CN115049170A - Method for debugging threading work of threading machine controller - Google Patents
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Abstract
The invention relates to the technical field of equipment parameter regulation and control, in particular to a method for debugging threading work of a threading machine controller.
Description
Technical Field
The invention relates to the technical field of equipment parameter regulation and control, in particular to a method for debugging the threading work of a threading machine controller.
Background
With the rapid development of the modern building industry, the quantity of processed reinforcing steel bar screws used by the modern building industry is larger and larger, and particularly, more threading screws are used on the building construction site. The threading machine is the equipment of processing reinforcing bar screw rod, the threading machine during operation, put into the pipe chuck earlier the pipe that will process the screw thread, strike the chucking, press the start switch, the pipe just rotates along with the chuck, adjust the die opening size on the die head, set for the screw mouth length, then pull the hand wheel of feed clockwise, make the die sword on the die head paste the tip of pivoted pipe with the constant force, the die sword is with the automatic cutting mantle fiber, but at the threading in-process, reinforcing bar fixed problem and cutter march problem all can influence reinforcing bar threading result.
At present, for reinforcing steel bars with different specifications, the requirement of reinforcing steel bar threading is realized through manual adjustment, but because the working environment and the use time of the threading machine are long, the performance of the threading machine is gradually reduced, an adjustment error and a hysteresis phenomenon can occur by utilizing manual operation under the condition, the equipment parameters of the threading machine cannot be timely and accurately adjusted, and then the reinforcing steel bar threading result is inconsistent.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method for debugging the threading work of a threading machine controller, which adopts the following technical scheme:
acquiring the current of a motor and the advancing speed of a threading die cutter after the current steel bar is clamped based on sampling frequency to obtain a current sequence and an advancing speed sequence of the motor in the whole threading time period of the current steel bar; calculating a motor current stability index according to the difference between adjacent motor currents in the motor current sequence, and calculating a thread uniformity index of the current reinforcing steel bar surface by combining the motor current stability index and the travelling speed change corresponding to the travelling speed sequence;
obtaining the thread uniformity index of each reinforcing steel bar in the current batch under the current threading machine to obtain a thread uniformity index sequence; calculating a local abnormal factor of each thread uniformity index according to the difference of any two thread uniformity indexes in the thread uniformity index sequence, and acquiring the yield of the reinforcing steel bars in the current batch based on the local abnormal factor; obtaining historical steel bar yields of multiple batches to form a historical yield sequence, and performing continuous iterative training on a yield prediction network by using the historical yield sequence to confirm a target yield prediction network corresponding to the current threading machine according to the difference between input data and output data of the yield prediction network;
acquiring the target good product rate prediction network of each threading machine, and enabling the real-time good product rate sequence of each threading machine to pass through the corresponding target good product rate prediction network to obtain a good product rate prediction sequence of each threading machine; obtaining the total quantity of good products of each threading machine according to the prediction sequence of the good product rate and the total quantity of the reinforcing steel bars corresponding to the batch, calculating a good product adaptation index of the current threading machine based on the total quantity of the good products, and calculating a machine aging index of the current threading machine according to a difference value of two adjacent elements in the prediction sequence of the good product rate; and determining a target threading machine according to the good product adaptation index and the machine aging index, and adjusting the equipment parameters of each threading machine to the equipment parameters of the target threading machine.
Further, the method for obtaining the yield of the current batch of steel bars based on the local abnormal factor includes:
setting a local abnormal factor threshold, and when the local abnormal factor is smaller than the local abnormal factor threshold, determining that the steel bar after corresponding threading is a good product; and counting the first quantity of the good products, calculating the ratio of the total quantity of the steel bars in the current batch to the first quantity, and taking the ratio as the steel bar yield.
Further, the method for obtaining the target yield prediction network includes:
based on the improved yield prediction network algorithm, performing one-time iterative training on a yield prediction network by using the historical yield sequence to obtain a yield prediction value corresponding to each historical yield in the historical yield sequence, and forming a historical yield prediction sequence;
respectively calculating first difference values between two elements at corresponding positions in the historical yield prediction sequence and the historical yield sequence, and performing control adjustment on network parameters of a yield prediction network according to the first difference values to obtain a new yield prediction network; the network parameters include: the input space of the yield prediction network and each parameter in the improved yield prediction network algorithm;
performing one-time iterative training on the new yield prediction network by using the historical yield sequence to obtain a new historical yield prediction sequence; calculating a second difference value between two elements at corresponding positions in the historical yield prediction sequence and the fresh historical yield prediction sequence, when the second difference value meets a difference value threshold value, calculating a third difference value between two elements at corresponding positions in the fresh historical yield prediction sequence and the historical yield sequence, accumulating the third difference value to obtain a third difference value accumulated value, and when the third difference value accumulated value is smaller than the accumulated value threshold value, determining that the new yield prediction network is the target yield prediction network.
Further, the improved yield prediction network algorithm is as follows:
wherein the content of the first and second substances,
as a weight value, the weight value,
and
in order to do the number of learning times,
and
for a certain memory cell to be activated,
in order to learn the rate of the data,
in order to be the desired value,
in order to output the value of the output,
in order to generalize the parameters of the process,
is as follows
An activated memory cell
The number of learned times at the time of the sub-learning,
is as follows
An activated memory cell
The number of learned times at the time of the sub-learning,
is a first
An activated memory cell
The number of learned times at the time of the sub-learning,
to balance the learning constants.
Further, the method for performing control adjustment of network parameters on the yield prediction network according to the first difference includes:
when the first difference is larger than or equal to the first difference threshold, based on an expert control system, introducing extremum control for quick correction; when the first difference is less than or equal to the negative of the first difference threshold, then control adjustments are made in conjunction with proportional control and CMAC control.
Further, the method for calculating the good product adaptation index of the current threading machine based on the total number of the good products includes:
and counting the number of good products matched with the standard nuts, and taking the ratio of the number of the good products to the total number of the good products as a good product matching index.
Further, the method for calculating the machine aging index of the current threading machine according to the difference value of two adjacent elements in the yield prediction sequence comprises the following steps:
respectively calculating the predicted value difference between two adjacent predicted values of the good product rate in the good product rate prediction sequence, and accumulating the predicted value difference to obtain a predicted value difference accumulated value; and calculating the ratio between the combined quantity of two adjacent elements in the yield prediction sequence and the difference value accumulated value of the prediction value, and taking the ratio as the machine aging index.
Further, the method for determining the target threading machine according to the good product adaptation index and the machine aging index comprises the following steps:
calculating the ratio between the non-defective product adaptation index and the machine aging index, and taking the ratio as the working state index of the corresponding threading machine; the working state index and the machine aging index are in a negative correlation relationship, and the working state index and the good product adaptation index are in a positive correlation relationship;
and acquiring the working state index of each threading machine, and taking the threading machine corresponding to the largest working state index as the target threading machine.
The embodiment of the invention at least has the following beneficial effects: the method comprises the steps of training an exclusive yield prediction network of each threading machine by utilizing historical data of the threading machine, accurately predicting the yield prediction value of each threading machine in a targeted manner by using the exclusive yield prediction network, confirming the optimal equipment parameters of the threading machine according to the prediction result of each threading machine, and adjusting the equipment parameters of the global threading machine in time, so that each threading machine is in the optimal working state, the consistency of steel bar threading results is improved, and the yield of steel bar threading is also improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating steps of a method for debugging threading operation of a threading machine controller according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, the specific implementation manner, the structure, the features and the effects thereof will be made for a method for debugging the threading operation of a threading machine controller according to the present invention with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of a method for debugging threading work of a threading machine controller provided by the invention in detail with reference to the accompanying drawings.
The embodiment of the invention aims at the following specific scenes: and training a good product rate prediction network of the corresponding threading machine according to the historical operation data of each threading machine, predicting the good product rate by using the good product rate prediction network of each threading machine, acquiring the threading machine with the optimal working state based on a prediction result, and adjusting equipment parameters of other threading machines based on the equipment parameters of the threading machine.
Referring to fig. 1, a flowchart illustrating a method for debugging threading operation of a threading machine controller according to an embodiment of the present invention is shown, where the method includes the following steps:
step S001, acquiring the current of the motor after the current steel bar is clamped and the advancing speed of the die cutter based on the sampling frequency to obtain a current sequence and an advancing speed sequence of the motor in the whole threading period of the current steel bar; and calculating a motor current stability index according to the difference between adjacent motor currents in the motor current sequence, and calculating the thread uniformity index of the current reinforcing steel bar surface by combining the motor current stability index and the corresponding travelling speed change of the travelling speed sequence.
Specifically, when the mantle fiber machine controls the rotary table to clamp, the motor for clamping almost stops rotating, the current is increased greatly, and when the current is increased to a certain value, the system judges that clamping is finished and sends a clamping finishing instruction. Because the reinforcing bar receives the influence of die cutter when rotating, can have slight rocking, lead to reinforcing bar mantle fiber result not perfect, so will monitor the reinforcing bar by the motor current of instantaneous clamp when the mantle fiber, so utilize the industrial ammeter just can real-time measurement press from both sides the motor current when tight through the circuit that connects near the motor, set up sampling frequency, based on a plurality of motor currents of the whole mantle fiber period of sampling frequency collection reinforcing bar, obtain a motor current sequence
Wherein the content of the first and second substances,
for the motor current sampled at the 1 st time,
is as follows
The sub-sampled current of the motor is,
is the number of samples.
Preferably, in the embodiment of the present invention, the sampling frequency is 5 s.
After the reinforcing steel bar is fixed, the use of the die cutter is startedFeeding, the feeding speed of the die cutter directly influences the steel bar threading result to meet the requirements, so the speed measuring sensor can be used for obtaining the advancing speed of the die cutter in real time, namely the advancing speed is the increment of displacement in unit time, and a plurality of advancing speeds in the whole threading time period of a steel bar are obtained on the basis of the set sampling frequency to obtain an advancing speed sequence
Wherein the content of the first and second substances,
is the travel speed of the 1 st sample,
is as follows
The speed of travel of the sub-sample,
is the number of samples.
Further, in the whole threading process of the steel bar, instability occurs in steel bar tightening due to shaking, and further the motor current changes, so that the motor current stability index is calculated according to the difference between adjacent motor currents in the motor current sequence, and the calculation formula of the motor current stability index is as follows:
wherein the content of the first and second substances,
the motor current stability index is obtained;
for the second in the motor current sequence
A motor current;
for the front of the motor current sequence
Average motor current between individual motor currents;
for the front of the motor current sequence
Average motor current between individual motor currents;
for the second in the motor current sequence
Current of motor and
the difference between the individual motor currents.
It should be noted that if no shaking occurs during threading of the steel bar, the magnitude of the motor current when the steel bar is clamped is almost kept unchanged, that is, the difference between the motor current at the current moment and the motor current at the previous moment is zero, and the difference between the motor current mean values is also zero, so that the motor current stability index is kept to be 1; if the motor current changes in the threading process of the reinforcing steel bar, the motor current stability index is smaller than 1, the larger the motor current change degree is, and the closer the motor current stability index is to 0.
Because in the threading process, the cutter can receive the resistance of a thread groove so as to cause the advancing speed of the die cutter to be influenced, the advancing speed between each moment and the previous moment is well controlled, the thread pitch of the steel bars can be more uniform, the thread depth can also be kept consistent, and therefore the thread uniformity index of the current steel bar surface is calculated by combining the motor current stability index and the advancing speed change corresponding to the advancing speed sequence, and the specific method comprises the following steps: obtaining the maximum advancing speed and the minimum advancing speed in the advancing speed sequence and the average advancing speed, and similarly, obtaining the minimum motor current in the motor current sequence, and calculating the thread uniformity index of the surface of the steel bar according to the difference between the maximum advancing speed, the minimum advancing speed and the average advancing speed, the minimum motor current and the motor current stability index, wherein the calculation formula of the thread uniformity index is as follows:
wherein the content of the first and second substances,
is an index of thread uniformity;
is the maximum travel speed;
is the average travel speed;
is the minimum travel speed;
is the minimum motor current;
is a hyperbolic tangent function and is a normalization means;
is an arcsine function and is a normalization means.
It should be noted that the more unstable the motor current, the less ideal the steel bar threading result, and the corresponding steel barThe worse the thread uniformity index is, the positive correlation between the motor current stability index and the thread uniformity index is formed;
the difference value between the maximum value of the advancing speed of the die cutter and the mean value can show whether the die cutter has sudden change behavior at a certain moment in the advancing process and the amplitude value of the maximum sudden change value compared with the mean value, the larger the amplitude value is, the larger the thread pitch is, the deviation occurs, the pitch is different in size, and further the thread uniformity on the surface of the steel bar is poor, otherwise, the smaller the amplitude value is, the uncontrollable deviation occurs in the threading work, the deviation is smaller, the deviation is closer to the mean value and can be ignored, and the thread uniformity on the surface of the steel bar is better at the moment;
the numerical value can show the similarity of each thread which is threaded and the previous thread in the threading process for the relative change condition of the advancing speed of the die cutter, and can show whether the threading machine keeps the same working state to a certain extent, the larger the numerical value is, the smaller the thread uniformity index is, otherwise, the smaller the numerical value is, the larger the thread uniformity index is.
S002, obtaining a thread uniformity index of each reinforcing steel bar in the current batch under the current threading machine to obtain a thread uniformity index sequence; calculating a local abnormal factor of each thread uniformity index according to the difference of any two thread uniformity indexes in the thread uniformity index sequence, and acquiring the yield of the reinforcing steel bars in the current batch based on the local abnormal factor; obtaining the yield of a plurality of batches of historical reinforcing steel bars, forming a historical yield sequence, and performing continuous iterative training on the yield prediction network by using the historical yield sequence so as to confirm the target yield prediction network corresponding to the current threading machine according to the difference between the input data and the output data of the yield prediction network.
Specifically, the method in step S001 is used to obtain the thread uniformity index of each reinforcing steel bar in the next batch of the threading machine, and form a thread uniformity index sequence, that is, one batch corresponds to one thread uniformity index sequence.
Preferably, the embodiment of the invention uses 100 steel bars as a batch.
Calculating the yield of the corresponding reinforcing steel bars in one batch according to the difference of any two thread uniformity indexes in the thread uniformity index sequence, and the specific method comprises the following steps: taking any one thread uniformity index in the thread uniformity index sequence as a target thread uniformity index, respectively calculating thread uniformity index difference values between the target thread uniformity index and other thread uniformity indexes, accumulating the thread uniformity index difference values to obtain a difference accumulated value, calculating a ratio between the difference accumulated value and the number of elements in the thread uniformity index sequence, and taking the reciprocal of the ratio as the local reachable density of the target thread uniformity index; obtaining local reachable density of each thread uniformity index in a thread uniformity index sequence to form a local reachable density set, taking any one local reachable density in the local reachable density set as a target local reachable density, respectively calculating local reachable density difference values between the target local reachable density and other target local reachable densities, accumulating the local reachable density difference values to obtain a local reachable density difference value accumulated value, calculating a ratio between the local reachable density difference value accumulated value and the number of elements in the local reachable density set, and taking a first ratio between the ratio and the target local reachable density as a local abnormal factor corresponding to the target local reachable density, namely the local abnormal factor corresponding to the thread uniformity index, so as to obtain a local abnormal factor of each thread uniformity index; setting a local abnormal factor threshold, when the local abnormal factor is smaller than the local abnormal factor threshold, confirming the steel bars after corresponding threading as good products, counting a first quantity of the good products, calculating a ratio between the total quantity and the first quantity of the steel bars in the current batch, and taking the ratio as the steel bar yield.
Preferably, in the embodiment of the present invention, the local abnormal factor threshold is an empirical value, and then the local abnormal factor threshold is 0.8.
By using the method for acquiring the good product rate of the steel bars, the good product rates of a plurality of batches of historical steel bars under one threading machine are acquired, and a historical good product rate sequence is formed. Continuously and iteratively training a good product rate prediction network of the threading machine by utilizing a historical good product rate sequence, and confirming a target good product rate prediction network corresponding to the threading machine according to the difference between input data and output data of the good product rate prediction network, preferably, the good product rate prediction network in the embodiment of the invention is a CMAC neural network, and the specific process is as follows:
(1) based on the idea of reliability distribution, the training algorithm of the conventional CMAC neural network is improved, and the improved CMAC neural network algorithm is as follows:
wherein the content of the first and second substances,
as a weight value, the weight value,
and
in order to do the number of learning times,
and
for a certain memory cell to be activated,
in order to learn the rate of speed,
in order to be the desired value,
in order to output the value of the output,
in order to generalize the parameters of the process,
is as follows
An activated memory cell
The number of learned times at the time of the sub-learning,
is a first
An activated memory cell
The number of learned times at the time of the sub-learning,
is as follows
An activated memory cell
The number of learned times at the time of the sub-learning,
to balance the learning constants.
It should be noted that the improved training algorithm of the CNAC neural network performs statistics on the number of learning times of each activated memory cell in the training process, the statistics not only includes changes of subsequent learning samples to the same number of activation times of the memory cell, but also includes changes of subsequent training to the number of activation times of the memory cell, and then when the weight is updated, the error is distributed according to the percentage of the number of learning times of the activated memory cell to the sum of the number of learning times of all activated memory cells, and the larger the percentage is, the smaller the distribution error is.
The weight adjustment rule is as follows: if the iterative learning times are more, the reliability of the included information is high, and the adjustment amount is less; if the iterative learning times are few, the reliability of the included information is low, and the adjustment amount is large. Therefore, the learning interference of the subsequent learning samples on the previous learning samples can be reduced, and the learning interference of the subsequent training on the previous training can also be reduced. The algorithm is based on the credibility distribution error, the error is less corrected for the storage unit with more learning times, and the error is more corrected for the storage unit with less learning times, so that the learning interference is reduced.
(2) Inputting the historical yield sequence into an improved CMAC neural network for one-time iterative training to obtain a yield prediction value corresponding to each historical yield in the historical yield sequence to form a historical yield prediction sequence; and respectively calculating first difference values between two elements at corresponding positions in the historical yield prediction sequence and the historical yield sequence, and performing control adjustment on network parameters of the improved CMAC neural network according to the first difference values to obtain a new CMAC neural network, wherein the network parameters comprise input space, generalization parameters, learning rate, learning times and the like of the network and other parameters of the improved CMAC neural network algorithm.
Specifically, an expert coordinator is introduced, and the control strategy is switched according to a first difference value:
wherein, the first and the second end of the pipe are connected with each other,
in order to be controlled by the expert,
it is shown that the CMAC control is,
indicating proportional control, e indicating a first difference,
is a first difference threshold.
The expert control means that extreme value control is introduced to quickly correct the neural network based on an expert control system, so that errors can be reduced in the next iterative training; CMAC control refers to the regulation corresponding to the self-learning of a CMAC neural network; the proportional control is an auxiliary controller of the CMAC neural network, because the independent reference of the proportional control can reduce the relative stability of the system and even cause the instability of the closed-loop system.
(3) Performing one-time iterative training on the new CMAC neural network by using the historical yield sequence to obtain a new historical yield prediction sequence; and calculating a second difference value between two elements at corresponding positions in the historical yield prediction sequence and the fresh historical yield prediction sequence, when the second difference value meets a difference value threshold value, calculating a third difference value between two elements at corresponding positions in the fresh historical yield prediction sequence and the historical yield sequence, accumulating the third difference value to obtain a third difference value accumulated value, and when the third difference value accumulated value is smaller than the accumulated value threshold value, determining the new CMAC neural network as the target CMAC neural network.
Specifically, second difference values between two elements at corresponding positions in the historical yield prediction sequence and the new historical yield prediction sequence are respectively calculated, an average second difference value between all the second difference values is calculated, a second difference value range is set, when the average second difference value is not in the second difference value range, the new CMAC neural network is immediately controlled and adjusted in network parameters, otherwise, when the average second difference value is in the second difference value range, the new CMAC neural network is better than the prediction result of the improved CMAC neural network, then a third difference value between the two elements at corresponding positions in the new historical yield prediction sequence and the historical yield sequence is calculated, the third difference value is accumulated to obtain a third difference value accumulated value, when the third accumulated value is smaller than an accumulated value threshold value, the new CMAC neural network is confirmed to be the target CMAC neural network, and when the third difference value accumulated value is larger than or equal to the accumulated value threshold value, and performing control adjustment on network parameters of the new CMAC neural network, and then performing iterative training on the adjusted network again until a target CMAC neural network is obtained.
The target CMAC neural network is a neural network corresponding to the best generalization ability, and the prediction effect of the neural network is the best.
S003, acquiring a target yield prediction network of each threading machine, and enabling the real-time yield sequence of each threading machine to pass through the corresponding target yield prediction network to obtain a yield prediction sequence of each threading machine; obtaining the total quantity of good products of each threading machine according to the prediction sequence of the good product rate and the total quantity of the reinforcing steel bars corresponding to the batch, calculating a good product adaptation index of the current threading machine based on the total quantity of the good products, and calculating a machine aging index of the current threading machine according to a difference value of two adjacent elements in the prediction sequence of the good product rate; and determining a target threading machine according to the good product adaptation index and the machine aging index, and adjusting the equipment parameters of each threading machine to the equipment parameters of the target threading machine.
Specifically, the method in step S002 is used to obtain a target yield prediction network for each threading machine, that is, one threading machine corresponds to one dedicated target yield prediction network.
And respectively acquiring a real-time yield sequence of each threading machine based on the time sequence, inputting each real-time yield sequence into a corresponding target yield prediction network to obtain a yield prediction sequence of the corresponding threading machine, and in the same way, one threading machine corresponds to one yield prediction sequence. Obtaining the total number of good products of the corresponding threading machine according to the predicted value of each good product rate and the total number of the reinforcing steel bars corresponding to the batch in the good product rate prediction sequence, counting the number of the good products matched with the standard nuts, and taking the ratio of the number of the good products to the total number of the good products as a good product matching index; calculating a machine aging index according to the difference value of two adjacent elements in the yield prediction sequence, wherein the method for acquiring the machine aging index comprises the following steps: and respectively calculating predicted value difference values between two adjacent predicted values of the good product rate in the good product rate prediction sequence, accumulating the predicted value difference values to obtain a predicted value difference value accumulated value, calculating the ratio between the combination quantity of two adjacent elements in the good product rate prediction sequence and the predicted value difference value accumulated value, and taking the ratio as a machine aging index.
The non-defective product adaptation index is whether the steel bar after threading is matched with the standard nut, namely whether the steel bar can be screwed with the standard nut; the machine aging index is the aging degree of the threading machine reflected according to the difference between the yield of each batch, namely the larger the difference is, the more the machine aging is aggravated by the overload work of the threading machine.
Calculating the ratio between the good product adaptation indexes and the machine aging indexes, taking the ratio as the working state index of the corresponding threading machine, wherein the working state index and the machine aging index are in a negative correlation relationship, and the working state index and the good product adaptation indexes are in a positive correlation relationship; the method comprises the steps that a threading machine corresponds to a good product adaptive index and a machine aging index, so that the working state index of each threading machine can be obtained, the threading machine corresponding to the largest working state index serves as a target threading machine, the target threading machine is the threading machine with the best equipment parameters during threading, and therefore in order to ensure that the overall threading machine can achieve the optimal condition, the equipment parameters of each threading machine are adjusted to the equipment parameters of the target threading machine.
It should be noted that, when there are a plurality of target threading machines, the average device parameter of the target threading machine is obtained, and the device parameter of each threading machine is adjusted to the average device parameter.
In summary, the embodiments of the present invention provide a method for debugging threading work of a threading machine controller, where the method trains a proprietary yield prediction network of each threading machine by using historical data of the threading machine, so that the proprietary yield prediction network can predict a yield prediction value of each threading machine accurately, and further confirm an optimal equipment parameter of the threading machine according to a prediction result of each threading machine, so as to adjust the equipment parameter of the global threading machine in time, so that each threading machine is in an optimal working state, improve consistency of steel bar threading results, and improve yield of steel bar threading.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit of the present invention are intended to be included therein.
Claims (8)
1. A method for debugging the threading work of a threading machine controller is characterized by comprising the following steps:
acquiring the current of a motor and the advancing speed of a threading die cutter after the current steel bar is clamped based on sampling frequency to obtain a current sequence and an advancing speed sequence of the motor in the whole threading period of the current steel bar; calculating a motor current stability index according to the difference between adjacent motor currents in the motor current sequence, and calculating a thread uniformity index of the current reinforcing steel bar surface by combining the motor current stability index and the travelling speed change corresponding to the travelling speed sequence;
obtaining the thread uniformity index of each reinforcing steel bar in the current batch under the current threading machine to obtain a thread uniformity index sequence; calculating a local abnormal factor of each thread uniformity index according to the difference of any two thread uniformity indexes in the thread uniformity index sequence, and acquiring the yield of the reinforcing steel bars in the current batch based on the local abnormal factors; obtaining the yield of a plurality of batches of historical reinforcing steel bars to form a historical yield sequence, and performing continuous iterative training on a yield prediction network by using the historical yield sequence to confirm a target yield prediction network corresponding to the current threading machine according to the difference between input data and output data of the yield prediction network;
acquiring the target good product rate prediction network of each threading machine, and enabling the real-time good product rate sequence of each threading machine to pass through the corresponding target good product rate prediction network to obtain a good product rate prediction sequence of each threading machine; obtaining the total quantity of good products of each threading machine according to the prediction sequence of the good product rate and the total quantity of the reinforcing steel bars corresponding to the batch, calculating a good product adaptation index of the current threading machine based on the total quantity of the good products, and calculating a machine aging index of the current threading machine according to a difference value of two adjacent elements in the prediction sequence of the good product rate; and determining a target threading machine according to the good product adaptation index and the machine aging index, and adjusting the equipment parameters of each threading machine to the equipment parameters of the target threading machine.
2. The method for debugging threading work of the threading machine controller according to claim 1, wherein the method for obtaining the yield of the current batch of the steel bars based on the local abnormal factor comprises:
setting a local abnormal factor threshold, and when the local abnormal factor is smaller than the local abnormal factor threshold, determining that the steel bar after corresponding threading is a good product; and counting the first number of good products, calculating the ratio of the total number of the reinforcing steel bars in the current batch to the first number, and taking the ratio as the yield of the reinforcing steel bars.
3. The method for debugging threading work of the threading machine controller according to claim 1, wherein the method for obtaining the target yield prediction network comprises:
based on the improved yield prediction network algorithm, performing one-time iterative training on a yield prediction network by using the historical yield sequence to obtain a yield prediction value corresponding to each historical yield in the historical yield sequence, and forming a historical yield prediction sequence;
respectively calculating first difference values between two elements at corresponding positions in the historical yield prediction sequence and the historical yield sequence, and performing control adjustment on network parameters of a yield prediction network according to the first difference values to obtain a new yield prediction network; the network parameters include: the input space of the yield prediction network and each parameter in the improved yield prediction network algorithm;
performing one-time iterative training on the new yield prediction network by using the historical yield sequence to obtain a new historical yield prediction sequence; calculating a second difference value between two elements at corresponding positions in the historical yield prediction sequence and the fresh historical yield prediction sequence, when the second difference value meets a difference value threshold value, calculating a third difference value between two elements at corresponding positions in the fresh historical yield prediction sequence and the historical yield sequence, accumulating the third difference value to obtain a third difference value accumulated value, and when the third difference value accumulated value is smaller than the accumulated value threshold value, determining that the new yield prediction network is the target yield prediction network.
4. The method for debugging threading work of a threading machine controller according to claim 3, wherein the improved yield prediction network algorithm is as follows:
wherein the content of the first and second substances,
as a weight value, the weight value,
and
in order to do the number of learning times,
and
for a certain memory cell to be activated,
in order to learn the rate of speed,
in order to be the desired value,
in order to output the value of the output,
in order to generalize the parameters of the process,
is as follows
An activated memory cell
The number of learned times at the time of the sub-learning,
is as follows
The activated memory cell
The number of learned times at the time of the sub-learning,
is as follows
The activated memory cell
The number of learned times at the time of the sub-learning,
to balance the learning constants.
5. The method for debugging threading work of the threading machine controller according to claim 3, wherein the method for controlling and adjusting the network parameters of the yield prediction network according to the first difference comprises the following steps:
when the first difference is larger than or equal to the first difference threshold, based on an expert control system, introducing extremum control to carry out rapid correction; when the first difference is less than or equal to the negative of the first difference threshold, then control adjustments are made in conjunction with proportional control and CMAC control.
6. The method for debugging threading work of a threading machine controller according to claim 1, wherein the method for calculating the good product adaptation index of the current threading machine based on the total number of good products comprises:
and counting the number of good products matched with the standard nuts, and taking the ratio of the number of the good products to the total number of the good products as a good product matching index.
7. The method for debugging threading work of the threading machine controller according to claim 1, wherein the method for calculating the machine aging index of the current threading machine from the difference value of two adjacent elements in the yield prediction sequence comprises the following steps:
respectively calculating predicted value difference values between two adjacent predicted values of the good product rate in the good product rate prediction sequence, and accumulating the predicted value difference values to obtain predicted value difference value accumulated values; and calculating the ratio between the combined quantity of two adjacent elements in the yield prediction sequence and the difference value accumulated value of the prediction value, and taking the ratio as the machine aging index.
8. The method for debugging threading work of the threading machine controller according to claim 1, wherein the method for determining the target threading machine according to the good product adaptation index and the machine aging index comprises the following steps:
calculating the ratio between the non-defective product adaptation index and the machine aging index, and taking the ratio as the working state index of the corresponding threading machine; the working state index and the machine aging index are in a negative correlation relationship, and the working state index and the good product adaptation index are in a positive correlation relationship;
and acquiring the working state index of each threading machine, and taking the threading machine corresponding to the largest working state index as the target threading machine.
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