the normal early warning of all the continuous Z point positions can be removed, otherwise, the early warning is continuously sent out, after the early warning is removed, the next statistical period is started, wherein the number of early warning statistical samples is set to be X, the number of defect points is Y, the number of the continuous point positions removed by the early warning is set to be Z, and n is a natural number.
Further, in step S4, when the early warning occurs, the quality monitoring server further pushes the reason why each quality defect point generates the quality defect through a system interface to assist in manual troubleshooting.
Further, the quality monitoring server has stored therein a failure-assisted determination program module which, when executed, implements the steps of: and continuously classifying and counting the quality defect reasons in the samples with the same quality defect at the background, sequencing the quality defect reasons in sequence from more to less according to the quantity counted by the quality defect reason types, and pushing the quality defect reasons on a system interface according to the sequence of sequencing the quality defect reasons for the same quality defect which occurs again during quality early warning.
Further, the quality monitoring server performs early warning in a manner of sending a quality early warning list to a system interface, wherein the quality early warning list includes quality defect point location information, quality defect type information, and quality defect reason information.
Further, the quality monitoring server stores an SPR quality defect judgment program, an FDS quality defect judgment program and a quality early warning judgment program.
Further, the SPR quality defect judging program comprises an SPR quality defect judging sub-algorithm and an SPR reason classifying sub-algorithm, and the SPR quality defect judging program realizes the following steps when being executed: s31, inputting an algorithm standard value of each point position on the vehicle body in the quality monitoring server in advance; s32, when the SPR equipment performs dotting, the quality monitoring server acquires the current dotting point number and the process data generated by the SPR equipment in the dotting process in real time; s33, according to the point location number obtained in the step S32, the quality monitoring server matches the algorithm standard value corresponding to the point location, the SPR quality defect judgment sub-algorithm corresponding to the point location and the SPR reason classification sub-algorithm corresponding to the point location, which are input in advance in the step S31; s34, using the process data corresponding to the point location acquired in the step S32 in an SPR quality defect judgment sub-algorithm corresponding to the point location matched in the step S33 to judge the quality defect so as to judge whether the point location has the quality defect; and S35, applying the process data corresponding to the point acquired in the step S32 to the SPR reason classification sub-algorithm corresponding to the point matched in the step S33 for identifying and classifying the quality defect types and the defect reasons so as to obtain and give classification results of the quality defect types and the defect reasons, wherein the step is executed only when the point is judged to have the quality defect in the step S34.
Further, the FDS quality defect determination program includes an FDS quality defect determination sub-algorithm and an FDS cause classification sub-algorithm, and when executed, the FDS quality defect determination program implements the steps of: s31', inputting an algorithm standard value of each point position on the vehicle body in the quality monitoring server in advance; s32', when the FDS equipment performs dotting, the quality monitoring server acquires the current dotting point number and the process data generated by the FDS equipment in the dotting process in real time; s33 ', according to the point location number obtained in the step S32 ', the quality monitoring server matches the algorithm standard value corresponding to the point location, the FDS quality defect judgment sub-algorithm corresponding to the point location and the FDS reason classification sub-algorithm corresponding to the point location which are input in advance in the step S31 '; s34 ', the process data corresponding to the point acquired in the step S32 ' is used for quality defect judgment in the FDS quality defect judgment sub-algorithm corresponding to the point matched in the step S33 ', so as to judge whether the point has quality defects; and S35 ', using the process data corresponding to the point acquired in the step S32' in the FDS reason classification sub-algorithm corresponding to the point matched in the step S33 'to identify and classify the quality defect types and the defect reasons so as to obtain and give classification results of the quality defect types and the defect reasons, wherein the step is executed only when the point is judged to have the quality defect in the step S34'.
The invention has the following technical effects:
(1) the invention collects the process data and the equipment state data in the working process of the SPR or FDS equipment in real time, realizes the judgment of non-destructive quality defects and reasons through a corresponding quality defect judgment program, can carry out full inspection on the connection points in production, can avoid the condition of missed inspection of the quality defect points, prevent the white car body with invalid connection points from flowing into the next procedure, can not generate a scrapping car due to quality inspection, saves the cost, can judge whether the point position statistical condition with the quality defects meets the condition of quality early warning through the quality early warning judgment program, and can carry out early warning and stop the production of the equipment if the point position statistical condition with the quality defects meets the condition of the quality early warning so as to remind staff of carrying out fault troubleshooting, thereby avoiding producing more quality defect points.
(2) The invention can also realize the classification and statistics of the quality defect reasons in the samples with the same quality defect continuously at the background through the fault auxiliary judgment program module, and sequentially sort the quality defect reasons according to the quantity counted by the quality defect reason types from more to less, wherein the more front the sorting is, the higher the possibility of causing the quality defect is, the same quality defect which occurs again when early warning is carried out, and the reason generated by each quality defect can be sequentially pushed according to the possibility of the quality defect generation reasons from more to less, thereby assisting the manual troubleshooting, saving the time for manually troubleshooting and improving the troubleshooting efficiency.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is an architectural diagram of the present invention;
fig. 2 shows a state of a process when a body is connected by the FDS process;
FIG. 3 illustrates the state of the process when attaching a vehicle body using the SPR process; and
fig. 4 is a flow chart of quality defect determination for FDS point location sliding tooth defect determination.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
When the vehicle body is connected through the self-piercing riveting process SPR or the flow drilling screwing process FDS, the auxiliary judgment method for the connection quality of the lightweight vehicle body can be adopted to carry out real-time online quality monitoring.
The invention is based on a distributed architecture to collect and process data and equipment state data generated in SPR and FDS process production. The invention relates to an intelligent monitoring method based on a big data platform, which is mainly used for data acquisition and analysis processing of SPR equipment and FDS equipment, so that the quality state can be judged on line in real time, and production management is assisted.
The invention discloses a lightweight vehicle body connection quality auxiliary judgment method, which comprises the following steps:
and S1, setting a quality monitoring server.
And S2, the quality monitoring server collects the process data and the equipment state data of each point in the working process of the SPR equipment or the FDS equipment in real time.
The quality monitoring server acquires data in real time through the data acquisition module.
Specifically, the SPR device and the FDS device each have a device database, and when the SPR device or the FDS device operates, the corresponding device database has real-time process data and device status data, and as shown in fig. 1, the quality monitoring server collects the real-time process data and the device status data in the database of the corresponding device through the data collection module.
The data acquisition module arranges the acquired process data and the acquired equipment state data according to a specific rule, sends the process data and the equipment state data to the quality monitoring server through a RabbitMQ advanced message queue protocol, and stores the process data and the equipment state data in a database of the quality monitoring server.
The specific regular arrangement refers to arranging the acquired data according to time/station number/equipment number/point number/process data (process characteristic values required to be acquired in quality judgment), and then sending the data to the quality monitoring server.
And S3, the quality monitoring server executes a corresponding SPR quality defect judgment program or an FDS quality defect judgment program, calculates the collected process data and equipment state data corresponding to each point location in real time to judge whether the corresponding point location has a quality defect or not in real time, and if the point location has the quality defect, gives the type of the quality defect of the point location and the reason for causing the quality defect.
If a point is judged to have a quality defect, the type of the quality defect of the point is also given as the FDS sliding tooth defect, and the reason of the quality defect is plate strength fluctuation.
Due to the complex quality causes, one quality defect may correspond to a different cause. Therefore, the quality monitoring server may give various reasons when determining the cause of the quality defect.
When the current point location is judged to have the quality defect in real time, the quality monitoring server records the information of the current point location, including the vehicle body number, the point location number, the dotting time, the dotting process data, the defect type and the defect reason, and stores the information into a database of the quality monitoring server. Different connecting point positions on the vehicle body correspond to respective point positions, and different vehicle bodies also correspond to respective vehicle body numbers.
The recording of the information enables follow-up manual work to check the quality defect points and conduct quick, effective and accurate recheck according to the information, and rework is conducted, so that the condition of missed detection can be avoided, and the body-in-white with failure connection points can be prevented from flowing into the next process.
And S4, the quality monitoring server executes a quality early warning judgment program to judge whether the point position statistical condition with the quality defect meets the quality early warning condition, and if so, the server performs early warning and stops the production of corresponding equipment so as to avoid producing more quality defect points.
Further, the quality monitoring server stores an SPR quality defect judgment program, an FDS quality defect judgment program and a quality early warning judgment program.
The quality early warning determination program, when executed, implements the steps of:
when the number of the counted samples is less than or equal to X, early warning is carried out at the Y-th defect point; when the number of the counted samples is equal to X and the number of the defect points is less than Y, no early warning is generated, the next counting period is started, and the counted points are not repeatedly counted; after the early warning is generated and when the production is recovered, when the Y + n point position is a defect point, the early warning is continuously sent out, when the Y + n point position is a normal point, the early warning is started to remove statistics, then the normal early warning can be removed for Z continuous point positions, otherwise, the early warning is continuously sent out, after the early warning is removed, the next statistical period is started, wherein the number of early warning statistical samples is set to be X, the number of defect points is Y, the number of the continuous point positions removed by the early warning is Z, and n is a natural number of 1, 2, 3 and ….
After the early warning occurs, manual intervention is performed, troubleshooting is performed, when production is recovered after the fault is repaired, the point location of the connection point of the first production and processing is the Y +1 th point location, the subsequent point locations are the Y +2 th point location, the Y +3 th point location and the … th Y + n th point location in sequence, when the Y + n th point location is a defect point, the early warning is continuously sent out, when the Y + n th point location is a normal point, statistics is removed by entering the early warning, then the normal early warning can be removed by Z continuous point locations, otherwise, the early warning is continuously sent out, and after the early warning is removed, the next statistical period is entered.
This is further detailed below in connection with a specific embodiment.
In the production process, the equipment continuously performs dotting according to set point positions and parameters, the total number of samples is set to be 10, the number of defect points is set to be 5, and the number of continuous point positions for early warning removal is set to be 5.
Whether the early warning is generated is divided into the following two conditions:
1) when the dotting quantity of the equipment reaches 10, accumulating the number of the points with the defects less than 5, not generating early warning, and counting the dotting from zero to enter the next round of statistics;
2) when the quantity of dotting is not more than 10 to equipment, the accumulative total defect number of points that takes place is 5, then produces the early warning after the 5 th defect point position takes place, and the early warning can indicate the manual work to intervene, does the technology adjustment, and the early warning can continuously send out always until satisfying the early warning and removing the condition.
The condition of early warning removal is as follows: when the production is recovered, statistics is started from the 1 st point after the early warning is generated, if no defect occurs in 5 continuous points, the early warning is released, and then statistics is performed from zero to determine whether the next early warning is generated or not.
In the step S4, when the early warning occurs, the quality monitoring server further pushes the reason why each quality defect point generates the quality defect through a system interface, so as to assist in manual troubleshooting.
Specifically, the quality monitoring server further stores a failure auxiliary determination program module, and the failure auxiliary determination program module implements the following steps when executed:
the quality defect reasons of the samples with the same quality defect are continuously classified and counted in the background, the quality defect reasons are sequentially sorted from more to less according to the number counted by the quality defect reason types, the reasons of the quality defect are pushed on a system interface according to the sequence arranged in the background when the quality is early warned, and therefore an operator is assisted in troubleshooting and production is recovered. Wherein the more advanced the quality defect cause push sequence, the greater the likelihood of causing such a quality defect.
The fault-assisted determination program module may also be configured to, when executed, manually identify a cause of a quality defect and then record the cause of the quality defect.
With the progress of production, the auxiliary fault judgment program module continuously counts the types and the ranks of the quality defect reasons in samples with the same quality defect in the background, which may change (after the reason of quality defect generation is manually determined once, the reason of quality defect is recorded).
The quality monitoring server is preset with quality defect reasons causing various quality defects, and the quality defect reasons are sequentially ranked from large to small according to the possibility, which is preset according to the experience in actual production, so that the reasons of the quality defects can be sequentially pushed when the lightweight vehicle body connection quality auxiliary judgment method is applied for the first time, and manual troubleshooting is assisted.
The SPR quality defect judging program or the FDS quality defect judging program in the quality monitoring server can judge a plurality of possible reasons causing the quality defects, and the fault auxiliary judging program module can sequentially push the reasons of the quality defects from large to small according to the possibility so as to be referred by personnel.
And pushing the early warning generation form to a system interface in a mode of sending a quality early warning list. The quality early warning list is provided with quality defect point position information, quality defect type information and quality defect reason information.
The point location information and the quality defect type information of all quality defect points in the duration from the generation of the current quality early warning to the release state and the quality defect reason information pushed in sequence can be checked through the quality early warning list. Only when the quality early warning judgment program judges that the early warning can be removed, the quality early warning list can be manually removed, and then the production can be recovered.
The SPR quality defect judging program comprises an SPR quality defect judging sub-algorithm and an SPR reason classifying sub-algorithm, and when being executed, the SPR quality defect judging program realizes the following steps:
and S31, inputting the algorithm standard value of each point on the vehicle body in the quality monitoring server in advance.
Because different point locations are distributed at different positions on the vehicle body, the adopted plates and the lap joint mode are different, so that the algorithm standard values corresponding to the point locations are different, the algorithm standard values of the point locations are confirmed through a large amount of tests in the early stage, and the different point locations are distinguished by point location numbers.
S32, when the SPR equipment performs dotting, the quality monitoring server acquires the current dotting point number and the process data generated by the SPR equipment in the dotting process in real time.
The process data comprises a speed value, a pressure value, a torque value and the like of a specific stage in the dotting process, and also comprises characteristic values of curves of the speed, the pressure, the torque and the like in the continuous dotting process, wherein the characteristic values comprise a slope, an extreme value and the like.
And S33, matching the algorithm standard value corresponding to the point, the SPR quality defect judgment sub-algorithm corresponding to the point and the SPR reason classification sub-algorithm corresponding to the point, which are input in advance in the step S31, by the quality monitoring server according to the point number acquired in the step S32.
And S34, applying the process data corresponding to the point acquired in the step S32 to the SPR quality defect judgment sub-algorithm corresponding to the point matched in the step S33 for quality defect judgment so as to judge whether the point has quality defects.
The SPR quality defect judgment sub-algorithm utilizes big data to fit key attribute characteristic values of the quality-qualified point positions into an equation, the acquired process data is calculated through the equation, a real-time test calculation value is further acquired, the acquired real-time test calculation value is compared with a matched algorithm standard value, if the acquired real-time test calculation value is compared with the algorithm standard value within an allowable error range, the judgment is made to be qualified, no quality defect exists, otherwise, the judgment is made to be unqualified, and the quality defect exists.
And S35, applying the process data corresponding to the point acquired in the step S32 to the SPR reason classification sub-algorithm corresponding to the point matched in the step S33 for identifying and classifying the quality defect types and the defect reasons so as to obtain and give classification results of the quality defect types and the defect reasons, wherein the step is executed only when the point is judged to have the quality defect in the step S34.
The SPR reason classification sub-algorithm is based on experience, selects continuous change characteristics of different process attribute data for classification to obtain a reason classification model, and classifies and identifies the defect types and defect reasons of the judgment point positions by using the reason classification model.
The SPR reason classification sub-algorithm is used for further data analysis on the basis that the quality defects are judged to exist, and the quality defects caused by different reasons are different in characteristics shown in process data.
The FDS quality defect judgment program comprises an FDS quality defect judgment sub-algorithm and an FDS reason classification sub-algorithm, and when being executed, the FDS quality defect judgment program realizes the following steps:
and S31', inputting an algorithm standard value of each point position on the vehicle body in the quality monitoring server in advance.
Because different point locations are distributed at different positions on the vehicle body, the adopted plates and the lap joint mode are different, so that the algorithm standard values corresponding to the point locations are different, the algorithm standard values of the point locations are confirmed through a large amount of tests in the early stage, and the different point locations are distinguished by point location numbers.
S32', when the FDS equipment performs dotting, the quality monitoring server acquires the current dotting point number and the process data generated by the FDS equipment in the dotting process in real time.
The process data comprises a speed value, a pressure value, a torque value and the like of a specific stage in the dotting process, and also comprises characteristic values of curves of the speed, the pressure, the torque and the like in the continuous dotting process, wherein the characteristic values comprise a slope, an extreme value and the like.
And S33 ', according to the point location number obtained in the step S32 ', the quality monitoring server matches the algorithm standard value corresponding to the point location, the FDS quality defect judgment sub-algorithm corresponding to the point location and the FDS reason classification sub-algorithm corresponding to the point location, which are input in advance in the step S31 '.
And S34 ', using the process data corresponding to the point acquired in the step S32 ' in the FDS quality defect judgment sub-algorithm corresponding to the point matched in the step S33 ' to judge the quality defect so as to judge whether the point has the quality defect.
The FDS quality defect judgment sub-algorithm utilizes big data to fit key attribute characteristic values of the quality-qualified point positions into an equation, the acquired process data is calculated through the equation, a real-time test calculation value is further acquired, the acquired real-time test calculation value is compared with a matched algorithm standard value, if the acquired real-time test calculation value is compared with the algorithm standard value within an allowable error range, the FDS quality defect judgment sub-algorithm is judged to be qualified, no quality defect exists, and otherwise, the FDS quality defect judgment sub-algorithm is judged to be unqualified, and the FDS quality defect exists.
And S35 ', using the process data corresponding to the point acquired in the step S32' in the FDS reason classification sub-algorithm corresponding to the point matched in the step S33 'to identify and classify the quality defect types and the defect reasons so as to obtain and give classification results of the quality defect types and the defect reasons, wherein the step is executed only when the point is judged to have the quality defect in the step S34'.
The FDS reason classification sub-algorithm is based on experience, continuous change characteristics of different process attribute data are selected for classification, a reason classification model is obtained, and the reason classification model is used for classifying and identifying the defect types and defect reasons of the judgment point positions.
The FDS reason classification sub-algorithm is used for further data analysis on the basis that the quality defects are judged to exist, and the characteristics of the quality defects caused by different reasons and expressed in process data are different.
The program logic of the SPR quality defect determination program and the FDS quality defect determination program is the same, and the execution is discriminated by a device number.
The specific stages mentioned above in the context of the quality monitoring server in acquiring process data refer to the critical stages in the SPR and FDS process flows.
Referring collectively to fig. 2, the key stages in the FDS process flow are shown, including 1, pre-drilling 2, drilling 3, piercing 4, self-tapping 5, tightening 6, seating (tightening to set torque).
Referring collectively to FIG. 3, key stages in the SPR process flow are shown, including A, rivet piercing into sheet B, rivet piercing yielding C, rivet piercing into the lower sheet, and staple legs splaying to form an interlock.
The present invention will be described in further detail with reference to a specific example of point location quality defect determination.
The FDS point location tooth slipping defect is judged as an example:
referring to fig. 4, when the FDS device performs dotting connection on a point location on a vehicle body, the quality monitoring server obtains process data of tightening torques at a tightening stage and a seating stage of the point location from the FDS device database, and matches the quality monitoring server according to the point location number to obtain a pre-input algorithm standard value of the tightening torque corresponding to the point location, and then calculates the obtained process data of the tightening torques by using a corresponding equation in the FDS quality defect judgment sub-algorithm to obtain a final real-time test calculation value of the tightening torques, and then compares the obtained real-time test calculation value with the matched algorithm standard value;
if the real-time test calculated value is larger than or equal to the algorithm standard value, judging that the point is qualified, judging that the tooth sliding defect does not exist, and executing FDS point position tooth sliding defect judgment of the next point position;
and if the real-time test calculated value is less than the algorithm standard value, judging that the test is unqualified and the sliding tooth defect exists, identifying and classifying the defect reason through an FDS reason classifying sub-algorithm, and executing the FDS point position sliding tooth defect judgment of the next point position after the classification result of the defect reason is obtained.
According to the invention, the staff is reminded of having more quality defect points in the current production state by giving out early warning and stopping production, so that production problems occur, manual intervention is carried out, and the reasons of the quality defects are pushed in sequence according to the possibility when the early warning occurs, so that manual troubleshooting is assisted, and the troubleshooting efficiency can be improved. And the continuous production of quality defect points can be avoided by stopping production during early warning, and the repair rate and the rejection rate are reduced.
The invention can not only judge the quality of the connection point in a full inspection mode, but also early warn to remind staff of production problems after the point position statistical condition of the quality defect point meets the condition of quality early warning, and can push the reason of the quality defect to help manual troubleshooting while early warning, thereby realizing good auxiliary production management.
In addition, the invention can realize real-time quality judgment of the connection points during production and has the characteristic of high quality inspection efficiency.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A lightweight vehicle body connection quality auxiliary judgment method is characterized by comprising the following steps:
s1, setting a quality monitoring server;
s2, the quality monitoring server collects process data and equipment state data of each point position of the SPR equipment or the FDS equipment in real time in the working process;
s3, the quality monitoring server executes a corresponding SPR quality defect judgment program or an FDS quality defect judgment program, carries out quality defect judgment on the collected process data and equipment state data corresponding to each point location, and gives the type of the quality defect of the point location and the reason causing the quality defect if the quality defect is judged to exist;
and S4, the quality monitoring server executes a quality early warning judgment program, judges whether the point position statistical condition with the quality defect meets the quality early warning condition, and if so, performs early warning and stops the production of corresponding equipment.
2. The method for assisting in determining the connection quality of a lightweight vehicle body according to claim 1, wherein in step S3, when it is determined in real time that the current point location has a quality defect, the quality monitoring server records information of the current point location, including a vehicle body number, a point location number, a point time, point process data, a defect type, and a defect cause, and stores the information into a database of the quality monitoring server for subsequent review and rework.
3. The lightweight vehicle body attachment quality auxiliary determination method according to claim 1, wherein the quality warning determination program, when executed, implements the steps of:
when the number of the counted samples is less than or equal to X, early warning is carried out at the Y-th defect point; when the number of the counted samples is equal to X and the number of the defect points is less than Y, no early warning is generated, the next counting period is started, and the counted points are not repeatedly counted; after the early warning is generated, when the production is recovered, when the Y + n point position is a defect point, the early warning is continuously sent out, when the Y + n point position is a normal point, the early warning is started to remove statistics, then the normal early warning of all the continuous Z point positions can be removed, otherwise, the early warning is continuously sent out, after the early warning is removed, the next statistical period is started, wherein the number of early warning statistical samples is set to be X, the number of defect points is Y, the number of the continuous point positions removed by the early warning is set to be Z, and n is a natural number.
4. The method for assisting in determining the connection quality of a lightweight vehicle body according to claim 1, wherein in step S4, when the warning is generated, the quality monitoring server further pushes a cause of the quality defect at each quality defect point through a system interface to assist in manual troubleshooting.
5. The lightweight vehicle body connection quality assistance determination method according to claim 4, wherein a failure assistance determination program module is stored in the quality monitoring server, and when executed, the failure assistance determination program module implements the steps of: and continuously classifying and counting the quality defect reasons in the samples with the same quality defect at the background, sequencing the quality defect reasons in sequence from more to less according to the quantity counted by the quality defect reason types, and pushing the quality defect reasons on a system interface according to the sequence of sequencing the quality defect reasons for the same quality defect which occurs again during quality early warning.
6. The lightweight vehicle body connection quality auxiliary judgment method according to claim 4, wherein the quality monitoring server performs early warning by sending a quality early warning list to a system interface, wherein the quality early warning list includes quality defect point location information, quality defect type information, and quality defect cause information.
7. The lightweight body attachment quality auxiliary determination method according to claim 1, wherein the quality monitoring server stores an SPR quality defect determination program, an FDS quality defect determination program, and a quality warning determination program.
8. The lightweight vehicle body connection quality auxiliary judgment method according to claim 1, wherein the SPR mass defect judgment program includes an SPR mass defect judgment sub-algorithm and an SPR cause classification sub-algorithm, and when executed, the SPR mass defect judgment program realizes the steps of:
s31, inputting an algorithm standard value of each point position on the vehicle body in the quality monitoring server in advance;
s32, when the SPR equipment performs dotting, the quality monitoring server acquires the current dotting point number and the process data generated by the SPR equipment in the dotting process in real time;
s33, according to the point location number obtained in the step S32, the quality monitoring server matches the algorithm standard value corresponding to the point location, the SPR quality defect judgment sub-algorithm corresponding to the point location and the SPR reason classification sub-algorithm corresponding to the point location, which are input in advance in the step S31;
s34, using the process data corresponding to the point location acquired in the step S32 in an SPR quality defect judgment sub-algorithm corresponding to the point location matched in the step S33 to judge the quality defect so as to judge whether the point location has the quality defect;
and S35, applying the process data corresponding to the point acquired in the step S32 to the SPR reason classification sub-algorithm corresponding to the point matched in the step S33 for identifying and classifying the quality defect types and the defect reasons so as to obtain and give classification results of the quality defect types and the defect reasons, wherein the step is executed only when the point is judged to have the quality defect in the step S34.
9. The lightweight body attachment quality auxiliary determination method according to claim 1, wherein the FDS quality defect determination program includes an FDS quality defect determination sub-algorithm and an FDS cause classification sub-algorithm, and when executed, the FDS quality defect determination program realizes the steps of:
s31', inputting an algorithm standard value of each point position on the vehicle body in the quality monitoring server in advance;
s32', when the FDS equipment performs dotting, the quality monitoring server acquires the current dotting point number and the process data generated by the FDS equipment in the dotting process in real time;
s33 ', according to the point location number obtained in the step S32 ', the quality monitoring server matches the algorithm standard value corresponding to the point location, the FDS quality defect judgment sub-algorithm corresponding to the point location and the FDS reason classification sub-algorithm corresponding to the point location which are input in advance in the step S31 ';
s34 ', the process data corresponding to the point acquired in the step S32 ' is used for quality defect judgment in the FDS quality defect judgment sub-algorithm corresponding to the point matched in the step S33 ', so as to judge whether the point has quality defects;
and S35 ', using the process data corresponding to the point acquired in the step S32' in the FDS reason classification sub-algorithm corresponding to the point matched in the step S33 'to identify and classify the quality defect types and the defect reasons so as to obtain and give classification results of the quality defect types and the defect reasons, wherein the step is executed only when the point is judged to have the quality defect in the step S34'.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112541707A (en) * | 2020-12-24 | 2021-03-23 | 安徽巨一科技股份有限公司 | FDS bottom layer plate thickness judgment method and device, electronic equipment and storage medium |
| CN113102984A (en) * | 2021-04-28 | 2021-07-13 | 浙江吉利控股集团有限公司 | Online repairing method and tool for automobile FDS sliding tooth failure, controller and storage medium |
| EP4043975A1 (en) * | 2021-02-15 | 2022-08-17 | Siemens Aktiengesellschaft | Computer-implemented method for determining at least one quality attribute for at least one defect of interest |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006116560A (en) * | 2004-10-20 | 2006-05-11 | Honda Motor Co Ltd | Method of self-piercing rivet joint |
| CN102004056A (en) * | 2010-12-24 | 2011-04-06 | 上海交通大学 | Self-piercing riveting quality online detection system and method |
| CN106938315A (en) * | 2017-03-02 | 2017-07-11 | 上海交通大学 | A kind of aircraft pneumatic riveting quality on-line detecting device and detection method |
| CN108500195A (en) * | 2018-04-04 | 2018-09-07 | 眉山中车紧固件科技有限公司 | intelligent riveting quality monitoring method |
| CN108971409A (en) * | 2018-06-30 | 2018-12-11 | 合肥巨智能装备有限公司 | A kind of aluminium vehicle body self-piercing riveting method of quality control based on power and displacement curve |
| CN111174705A (en) * | 2019-12-04 | 2020-05-19 | 广州市德力达智能设备有限公司 | Cable cutting and stripping quality monitoring device |
| CN111199072A (en) * | 2019-12-13 | 2020-05-26 | 同济大学 | All-aluminum body riveting system based on online process library self-learning and its realization method |
-
2020
- 2020-07-28 CN CN202010737257.0A patent/CN112015149A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006116560A (en) * | 2004-10-20 | 2006-05-11 | Honda Motor Co Ltd | Method of self-piercing rivet joint |
| CN102004056A (en) * | 2010-12-24 | 2011-04-06 | 上海交通大学 | Self-piercing riveting quality online detection system and method |
| CN106938315A (en) * | 2017-03-02 | 2017-07-11 | 上海交通大学 | A kind of aircraft pneumatic riveting quality on-line detecting device and detection method |
| CN108500195A (en) * | 2018-04-04 | 2018-09-07 | 眉山中车紧固件科技有限公司 | intelligent riveting quality monitoring method |
| CN108971409A (en) * | 2018-06-30 | 2018-12-11 | 合肥巨智能装备有限公司 | A kind of aluminium vehicle body self-piercing riveting method of quality control based on power and displacement curve |
| CN111174705A (en) * | 2019-12-04 | 2020-05-19 | 广州市德力达智能设备有限公司 | Cable cutting and stripping quality monitoring device |
| CN111199072A (en) * | 2019-12-13 | 2020-05-26 | 同济大学 | All-aluminum body riveting system based on online process library self-learning and its realization method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112541707A (en) * | 2020-12-24 | 2021-03-23 | 安徽巨一科技股份有限公司 | FDS bottom layer plate thickness judgment method and device, electronic equipment and storage medium |
| CN112541707B (en) * | 2020-12-24 | 2024-04-09 | 安徽巨一科技股份有限公司 | FDS bottom plate thickness determination method and device, electronic equipment and storage medium |
| EP4043975A1 (en) * | 2021-02-15 | 2022-08-17 | Siemens Aktiengesellschaft | Computer-implemented method for determining at least one quality attribute for at least one defect of interest |
| US12007871B2 (en) | 2021-02-15 | 2024-06-11 | Siemens Aktiengesellschaft | Computer-implemented method for determining at least one quality attribute for at least one defect of interest |
| CN113102984A (en) * | 2021-04-28 | 2021-07-13 | 浙江吉利控股集团有限公司 | Online repairing method and tool for automobile FDS sliding tooth failure, controller and storage medium |
| CN113102984B (en) * | 2021-04-28 | 2023-02-28 | 浙江吉利控股集团有限公司 | Automobile FDS sliding tooth failure online repairing method and tool, controller and storage medium |
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