CN113829034B - Quality monitoring method, system and equipment based on bolt tightening working curve - Google Patents
Quality monitoring method, system and equipment based on bolt tightening working curve Download PDFInfo
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- CN113829034B CN113829034B CN202010587080.0A CN202010587080A CN113829034B CN 113829034 B CN113829034 B CN 113829034B CN 202010587080 A CN202010587080 A CN 202010587080A CN 113829034 B CN113829034 B CN 113829034B
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- B23P—METAL-WORKING NOT OTHERWISE PROVIDED FOR; COMBINED OPERATIONS; UNIVERSAL MACHINE TOOLS
- B23P19/00—Machines for simply fitting together or separating metal parts or objects, or metal and non-metal parts, whether or not involving some deformation; Tools or devices therefor so far as not provided for in other classes
- B23P19/04—Machines for simply fitting together or separating metal parts or objects, or metal and non-metal parts, whether or not involving some deformation; Tools or devices therefor so far as not provided for in other classes for assembling or disassembling parts
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Abstract
The application relates to a quality monitoring method, system and equipment based on a bolt tightening working curve. According to some embodiments of the present application, there is provided a quality monitoring method, including: acquiring a torque time curve of bolt tightening operation of a station; comparing the torque-time curve to a standard torque-time curve, the standard torque-time curve being obtained based on torque-time curves of historical multiple qualified bolt tightening operations at the station and characterizing the qualified bolt tightening operations at the station; and determining whether the torque-time profile is an abnormal profile based on the comparison, thereby indicating whether the bolt tightening operation of the station reflects a potential quality issue. The embodiment of the application provides more accurate, efficient and consistent abnormal curve identification.
Description
Technical Field
The present disclosure relates to quality monitoring, and in particular to a quality monitoring method, system and apparatus based on bolt tightening work curves.
Background
In various production manufacturing fields, the use of a large number of bolts is involved in the assembly of products. Quality issues related to the bolts and their operation often affect the quality of the product. In the automotive field, for example, quality problems due to the bolts and their operation can cause serious safety hazards when, for example, automotive safety parts are involved.
Disclosure of Invention
According to some embodiments of the present application, there is provided a quality monitoring method, including: acquiring a torque time curve of bolt tightening operation of a station; comparing the torque-time curve to a standard torque-time curve, the standard torque-time curve being obtained based on torque-time curves of historical multiple qualified bolt tightening operations at the station and characterizing the qualified bolt tightening operations at the station; and determining whether the torque-time profile is an abnormal profile based on the comparison, thereby indicating whether the bolt tightening operation of the station reflects a potential quality issue.
In some embodiments, the step of comparing the torque-time curve to a standard torque-time curve further comprises: calculating characteristic parameters of the torque time curve; and comparing the characteristic parameter of the torque-time curve with the characteristic parameter of the standard torque-time curve.
In some embodiments, the characteristic parameter of the torque-time curve is calculated in the same way as the characteristic parameter of the standard torque-time curve.
In some embodiments, calculating the characteristic parameter of the torque-time curve further comprises: dividing the torque-time curve into a plurality of sections; and calculating a characteristic parameter of the torque-time curve for at least one section but not all sections.
In some embodiments, the characteristic parameter of the torque-time curve is based on at least one of: a first standard deviation of values of a first analysis segment of the torque-time curve; and a second standard deviation of slopes of a plurality of subsections of a second analysis section of the torque-time curve, wherein the second analysis section is different from the first analysis section and the second analysis section is equally divided into the plurality of subsections.
In some embodiments, the first analysis section is a section between a starting point of the torque-time curve to a last slope jump before the first target torque, and the second analysis section is a section between a first slope jump after the first target torque point and a final target torque point of the torque-time curve.
In some embodiments, the standard deviation of the torque-time curve is a weighted sum of the first standard deviation and the second standard deviation.
In some embodiments, the quality monitoring method may further comprise: if the characteristic parameter of the torque time curve is less than or equal to the characteristic parameter of the standard torque time curve, determining that the torque time curve is a normal curve; and if the characteristic parameter of the torque-time curve is larger than that of the standard torque-time curve, determining that the torque-time curve is an abnormal curve.
In some embodiments, the quality monitoring method may further comprise: in response to determining that the torque-time curve is an abnormal curve, calculating a similarity measure between the torque-time curve and each of one or more abnormal curves stored in a library of abnormal curves, resulting in one or more similarity measures; determining that there is at least one abnormal curve similar to the torque-time curve based on the one or more similarity metrics; and determining the abnormal curve which is most similar to the torque-time curve as the abnormal curve matched with the torque-time curve, thereby determining the potential quality problem corresponding to the torque-time curve, wherein each abnormal curve in the abnormal curve library corresponds to the potential quality problem.
In some embodiments, the quality monitoring method may further comprise: determining, based on the one or more similarity measures, that there is no abnormal curve similar to the torque-time curve, thereby determining that the torque-time curve does not match any abnormal curve in a library of abnormal curves; tagging the torque-time curve, wherein tagging comprises manually identifying a corresponding potential quality issue; and storing the torque-time curve to an anomaly curve library.
In some embodiments, the quality monitoring method may further comprise: determining that the bolt tightening operation meets a first quality requirement based on the torque and angle at the end of the bolt tightening operation falling within a standard torque range and a standard angle range; and responsive to determining that the bolt tightening operation meets the first quality requirement, obtaining a torque-time curve for the bolt tightening operation.
In some embodiments, the step of obtaining a torque-time curve for a bolt tightening operation for a station may comprise: acquiring a set of sensor data of the bolt tightening operation of the station; and fitting the set of sensor data to obtain the torque-time curve.
In some embodiments, the standard torque-time curve is obtained by averaging torque-time curve alignments of the historical plurality of qualified bolt tightening operations at the station.
According to further embodiments of the present application, there is provided a computer system including: one or more processors, and a memory coupled with the one or more processors, the memory storing computer-readable program instructions that, when executed by the one or more processors, perform a method as described above.
According to further embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable program instructions, which when executed by a processor, perform the method as described above.
According to further embodiments of the present application, there is provided a quality monitoring apparatus comprising means for carrying out the operations of the method as described above.
Drawings
FIG. 1 is a schematic diagram of an exemplary control strategy for illustrating bolt tightening operations.
Fig. 2 is a torque angle diagram for explaining quality detection of a bolt tightening operation in the related art.
Fig. 3 shows a schematic diagram of a comparison of an ideal torque time curve and an actual measured anomaly curve for a bolt tightening operation according to an embodiment of the present disclosure.
Fig. 4 shows a flow chart of a quality monitoring method based on a working curve of a bolt tightening operation according to an embodiment of the present disclosure.
Fig. 5 shows a flow chart of another quality monitoring method based on a work curve of a bolt tightening operation according to an embodiment of the present disclosure.
FIG. 6 illustrates a schematic diagram of an exemplary standard torque-time curve, according to an embodiment of the present disclosure.
FIG. 7 illustrates a schematic diagram of an exemplary torque-time curve to be evaluated, according to an embodiment of the present disclosure.
Fig. 8 shows a flow chart of yet another quality monitoring method based on a work curve of a bolt tightening operation according to an embodiment of the present disclosure.
Fig. 9 is a schematic diagram illustrating a general hardware environment in which a device according to an embodiment of the present disclosure may be implemented.
Detailed Description
The following description is presented to enable any person skilled in the art to make and use the embodiments, and is provided in the context of a particular application and its requirements. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the described embodiments. Thus, the described embodiments are not limited to the embodiments shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein.
The threaded coupling is achieved by applying a certain torque to the threaded members so that the coupled members are subjected to a sufficient clamping force to ensure that the coupled members are reliably and tightly coupled together under no load or load. The magnitude of the clamping force is generally used to evaluate the quality of the threaded connection. The clamping force is too small and the coupled member may easily loosen. The clamping force is too large, and the threaded member and the coupled member are easily damaged.
Common process methods for threaded connection assembly include a direct torque control method, a torque control angle monitoring method, an angle control torque monitoring method, a yield point control method, a bolt length method and the like.
For bolt joints of critical parts, such as wheels, steering systems, braking systems, etc., torque control angle monitoring or angle control torque monitoring is generally used to control and detect torque and/or angle-related production data in order to achieve high-quality assembly and to avoid as much as possible failures of the bolt joints that could endanger the safety of passengers and pedestrians.
FIG. 1 is a schematic diagram of an exemplary control strategy for illustrating a bolt tightening operation. As shown in the torque curve of fig. 1, the bolt tightening operation involves two steps, i.e., a two-step tightening method is employed. The first step is a pre-tightening process, the torque reaches a first target torque value at the end of the pre-tightening process, as shown by P111, and then the electric gun stops rotating or is loosened reversely, and the torque value naturally falls back. The second step continues to tighten until the final torque value is reached, as indicated at P113. After that, the electric gun stops working.
For a bolt at a certain station, an operator can set some parameters on the electric gun. This means that, when the electric gun is performing the bolt-tightening operation for the station, ideally its working curve must pass through the characteristic points represented by these parameters.
For example, it is possible to set:
P110＝2.85N·M，
P111＝5.70N·M，
P113＝19.00N·M，
P120＝9.50N·M，
P130＝50r/min，
P131＝300r/min，
P132＝30r/min，
wherein N.M is Niumm, and r/min is r/min.
Where P110 denotes a torque at the start of a cycle of the bolt tightening operation, P111 denotes a first target torque value to be reached by the pre-tightening process, P113 denotes a final target torque value to be finally reached by the bolt tightening operation, P120 denotes a torque value at the start of calculation of the final angle, P130 denotes a soft start speed of the torch head during the first step of the process, P131 denotes a rotation speed of the torch head for the first step, and P132 denotes a rotation speed of the torch head for the second step.
Fig. 2 is a torque angle diagram for explaining quality detection of a bolt tightening operation in the related art.
As mentioned above, for bolt tightening operations at important stations, both torque and angle need to be monitored to ensure that both torque and angle are acceptable.
In the related art, threshold ranges of torque and angle are typically set, as indicated by the blocks in fig. 2. In this figure, an acceptable torque range of 16.15-21.85 Niu meters is shown, and an acceptable (relative) angle range of 10-90 revolutions per minute. In the related art, if a torque angle curve of a bolt tightening operation shows that a final torque reached at the time of tightening satisfies a threshold range of torque while an angle reached satisfies an angle threshold, the result of the bolt tightening operation is judged to be qualified. If the two thresholds cannot be satisfied simultaneously, the result of the bolt tightening operation is determined to be not qualified.
In the case where the result of the bolt tightening operation is determined to be unsatisfactory, the operator empirically determines the cause of the result of the bolt tightening operation being unsatisfactory, i.e., a quality problem.
The judgment method only inspects the final torque and angle and does not inspect the parameter change of the whole process of the bolt tightening operation, so that the judgment accuracy has certain limitation.
In addition, since the reason why the result of the bolt tightening operation is not qualified is determined completely depending on the experience of the operator, the judgment result is often different from person to person and does not have accuracy and consistency.
Moreover, relying entirely on the experience of the operator to determine the cause of the failure of the result of the bolt tightening operation is inefficient and detrimental to production quality management.
The inventors of the present application have conceived that the variation with time of the torque during the bolt tightening operation can be further examined to more accurately detect the quality of the bolt tightening operation. Specifically, the inventors of the present application conceived of establishing a standard torque-time curve, identifying an abnormal real-time torque-time curve based on a comparison of the real-time torque-time curve during a tightening operation with the standard torque-time curve, and thereby identifying whether the bolt tightening operation reflects a quality issue. Compared with the method only considering the final torque and the final angle, the method further considers the change of the torque along with the time in the bolt tightening operation process, and can further identify abnormal bolt tightening operation from the bolt tightening operation which is judged to be qualified in result based on the final torque and the final angle, so that possible quality problems can be identified more accurately.
The inventors of the present application have also contemplated segmenting the torque-time curve and analyzing it for one or more segments rather than the entire torque-time curve to more accurately identify anomalous curves while reducing the amount of unnecessary computation.
The inventors of the present application further contemplate that an anomaly curve library is constructed, which may include one or more anomaly curves and each anomaly curve corresponds to a particular potential quality issue.
And comparing the identified abnormal curve to be evaluated with the abnormal curve in the abnormal curve library. If the matched known abnormal curve exists in the abnormal curve library, the potential quality problem corresponding to the abnormal curve to be evaluated can be rapidly and accurately determined. The manual identification for each abnormal curve is avoided. And such identification is more accurate and efficient. In addition, the abnormal curve library can be continuously updated, so that the accuracy and efficiency of identifying the abnormal curve are continuously improved.
Fig. 3 shows a schematic diagram of a comparison of an ideal torque time curve and an actual measured anomaly curve for a bolt tightening operation according to an embodiment of the present disclosure.
The ideal torque time curve for a two-step bolt tightening operation is shown as the dashed curve 301. As can be seen from the curve 301, the ideal torque-time curve has two peak points P1 and P2, where P1 represents the target torque value of the first step and P2 represents the target torque value finally reached. In the first step, the curve 301 rises smoothly from the starting point to the first peak point P1, and then the gun loosens, and the torque value of the curve naturally falls back. In the second step, the curve 301 starts to rise smoothly from the lowest point of the fall back to the second peak point P2, and then the gun loosens, and the torque value of the curve naturally falls back.
The curve 302 indicated by the solid line is an abnormal curve actually measured in the bolt tightening operation, and as can be seen from the profile of the curve 302, the curve has a plurality of peak points, and the profile of the curve does not exhibit a rule of smoothly rising again to smoothly falling, and then smoothly rising again to smoothly falling.
Although the torque and angle ultimately achieved by curve 302 may meet the torque and angle thresholds that qualify the results of the bolt tightening operation, it is likely that there is a quality issue from a comparison of its torque time curve with the ideal torque time curve.
Fig. 4 shows a flow chart of a quality monitoring method based on a working curve of a bolt tightening operation according to an embodiment of the present disclosure.
As shown in fig. 4, the method includes step 401, where a torque-time curve for a bolt tightening operation at a station is obtained.
In some embodiments, a set of sensor data for the bolt tightening operation for that station may be acquired via a sensor on the electric gun. The set of sensor data is then fitted to obtain the torque-time curve.
In some embodiments, a least squares polynomial curve fit may be employed.
Specifically, assume that the discrete sensor data acquired is pi (xi, yi), where i =1,2, … …, m. Calculating an approximate curve of the curve y = f (x) using least squares polynomial curve fitting
So that the deviation of the approximation curve from y = f (x) is minimized. The deviation of the approximation curve at the point pi is
i =1,2, … … m. And selecting a fitting curve according to the principle of minimum deviation sum of squares, and adopting a binomial equation to fit the curve. The formula for minimizing the sum of squared deviations here is:
other methods may also be used to perform the curve fitting, such as interpolation (cubic spline interpolation, etc.), polishing, approximating the discrete data with analytical expressions, and the like.
It will be appreciated that any suitable curve fitting method may be selected by one skilled in the art to fit a torque-time curve to the discrete sensor data obtained, and is not limited herein.
The method further includes step 403, where the torque-time curve is compared to a standard torque-time curve that is obtained based on the torque-time curves of the historical plurality of qualified bolt tightening operations at the station and that characterizes the qualified bolt tightening operations at the station.
In some embodiments, the standard torque-time curve is determined from qualified torque-time data or qualified torque-time curves collected for a plurality of historical bolt tightening operations for the station.
Where the qualifying torque-time data collected for a plurality of historical bolt tightening operations is discrete, curve fitting may be performed to obtain a historical qualifying torque-time curve. The method of fitting may be the same or different from the fitting method described above for step 401.
In some embodiments, for example, multiple bolt tightening operations are performed for the same station and using the same tightening strategy settings (e.g., the same P-value settings described for fig. 1). Corresponding torque time curves for these bolt tightening operations are collected. Multiple historical torque-time curves are screened out via human experience, for example by observing whether the torque-time curves approach the profile of the ideal torque-time curve.
By using the plurality of qualified historical torque time curves, a standard torque time curve can be obtained to be used as a reference curve for screening the abnormal curve.
In one embodiment, the standard torque-time curves may be obtained by aligning and averaging the plurality of historical torque-time curves. For example, each historical torque-time curve may be segmented according to the feature points, and then the data of each segment may be aligned by interpolation or value, so as to average the segments of the multiple historical torque-time curves.
In some embodiments, the comparison between the torque-time curve and the standard torque-time curve may be based on a comparison between all sections of the two curves.
In other embodiments, the comparison between the torque-time curve and the standard torque-time curve may be made based only on the selected analysis section or sections. This is because the inventors have recognized that the process corresponding to the partial section in the torque-time curve may have little or no effect on the quality of the entire bolt tightening operation and therefore may be discarded in the analysis and comparison. This will be described in detail below in conjunction with fig. 6 and 7.
The method 400 also includes step 405, where based on the comparison, it is determined whether the torque-time curve is an abnormal curve, thereby indicating whether the bolt tightening operation of the station reflects a potential quality issue.
By the method of the present application, an abnormal torque time curve is identified by comparing an actually measured torque time curve (i.e., a torque time curve to be evaluated) with a standard torque time curve obtained based on the torque time curves of historical multiple qualified bolt tightening operations, thereby identifying whether a potential quality problem exists. On one hand, the standard torque time curve can accurately reflect the actual qualified torque time curve, and a reliable reference basis is provided for the comparison. On the other hand, the change of the torque along with the time is considered, inconsistency and unreliability brought by manual experience-based comparison are avoided, and the abnormal curve can be identified more accurately, efficiently and reliably, so that abnormal bolt tightening operation can be identified.
Fig. 5 shows a flow chart of another quality monitoring method based on a work curve of a bolt tightening operation according to an embodiment of the present disclosure.
As shown in FIG. 5, the method 500 includes a step 501 in which a torque time curve for a bolt tightening operation for a station is obtained. This step is similar to step 401 in fig. 4 and will not be described again here.
The method 500 further includes step 503, where a characteristic parameter of the torque-time curve is calculated. In some embodiments, this comprises: dividing the torque-time curve into a plurality of sections; and calculating a characteristic parameter of the torque-time curve for at least one section but not all sections.
The method 500 further includes step 505, where the characteristic parameter of the torque-time curve is compared to a characteristic parameter of a standard torque-time curve.
In some embodiments, the characteristic parameter of the torque-time curve is calculated in the same way as the characteristic parameter of the standard torque-time curve.
An example method of feature parameter calculation is described in detail below in conjunction with fig. 6 and 7.
FIG. 6 illustrates a schematic diagram of an exemplary standard torque-time curve in accordance with an embodiment of the present disclosure.
As shown in fig. 6, characteristic points a-F on the curve are identified, where C denotes a first torque target value point, E denotes a final torque target value point, a denotes a start point of a bolt tightening operation, B denotes a last slope discontinuity before the first torque target value, D denotes a first slope discontinuity after the first torque target value, and F is a bolt tightening operation end point.
It will be appreciated by those skilled in the art that the data for the characteristic points in the curve do not necessarily correspond exactly to the settings of the gun when identifying the points a-F. For example, the torque value at point C in the graph does not necessarily coincide exactly with the set value of P111, but it will be appreciated that the point in the graph closest to the set value of P111 may be identified as C. For example, the torque value at point E in the curve does not necessarily coincide exactly with the set value of P113, but the point in the curve closest to the set value of P113 may be identified as E.
The entire standard torque-time curve may be divided into four segments, where A-B are the first segment, B-D are the second segment, D-E are the third segment, and E-F are the fourth segment.
The first section corresponds to a screwing-in stage of a pre-tightening process of bolt tightening operation, is small in torque value, is a screwing-in stage of the electric gun, and is sensitive to a tightening environment.
The second section corresponds to the process of reaching the first target torque and then loosening the electric gun, wherein the final stage of the electric gun pre-tightening adjustment is performed before the first target torque value point C, almost no abnormal condition exists, and the subsequent electric gun control program is operated without the electric gun, so that the section has no influence or little influence on the quality of the whole bolt tightening operation result.
The third segment is the most important part of the overall bolt tightening operation, the torque increases with time, and some slight changes in the environment or operation may cause the torque-time curve to be abnormal.
The fourth section is the torque drop trajectory after the electric gun reaches the peak torque, at which time the no-electric gun control program runs, and therefore, this section has no effect on the quality of the overall bolt tightening operation result.
In some embodiments, the characteristic parameter of the standard torque-time curve may be based on at least one of: a first standard deviation of values of the first section; and a second standard deviation of the slopes of the plurality of subsections of the third section. The third section may be equally divided into the plurality of subsections. For example, the characteristic parameter of the standard torque-time curve may be a weighted sum of the first standard deviation and the second standard deviation.
For example, let V be a first standard deviation of the value of the first segment and a second standard deviation of the slope of the plurality of subsections of the third segment a And V b ，V 0 ＝aV a +bV b Where a, b are the calculated weights of the two parts, respectively, and a + b =1.
Assuming that the number of points acquired for the first segment is k, the points of the first segment are represented as (x) i ,y i ),i＝1,…,k。
Calculating a standard deviation V of the values of the first section according to equations 2 and 3 a ：
If the third segment is divided into t subsections, the collected endpoint value is (x) j ,y j ),j＝0,1,…,t。
For each subsection, according toEquation 4 respectively calculates the slope ρ q ,q＝1,2,…,t
Where j =1 when q =1, and so on, and j = t when q = t.
Then, the standard deviation of the sub-slopes is calculated according to equation 5,
wherein, the first and the second end of the pipe are connected with each other,
in some embodiments, the characteristic parameter may characterize the result of the analysis of the first segment or the result of the analysis of the second segment. For example, the weighting factor a is 0,b is 1. Or the weighting coefficient b is 1,a is 0.
In further embodiments, the characteristic parameter may characterize a combined consideration of the analysis result of the first section or of the analysis result of the second section. For example, in the case where neither of the weighting coefficients a and b is 0.
FIG. 7 shows a schematic diagram of an exemplary torque-time curve to be evaluated, in accordance with an embodiment of the present disclosure.
The torque-time curve in fig. 7 is, for example, a torque-time curve actually measured for a bolt tightening operation at a certain station.
As shown in fig. 7, similar to fig. 6, characteristic points a-F are identified, where C denotes a first torque target value point, E denotes a final torque target value point, a denotes a start point of a bolt tightening operation, B denotes a last slope discontinuity before the first torque target value, D denotes a first slope discontinuity after the first torque target value, and F is a bolt tightening operation end point.
Likewise, those skilled in the art will appreciate that the actual measured data does not necessarily correspond exactly to the set point for the gun at several points a-F, e.g., the torque value at point C in the graph does not necessarily correspond exactly to the set value for P111, but that the point in the graph closest to the set value for P111 may be identified as C. For example, the value of the point E in the figure does not necessarily coincide exactly with the set value of P113, but the point closest to the set value of P113 in the curve may be identified as E.
In some embodiments, the method of calculating characteristic parameters for the torque-time curves shown in FIG. 7 may be the same as the method of calculating characteristic parameters for the standard time curves described in connection with FIG. 6.
For example, in fig. 7, the curve is similarly divided into four segments, and a first segment between a to B and a third segment between D to E are taken as analysis emphasis points. The segmentation method is similar to that described with respect to fig. 6 and will not be described again.
Similarly, the characteristic parameter of the torque-time curve to be evaluated may be based on at least one of: a first standard deviation of values of the first section; and a second standard deviation of the slopes of the plurality of subsections of the third section. The third section may be equally divided into the plurality of subsections. For example, the characteristic parameter of the torque-time curve to be evaluated may be a weighted sum of the first standard deviation and the second standard deviation.
For example, let V be a first standard deviation of the value of the first segment and a second standard deviation of the slope of the plurality of subsections of the third segment a ' and V b ′，V 1 ＝aV a ′+bV b ', where a, b are the calculated weights of the two parts, respectively, a + b =1.
Assuming that the number of points acquired for the first segment is k, the points of the first segment are represented as (x) i ′,y i ′),i＝1,…,k。
Calculating a standard deviation V of the values of the first section according to equations 6 and 7 a ′：
If the third segment is divided into t subsections, the collected endpoint value is (x) j ′,y j ′),j＝0,1,…,t。
For each subsection, the slope ρ of each subsection is calculated according to equation 8 q ′,q＝1,2,…,t。
Where j =1 when q =1, and so on, and j = t when q = t.
Then, the standard deviation of the sub-slopes is calculated according to equation 9,
wherein the content of the first and second substances,
as can be seen from the above, the characteristic parameters of the torque-time curve to be evaluated are calculated in the same way as the standard torque-time curve, for example, the identification of the characteristic points and the division of the segments are performed in the same way, the same number of data are sampled for the first segment, the third segment is equally divided into the same number of subsections, and the same weight is used for the standard deviation of the values of the first segment and the standard deviation of the slopes of the subsections of the third segment, respectively.
In some embodiments, by calculating the characteristic parameter V as described above 1 And V 0 A comparison is made to determine if the torque-time curve to be evaluated is abnormal.
In some embodiments, if V 1 Greater than or equal to V 0 Then the torque-time curve to be evaluated is determined to be an abnormal curve. If V 1 Less than V 0 The torque-time curve to be evaluated is determined to be a normal curve.
It will be appreciated by those skilled in the art that the above merely illustrates examples of characteristic parameters and their calculation.
Other feature parameters or other feature parameter calculation methods may be used. For example, the characteristic parameter may be based on a mode distance calculated using a piece-wise linear representation (PLR) algorithm, or may be based on a cumulative distance calculated using a DTW algorithm. It will be appreciated that algorithms and measures that can characterize the degree of pattern matching or similarity between the curves can be used to determine the characteristic parameters. The comparison based on the feature parameter may for example also be based on a comparison of the calculated feature parameter with a threshold value, such as a pattern distance with a pattern distance threshold value, an accumulated distance with an accumulated distance threshold value, etc.
In addition, regardless of which characteristic parameter or characteristic parameter calculation method is used, the torque-time curve may be segmented, the relevant parameters calculated for only one or more segments, and the characteristic parameters used for comparison calculated based on the calculated parameters. Similarly, if multiple sections are involved, a weighted calculation may be used.
According to the method of the present application, the torque-time curve is segmented, and the identification of an abnormal curve can be performed based on the analysis of only one or more sections, not all sections, which can further improve the accuracy of the analysis result and reduce the amount of unnecessary calculation.
Fig. 8 illustrates a flow chart of yet another quality monitoring method 800 based on a work curve of a bolt tightening operation in accordance with an embodiment of the present disclosure.
As shown, method 800 includes step 801, where, in response to determining that the torque-time curve is an abnormal curve, a similarity measure is calculated between the torque-time curve and each of one or more abnormal curves stored in an abnormal curve library, resulting in one or more similarity measures.
The similarity measure may be any measure that can characterize the similarity between two curves. For example, the similarity measure may be based on a mode distance calculated using a Piecewise Linear Representation (PLR) algorithm, or may be based on a cumulative distance calculated using a DTW algorithm. In some embodiments, it may be determined whether the two curves are similar based on a comparison of the value of the similarity metric to a threshold. The magnitude of the value of the similarity measure may characterize the degree of similarity of the two curves.
For example, the similarity metric may also be based on one or more of the following: characteristic parameter (e.g. V) of each curve individually 1 ) The standard deviation of the values of the first segment, the standard deviation of the slope of the subsegment of the third segment; and a characteristic parameter of the standard torque-time curve (e.g. V) 0 ) The standard deviation of the values of the first segment, the standard deviation of the slope of the subsegment of the third segment. For example, the similarity measure may be the difference between the standard deviations of the values of the first section between the curves, with a smaller difference indicating a greater similarity between the curves. The similarity measure may be the difference between the standard deviations of the slopes of the subsections of the third section between the curves, with smaller differences indicating greater similarity between the curves. The similarity measure may also be the difference between the characteristic parameters of different curves, the smaller the difference, the more similar between the curves is represented. As another example, the similarity measure may be the difference between a characteristic parameter of each curve and a characteristic parameter of a standard torque-time curve (e.g., V) 1 And V 0 Difference) of the two curves, the smaller it is, the more similar the curves are.
As shown in fig. 8, if it is determined in step 803 that there is at least one abnormal curve similar to the torque-time curve, the method 800 proceeds to step 805, where the abnormal curve most similar to the torque-time curve is determined as the abnormal curve matching the torque-time curve, thereby determining the potential quality problem reflected by the torque-time curve, wherein each abnormal curve in the abnormal curve library corresponds to a potential quality problem. The library of exception curves may store previously identified exception curves and various parameter values that may characterize the exception curves. Each exception curve may also flag a corresponding potential quality issue.
Potential quality problems may be, for example: the bolt or the gun head is oil-stained; damage to the threads; the thread surface is painted too thick; repeated tightening (anthropogenic reason); welding slag is arranged on the surface of the thread; the gun head sleeve is worn; the material of the connected piece is soft/hard; premature release of the electric gun trigger (anthropogenic cause); gun power is low, etc.
By identifying potential quality issues, valuable reference information can be provided for quality management. For example, if it is determined that a potential quality problem reflected by the anomaly curve is a damaged thread, the operator needs to inspect the bolts at that station, possibly requiring replacement of the bolts. For example, if it is determined that the potential quality problem reflected by the abnormal curve is that the electric gun is low on power, maintenance of the electric gun may be required.
If it is determined at step 803 that no similarity measure is equal to or greater than the similarity measure threshold, meaning that the torque time curve does not match any of the abnormal curves in the library of abnormal curves, the method 800 proceeds to step 807 where the torque time curve is labeled and stored to the library of abnormal curves. In one embodiment, tagging includes manually identifying potential quality issues corresponding to the torque-time curve.
By the method, if the matched known abnormal curve exists in the abnormal curve library, the potential quality problem corresponding to the abnormal curve can be rapidly and accurately determined. The manual identification for each abnormal curve is avoided. And such identification is more accurate and efficient. By the method, the abnormal curve library can be continuously updated, so that the accuracy and efficiency of identifying the abnormal curve are continuously improved. FIG. 9 is a schematic diagram illustrating a general hardware environment in which a device according to embodiments of the present disclosure may be implemented.
Referring now to fig. 9, a schematic diagram of an example of a compute node 900 is shown. Computing node 900 is only one example of a suitable computing node and is not intended to suggest any limitation as to the scope of use or functionality of the embodiments described herein. In any case, computing node 900 is capable of implementing and/or performing any of the functions set forth above.
In computing node 900, there is a computer system/server 9012, which is operable with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 9012 include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computer systems, distributed cloud computing environments that include any of the above systems or devices, and the like.
Computer system/server 9012 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer system/server 9012 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
As shown in fig. 9, computer system/server 9012 in computing node 900 is shown in the form of a general purpose computing device. Components of computer system/server 9012 may include, but are not limited to: one or more processors or processing units 9016, a system memory 9028, and a bus 9018 that couples various system components including the system memory 9028 to the processing unit 9016.
Computer system/server 9012 typically includes a variety of computer system readable media. Such media can be any available media that is accessed by computer system/server 9012 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 9028 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 9032. Computer system/server 9012 may also include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 9034 may be provided for reading from and writing to non-removable, nonvolatile magnetic media (not shown, and commonly referred to as "hard drives"). Although not shown, a magnetic disk drive may be provided for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive may be provided for reading from and writing to a removable, nonvolatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media). In such cases, each may be connected to bus 9018 by one or more data media interfaces. As will be further depicted and described below, memory 9028 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the present disclosure.
By way of example, and not limitation, programs/utilities 9040, including a set of (at least one) program modules 9042, and an operating system, one or more application programs, other program modules, and program data may be stored in memory 9028. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a network environment. Program modules 9042 generally perform the functions and/or methods in the embodiments as described herein.
The computer system/server 9012 may also communicate with one or more external devices 9014 (such as a keyboard, pointing device, display 9024, etc.), one or more devices that enable a user to interact with the computer system/server 9012, and/or any device (e.g., network card, modem, etc.) that enables the computer system/server 9012 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 22. Also, computer system/server 9012 may communicate with one or more networks, such as a Local Area Network (LAN), a general Wide Area Network (WAN), and/or a public network (e.g., the internet) via network adapter 20. As depicted, the network adapter 20 communicates with the other components of the computer system/server 9012 via the bus 9018. It should be appreciated that although not shown, other hardware and/or software components may be used in conjunction with the computer system/server 9012. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data archive storage systems, and the like.
The present disclosure may be embodied as systems, methods, and/or computer program products. The computer program product may include computer-readable storage medium(s) having computer-readable program instructions thereon for causing a processor to perform aspects of the present disclosure.
The computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device such as a punch card or an in-groove protrusion structure having instructions stored thereon, and any suitable combination of the foregoing. A computer-readable storage medium as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded over a network (e.g., the internet, a local area network, a wide area network, and/or a wireless network) to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in the respective computing/processing device.
Computer-readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry, including, for example, programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), may execute the computer-readable program instructions in order to perform aspects of the present disclosure by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry.
Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium having stored thereon the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer-implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It should also be understood by those skilled in the art that various operations illustrated as sequential in the embodiments of the present disclosure do not necessarily have to be performed in the illustrated order. The order of operations may be adjusted as desired by one skilled in the art. One skilled in the art may also add more operations or omit some of them as desired.
The description of the various embodiments of the present disclosure has been presented for purposes of illustration but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques found in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.
Claims (11)
1. A method of quality monitoring, comprising:
acquiring a torque time curve of bolt tightening operation of a station;
comparing the torque-time curve to a standard torque-time curve, the standard torque-time curve being obtained based on torque-time curves of historical multiple qualified bolt tightening operations at the station and characterizing the qualified bolt tightening operations at the station;
determining, based on the comparison, whether the torque-time profile is an abnormal profile, indicating whether the bolt tightening operation of the station reflects a potential quality issue,
wherein the step of comparing the torque-time curve to a standard torque-time curve further comprises:
calculating characteristic parameters of the torque time curve; and
comparing the characteristic parameter of the torque-time curve with the characteristic parameter of a standard torque-time curve,
wherein calculating the characteristic parameters of the torque-time curve further comprises:
dividing the torque-time curve into a plurality of segments;
the characteristic parameters of the torque-time curve are calculated for at least one section but not all sections,
wherein the characteristic parameter of the torque-time curve is based on at least one of:
a first standard deviation of values of a first analysis segment of the torque-time curve; and
a second standard deviation of the slopes of a plurality of subsections of a second analysis section of the torque-time curve, wherein the second analysis section is different from the first analysis section and is divided equally into the plurality of subsections, wherein the first analysis section is a section between a starting point of the torque-time curve and a last slope discontinuity before the first target torque, the second analysis section is a section between the first slope discontinuity after the first target torque point and a final target torque point of the torque-time curve,
wherein the standard deviation of the torque-time curve is a weighted sum of the first standard deviation and the second standard deviation.
2. A quality monitoring method as set forth in claim 1, wherein the characteristic parameter of the torque-time curve is calculated in the same manner as the characteristic parameter of the standard torque-time curve.
3. The quality monitoring method of claim 2, further comprising:
if the characteristic parameter of the torque-time curve is less than or equal to the characteristic parameter of the standard torque-time curve, determining that the torque-time curve is a normal curve; and
and if the characteristic parameter of the torque-time curve is larger than that of the standard torque-time curve, determining that the torque-time curve is an abnormal curve.
4. A quality monitoring method as set forth in claim 3 further comprising:
in response to determining that the torque-time curve is an abnormal curve, calculating a similarity measure between the torque-time curve and each of one or more abnormal curves stored in a library of abnormal curves, resulting in one or more similarity measures;
determining, based on the one or more similarity metrics, that there is at least one abnormal curve that is similar to the torque-time curve;
and determining the abnormal curve which is most similar to the torque-time curve as the abnormal curve which is matched with the torque-time curve, thereby determining the potential quality problem corresponding to the torque-time curve, wherein each abnormal curve in the abnormal curve library corresponds to the potential quality problem.
5. The quality monitoring method of claim 4, further comprising:
determining, based on the one or more similarity measures, that there is no abnormal curve similar to the torque-time curve, thereby determining that the torque-time curve does not match any abnormal curve in the library of abnormal curves;
tagging the torque-time curve, wherein tagging comprises manually identifying a corresponding potential quality issue; and
the torque-time curve is stored in an abnormal curve library.
6. The quality monitoring method of claim 1, further comprising:
determining that the bolt tightening operation meets a first quality requirement based on the torque and angle at the end of the bolt tightening operation falling within a standard torque range and a standard angle range;
in response to determining that the bolt tightening operation meets the first quality requirement, a torque-time curve for the bolt tightening operation is obtained.
7. A quality monitoring method as set forth in claim 1 wherein the step of obtaining a torque time curve for a bolt tightening operation at a station comprises:
acquiring a set of sensor data of the bolt tightening operation of the station; and
fitting the set of sensor data to obtain the torque-time curve.
8. A quality monitoring method as set forth in claim 1 wherein the standard torque-time curve is obtained by averaging the torque-time curve alignments of the historical multiple acceptable bolting operations at that station.
9. A computer system, comprising:
one or more processors, and
a memory coupled with the one or more processors, the memory storing computer-readable program instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.
10. A computer readable storage medium having computer readable program instructions stored thereon that, when executed by a processor, cause the processor to perform the method of any of claims 1-8.
11. A quality monitoring device comprising means for implementing the operations of the method of any one of claims 1-8.
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| CN114459743A (en) * | 2022-01-14 | 2022-05-10 | 东风柳州汽车有限公司 | Bolt abnormity detection method, device, equipment and storage medium |
| CN114749899B (en) * | 2022-03-28 | 2023-04-11 | 五邑大学 | Electric screwdriver screw locking method and system based on reinforcement learning |
| CN116226692B (en) * | 2023-05-09 | 2023-07-28 | 中汽信息科技(天津)有限公司 | Tightening abnormality identification method, device and medium based on dynamic time planning |
| CN117407823B (en) * | 2023-12-14 | 2024-03-15 | 长春泰坦斯科技有限公司 | Method and system for preventing error in bolt tightening based on vision-aided positioning |
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