CN116571592A - Monitoring system and punching monitoring system - Google Patents
Monitoring system and punching monitoring system Download PDFInfo
- Publication number
- CN116571592A CN116571592A CN202310838463.4A CN202310838463A CN116571592A CN 116571592 A CN116571592 A CN 116571592A CN 202310838463 A CN202310838463 A CN 202310838463A CN 116571592 A CN116571592 A CN 116571592A
- Authority
- CN
- China
- Prior art keywords
- die
- processing
- tag
- processing equipment
- identity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 54
- 238000004080 punching Methods 0.000 title claims description 19
- 238000012545 processing Methods 0.000 claims abstract description 221
- 238000007405 data analysis Methods 0.000 claims description 34
- 238000004519 manufacturing process Methods 0.000 claims description 33
- 238000000034 method Methods 0.000 claims description 23
- 230000008569 process Effects 0.000 claims description 23
- 230000002159 abnormal effect Effects 0.000 claims description 17
- 230000004044 response Effects 0.000 claims description 11
- 230000005856 abnormality Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 4
- 238000006073 displacement reaction Methods 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 4
- 238000003909 pattern recognition Methods 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 2
- 238000011084 recovery Methods 0.000 claims 1
- 238000003754 machining Methods 0.000 description 19
- 238000004458 analytical method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000010365 information processing Effects 0.000 description 4
- 239000000243 solution Substances 0.000 description 4
- 238000001746 injection moulding Methods 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013475 authorization Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21C—MANUFACTURE OF METAL SHEETS, WIRE, RODS, TUBES OR PROFILES, OTHERWISE THAN BY ROLLING; AUXILIARY OPERATIONS USED IN CONNECTION WITH METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL
- B21C51/00—Measuring, gauging, indicating, counting, or marking devices specially adapted for use in the production or manipulation of material in accordance with subclasses B21B - B21F
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21C—MANUFACTURE OF METAL SHEETS, WIRE, RODS, TUBES OR PROFILES, OTHERWISE THAN BY ROLLING; AUXILIARY OPERATIONS USED IN CONNECTION WITH METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL
- B21C51/00—Measuring, gauging, indicating, counting, or marking devices specially adapted for use in the production or manipulation of material in accordance with subclasses B21B - B21F
- B21C51/005—Marking devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B30—PRESSES
- B30B—PRESSES IN GENERAL
- B30B15/00—Details of, or accessories for, presses; Auxiliary measures in connection with pressing
- B30B15/26—Programme control arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application relates to the technical field of equipment monitoring, and discloses a monitoring system and a stamping monitoring system, wherein the monitoring system comprises a die identity recognition device which at least comprises a tag and a die identity recognition module; the tag is arranged on the processing equipment and used for sending radio frequency signals to the die identity recognition module, the radio frequency signals comprise tag information, and the tag information is used for representing the die identity of the processing die on the processing equipment; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, receiving tag information sent by all the tags under the corresponding preset working frequencies, and further determining the die identity of the processing die on the processing equipment based on the tag information sent by all the tags under the corresponding preset working frequencies.
Description
Technical Field
The application relates to the technical field of equipment monitoring, in particular to a monitoring system and a punching monitoring system.
Background
Processing equipment such as punching machine, injection molding machine, etc. is widely adopted in industry manufacturing fields such as household electrical appliances, automobiles, etc. and has the remarkable characteristic of high production efficiency. Taking a punching machine as an example, the punching frequency of the high-speed punching machine per minute can reach more than 200 times; however, under the high-speed production beat of the punching machine, the identity of the stamping die and the like need to be monitored manually, the change of the identity of the stamping die cannot be monitored timely, the efficiency is low, and a large amount of labor cost is required to be consumed.
Disclosure of Invention
The application mainly solves the technical problem of providing a monitoring system and a stamping monitoring system, which can realize intelligent identification of the mold identity of a processing mold.
In order to solve the technical problems, the application adopts a technical scheme that: providing a monitoring system, wherein the monitoring system comprises a mold identity recognition device; the mold identity recognition device at least comprises a label and a mold identity recognition module; the tag is arranged on the processing equipment and used for sending radio frequency signals to the die identity recognition module, the radio frequency signals comprise tag information, and the tag information is used for representing the die identity of the processing die on the processing equipment; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, receiving tag information sent by all the tags under the corresponding preset working frequencies, and further determining the die identity of the processing die on the processing equipment based on the tag information sent by all the tags under the corresponding preset working frequencies.
In order to solve the technical problems, the application adopts another technical scheme that: the stamping monitoring system comprises a die identity recognition device, wherein the die identity recognition device at least comprises a tag and a die identity recognition module; the label is arranged on the punching machine and used for sending radio frequency signals to the die identity recognition module, the radio frequency signals comprise label information, and the label information is used for representing the die identity of the punching die on the punching machine; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, receiving tag information sent by all the tags under the corresponding preset working frequencies, and further determining the die identity of the stamping die on the stamping machine based on the tag information sent by all the tags under the corresponding preset working frequencies.
According to the technical scheme, the die identity recognition device of the monitoring system at least comprises a tag and a die identity recognition module; the tag is used for sending radio frequency signals comprising tag information representing the identity of the processing die to the die identity recognition module, and the die identity recognition module is used for obtaining the tag information under the preset working frequency of the tag so as to further determine the die identity of the processing die on the processing equipment. Therefore, through setting up the mould identity recognition device of being connected with processing equipment, can intelligently and accurately discern the mould identity of the last processing mould of processing equipment, whole journey need not manual operation and intervenes, has realized the intellectuality of processing mould identity monitoring, has saved the human cost.
Drawings
FIG. 1 is a schematic view of a frame structure of an embodiment of a processing apparatus provided by the present application;
FIG. 2 is a schematic diagram of a frame structure of an embodiment of a mold identification device according to the present application;
FIG. 3 is a logic diagram of an embodiment of a condition recognition device provided by the present application;
fig. 4 is a schematic frame structure of an embodiment of the data analysis device provided by the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is only for descriptive purposes, and is not to be construed as indicating or implying relative importance or implying that the number of technical features indicated is indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
Referring to fig. 1 and fig. 2 in combination, fig. 1 is a schematic frame structure of an embodiment of a processing apparatus according to the present application, and fig. 2 is a schematic frame structure of an embodiment of a mold identity recognition device according to the present application. The monitoring system 10 includes a die identification device 11, the die identification device 11 including at least a tag 111 and a die identification module 112. The tag 111 is configured to be disposed on the processing device 20, where the tag 111 is configured to send a radio frequency signal to the die identity recognition module 112, where the radio frequency signal includes tag information that characterizes a die identity of a processing die on the processing device 20; the die identity recognition module 112 is configured to determine a preset operating frequency of each tag in the tag group including the tag 111, and receive tag information sent by each tag at a corresponding preset operating frequency, so as to determine a die identity of the processing die on the processing device 20 based on the tag information sent by each tag at the corresponding preset operating frequency. That is, the tag 111 is for attaching to the processing apparatus 20 provided with the processing die, so that tag information representing the die identity of the processing die on the processing apparatus 20 can be returned; the mold identity recognition module 112 can perform verification of the mold identity of the machining mold on the machining apparatus 20 based on the tag information, so as to learn the mold identity of the machining mold on the machining apparatus 20, so as to facilitate subsequent determination of whether the machining mold on the machining apparatus 20 is replaced. Therefore, by arranging the die identity recognition device 11 connected with the processing equipment 20, the die identity of the processing die on the processing equipment 20 can be intelligently and accurately recognized, manual operation intervention is not needed in the whole process, the intelligent monitoring of the die identity is realized, and the labor cost is saved.
In one embodiment, as shown in fig. 1, the processing apparatus 20 is a stamping press and the monitoring system 10 is used to monitor the die identity of a stamping die on the stamping press. Of course, in other embodiments, the processing apparatus 20 may be an injection molding machine, and the monitoring system 10 is used to monitor the mold identity of an injection mold on the injection molding machine, which is not limited herein.
In an embodiment, the tag information includes the strength of the radio frequency signal sent by the tag 111, and the die identity recognition module 112 is configured to determine the preset operating frequency of each tag in the tag group including the tag 111, specifically: the die identity recognition module 112 is configured to obtain radio frequency signal intensities of each tag in the tag group including the tag 111 at each operating frequency, and determine a preset operating frequency of each tag in the tag group including the tag 111 based on the radio frequency signal intensities of each tag in the tag group including the tag 111 at each operating frequency. That is, the preset operating frequency of each tag of the tag group including the tag 111 is determined by the radio frequency signal strength of each tag at each operating frequency of each tag of the tag group including the tag 111.
The problem of multi-tag interference occurs after the complex reflection of the rf signal in the current complex industrial metal environment, that is, one die id recognition module 112 may read a plurality of different rf id tags at the same time, so that the die id of the processing die on the processing device 20 cannot be effectively recognized. In theory, the identity tag closest to the processing device 20 may be identified by demodulating the identity tag with the strongest signal strength selection signal returned by the identity tag, however, in the practical application environment, because the center frequency point with the highest sensitivity of the identity tag is offset after the identity tag is installed in a complex metal environment, the electromagnetic wave emitted by the radio frequency antenna in the process of reading the identity tag by the die identity recognition module 112 is a broadband electromagnetic wave, and the center frequency point consistency of the identity tag has a certain error when the identity tag leaves the factory, so that the determined identity tag closest to the processing device may be wrong, thereby determining the die identity of the processing die on the processing device 20 by mistake. Therefore, in one embodiment, the preset operating frequency is the optimal operating frequency, and at this time, for each tag, the die id identifying module 112 is configured to use the operating frequency with the greatest rf signal strength as the optimal operating frequency of the tag.
Specifically, the mold identity recognition module 112 includes an antenna (not shown in the figure), a signal demodulator 1122 and a mold identity recognition module 1123, where the antenna is used to receive a radio frequency signal and transmit the radio frequency signal to the signal demodulator 1122, and the signal demodulator 1122 is used to demodulate the radio frequency signal to obtain tag information; for each tag in the tag group, the die identity recognition module 1123 is configured to use the working frequency with the greatest radio frequency signal strength as the optimal working frequency of the tag; wherein, each label in the label group is a label in the antenna action area, and the label information of each label in the label group is read by the signal demodulator 1122 and then sent to the mold identity recognition module 1123. That is, the mold identity recognition module 1123 determines the actual optimal working frequency of each tag in the tag group including the tag 111 after being affected by the environment, specifically, takes the working frequency with the maximum radio frequency signal strength as the optimal working frequency; then, the antenna receives the radio frequency signals sent by each tag in the tag group including the tag 111 at the corresponding real optimal operating frequency and sends the radio frequency signals to the signal demodulator 1122, and the signal demodulator 1122 demodulates the radio frequency signals to obtain tag information sent by each tag in the tag group including the tag 111 at the corresponding optimal operating frequency and sends the tag information to the die identity recognition module 1123; then, the mold identity module 1123 determines the mold identity of the processing mold on the processing apparatus 20 based on the tag information corresponding to each tag 111 in the tag group including the tag 111. By determining the mold identity of the processing mold on the processing apparatus 20 based on the label information transmitted at the optimal operating frequency of each label in the label group including the label 111, interference caused by the center frequency point offset, the label manufacturing frequency center point error, the signal superposition, and the like of the radio frequency signal after reflection in the complex environment such as metal can be effectively avoided.
In one embodiment, the mold identification module 1123 determines, by scanning, each operating frequency of each tag in the tag group including the tag 111, and further determines an optimal operating frequency of each tag in the tag group including the tag 111.
In an embodiment, the tag information further includes a mold identity number of the corresponding processing mold, and the mold identity recognition module 112 determines, based on the tag information sent by each tag at the respective corresponding preset operating frequency, a mold identity of the processing mold on the processing apparatus 20, specifically: the die identity recognition module 112 obtains the reading success rate corresponding to each tag in the tag group including the tag 111, and takes the die identity number corresponding to the highest radio frequency signal strength and the highest reading success rate as the die identity of the processing die. That is, the radio frequency signal intensity and the reading success rate corresponding to each tag in the tag group including the tag 111 are comprehensively considered to determine the die identity of the processing die on the processing apparatus 20, so that the accuracy of the identification of the processing die is improved. Of course, in other embodiments, only the die id number corresponding to the strongest rf signal strength may be used as the die id of the processing die on the processing apparatus 20. In other embodiments, only the die identity number corresponding to the highest success rate of tag reading may be used as the die identity of the processing die on the processing apparatus 20.
The reading success rate of the tag 111 refers to the ratio between the number of times the signal demodulator 1122 successfully demodulates the radio frequency signal transmitted by the tag 111 and the number of times the signal demodulator 1122 receives the radio frequency signal transmitted by the tag 111.
In one embodiment, the signal demodulator 1122 is a reader that is capable of demodulating the radio frequency signal transmitted by the tag 111 to obtain tag information that characterizes the die identity of the process die on the process tool 20. In the tag communication system, the reader provides energy for the tag 111 to emit electromagnetic waves to the surrounding space, after the electromagnetic waves reach the tag 111, part of the electromagnetic wave energy is absorbed by the tag 111 to drive a circuit of the tag 111 to work, and the other part of the electromagnetic wave energy is emitted to all directions with different intensities, and part of the reflected energy finally returns to the reader; the tag 111 uses the energy reflected by the portion to transmit data to the reader.
In one embodiment, tag 111 is an RFID tag or an NFC tag. In one embodiment, tag 111 is an RFID tag operating in the UHF band.
The processing equipment 20 typically has normal production, mold change, machine debugging, equipment maintenance, etc. during the production process. When the mold is replaced, the process state of the mold is greatly different under different molds and machine stations, and the corresponding pattern recognition parameter configuration needs to be adjusted, otherwise, the monitoring analysis accuracy of the processing equipment 20 is seriously affected. In the single process debugging machine stage, the relevant process parameters are not fixed, and the production process state is also unstable, so that if the data sample of the stage is adopted for self-learning, the monitoring accuracy of the processing equipment 20 is also seriously affected. The samples also need to be relearned after equipment maintenance and the like, otherwise, the monitoring accuracy of the processing equipment is also affected.
Therefore, referring to fig. 1 and 3 in combination, fig. 3 is a logic diagram of an embodiment of the working condition identifying device provided by the present application, in an embodiment, the monitoring system 10 further includes a working condition identifying device 12 and a data collecting device 13, the data collecting device 13 is connected to the working equipment 20, the working condition identifying device 12 is respectively connected to the data collecting device 13 and the die identity identifying module 112 of the die identity identifying device 11, the data collecting device 13 is used for collecting the state parameters in the working process of the working equipment 20, the working condition identifying device 12 is used for determining the current working condition of the working equipment 20 based on the state parameters of the working equipment 20 and the die identity of the working die on the working equipment 20, that is, the die identity identifying result and the state parameters of the die collected by the data collecting device 13 are used as the input of the working condition identifying device 12, and the current working condition of the working equipment 20 is identified. That is, by setting the data acquisition device 13 connected with the processing equipment 20 and the working condition identification device 12 connected with the data acquisition device 13 and the die identity identification module 112 of the die identity identification device 11, the current working condition of the processing equipment 20 can be intelligently and accurately identified, manual operation intervention is not needed in the whole process, and the intellectualization of the working condition monitoring of the processing equipment 20 is realized, so that the corresponding adjustment operation can be performed on the processing equipment 20 according to the working condition of the processing equipment 20 in a later time.
In one embodiment, the current operating conditions of the processing apparatus 20 include abnormal production, including shutdown, machine adjustment, and mold change; the working condition identifying device 12 is configured to determine a current working condition of the processing apparatus 20 based on a state parameter of the processing apparatus 20 and a mold identity of a processing mold on the processing apparatus 20, specifically: the working condition identifying device 12 is configured to determine that the current working condition of the processing apparatus 20 is die change in response to a die identity of a processing die on the processing apparatus 20 being different from a historical identity corresponding to a historical die on the processing apparatus 20; in response to the die identity of the processing die on the processing equipment 20 being the same as the historical identity corresponding to the historical die on the processing equipment 20, and no new state parameter being received within a preset time period after the state parameter is received for the previous time, determining that the current working condition of the processing equipment 20 is shutdown; and determining that the current working condition of the processing equipment 20 is a tuning machine in response to the die identity of the processing die on the processing equipment 20 being the same as the historical identity corresponding to the historical die on the processing equipment 20 and the time interval between the moment the state parameter is currently received and the moment the state parameter is previously received being greater than an interval threshold. That is, when the mold identity is changed, it is determined that the processing apparatus 20 is currently in the mold change state; when the identity of the mold is not changed and the state parameter data is not received within a preset time period after the state parameter data is received last time, indicating that the current processing equipment 20 is in a shutdown state, and judging that the processing equipment 20 is in the shutdown state; when the identity of the mold is not changed and the time interval between the current time when the state parameter is received and the previous time when the state parameter is received is greater than the interval threshold, the time interval when the state parameter is acquired is not uniform, and the processing equipment 20 is indicated to be operated intermittently at this time, so that the processing equipment 20 is determined to be in the machine-adjusting state currently. In other words, by providing the working condition identifying device 12, not only the fault state of the processing apparatus 20 but also the fault state type of the processing apparatus 20 can be identified, which is beneficial to the positioning and quick repair of subsequent faults.
In one embodiment, the current operating conditions of the processing apparatus 20 also include normal production; the working condition identifying device 12 is configured to determine a current working condition of the processing apparatus 20 based on a state parameter of the processing apparatus 20 and a mold identity of a processing mold on the processing apparatus 20, specifically: and in response to the time interval between the time when the state parameter is currently received and the time when the state parameter is previously received being equal to the interval threshold, or a new state parameter is received within a preset time period after the state parameter is previously received, and the die identity of the processing die on the processing equipment is the same as the historical identity corresponding to the historical die on the processing equipment 20, determining that the current working condition of the processing equipment 20 is normal production. That is, when the identity of the mold is not changed and no new state parameter data is received within a preset period of time after the state parameter data is received the last time or the time interval between the time when the state parameter is currently received and the time when the state parameter is received the last time is equal to the interval threshold, the time interval when the state parameter is collected is stable and uniform, and the processing device 20 is shown to be in stable and continuously running, so that the processing device 20 is determined to be in the normal production state currently.
With continued reference to fig. 1, in an embodiment, the current working condition includes abnormal production, the monitoring system 10 further includes a data analysis device 14 and a control device 15, the control device 15 is respectively connected to the working condition identification device 12 and the data analysis device 14, the data acquisition device 13 is connected to the data analysis device 14, the data analysis device 14 is at least used for determining a processing mode of the processing mold, and the control device 15 is used for suspending the operation of the data analysis device 14 in response to the current working condition of the processing apparatus 20 being abnormal production. That is, the monitoring system 10 is also capable of determining a machining mode of the machining die based on the state parameters acquired by the data acquisition device 13 to monitor the machining process; however, considering that the machining die set on the machining device 20 cannot be machined normally when the machining device is in abnormal production, the monitoring system 10 is further provided with a control device 15 for controlling the data analysis device 14 to stop running when the current working condition of the machining device 20 is in abnormal production, so that energy consumption is reduced. In other words, the monitoring system 10 provided by the application can intelligently and accurately identify the processing mode of the processing mold, manual operation intervention is not needed in the whole process, and the intellectualization of the processing mode monitoring of the processing mold is realized, so that the processing equipment 20 can be correspondingly adjusted according to the processing mode of the processing mold in a subsequent process.
In other embodiments, the data analysis device 14 may also determine, based on the state parameters during the processing of the processing apparatus 20 acquired by the data acquisition device 13, classification and characterization of quality defects such as imprint caused by scrap skip and pull-out and burrs caused by punch wear; in addition, it is also possible to analyze whether the current processing die, parameter range, and the like are in a healthy state based on the state parameters in the processing of the processing apparatus 20 acquired by the data acquisition device. In a specific embodiment, the production count in the processing process can be optimized based on the analysis and identification result of the data analysis device 14, so that the counts of air compression and poor quality can be effectively removed, and real-time and accurate statistics of the production reject ratio can be realized.
Since some adjustment or modification of the process parameters of the processing apparatus 20 may be involved after the processing apparatus 20 is restored from the abnormal production state to the normal production state, if the process parameters before the processing apparatus 20 are used to identify the processing mode of the processing mold of the processing apparatus 20, the identification may be wrong, so that the processing yield of the subsequent statistics may be inaccurate. Therefore, in an embodiment, as shown in fig. 4, fig. 4 is a schematic frame structure diagram of an embodiment of a data analysis device provided by the present application, where the data analysis device 14 includes a parameter learning module 141, the parameter learning module 141 is connected to the data acquisition device 13, and the parameter learning module 141 is configured to learn and generate a parameter range corresponding to a normal processing mode based on a state parameter of the processing equipment 20; the control means 15 is further adapted to restart the operation of the data analysis means 14 in response to the current operating conditions of the processing device 20 returning to normal production, and the data analysis means 14 is further adapted to re-learn and generate new parameter ranges based on the received new state parameters after the restart. That is, after the processing apparatus 20 re-enters the normal production, the control device 15 also controls the data analysis device 14 to restart, and after the data analysis device 14 is started, the parameter learning module 141 re-learns the new state parameter obtained from the data acquisition device 13 as a new sample to obtain a new parameter range to update the algorithm configuration parameter. In other words, the monitoring system 10 provided by the application can automatically update the set parameter range, thereby improving the production efficiency.
In one embodiment, as shown in fig. 4, the data analysis device 14 further includes a quality check module 142, a cleaning module 143, a feature extraction module 144, and a processing mode identification module 145; the quality checking module 142 is connected with the data acquisition device 13, the quality checking module 142, the cleaning module 143 and the feature extraction module 144 are sequentially connected, the feature extraction module 144 is also connected with the parameter learning module 141 and the processing mode identification module 145, and the parameter learning module 141 and the processing mode identification module 145 are respectively connected. The data acquisition device 13 acquires sensor data, i.e. state parameters, to verify the data quality, so as to ensure that the working state of the sensor and the measured data state are normal. The data after the data quality check is subjected to data cleaning pretreatment, wherein the data cleaning comprises but is not limited to filtering, noise reduction, dimension reduction and other data pretreatment means. After the data is cleaned and preprocessed, extracting characteristic parameters of the data, wherein related characteristics are used for self-learning in a sample acquisition stage to generate a parameter range of a normal working state, and the characteristics after the self-learning are used for comparing with the characteristic range generated in the self-learning stage so as to perform pattern recognition.
With continued reference to fig. 1, in an embodiment, the monitoring system 10 further includes an abnormality executing device 16, where the abnormality executing device 16 is respectively connected to the data analyzing device 14 and the control device 15, the abnormality executing device 16 is at least configured to adjust the machining mode of the machining device 20 based on the machining mode of the machining die, and the control device 15 is further configured to suspend operation of the abnormality executing device 16 in response to the current working condition of the machining device 20 being abnormal production. That is, the abnormality execution device 16 may make corresponding feedback, such as controlling the processing apparatus 20 to stop, alarm, etc., based on the result of the data analysis; in addition, since adjustment or modification of some process parameters of the processing apparatus 20 may be involved after the processing apparatus 20 is restored from the abnormal production state to the normal production state, if the processing mode of the processing mold of the processing apparatus 20 is identified by using the process parameters before the processing apparatus 20, the identification may be incorrect, and thus the processing yield of the subsequent statistics may be inaccurate, the data analysis device 14 is controlled to pause operation at this time, and the abnormal execution device 16 is correspondingly fed back based on the analysis result of the data analysis device 14, so that when the data analysis device 14 pauses operation, the operation of the abnormal execution device 16 is unnecessary, and thus when the current working condition of the processing apparatus 20 is abnormal production, the operation of the abnormal execution device 16 is simultaneously controlled to pause.
In one embodiment, the anomaly execution device 16 may control the machine 20 to stop and alert to notify the associated operator of timely handling when it is determined that foreign matter/machining head wear is identified within the machine tool. In other embodiments, the anomaly execution device 16 may also control the robot/manipulator to grasp and sort out the identified product with poor quality, for large-sized products, and control the motor to drive the ball screw to combine with the movable non-acceptable product frame of the guide rail to sort out the small-sized products, and may also control the acceptable product frame to sort out the acceptable products.
In a specific embodiment, the monitoring system 10 further includes a data analysis device 14, the data acquisition device 13 includes a sensor 131, a data acquisition card (not shown in the figure) and an edge industrial personal computer (not shown in the figure), the data acquisition card is respectively connected with the sensor 131 and the edge industrial personal computer, the sensor 131 is disposed on the processing device 20, and the edge industrial personal computer is connected with the data analysis device 14 and the working condition identification device 12; the sensor 131 is configured to convert a state parameter of the processing device 20 during the processing into an analog electrical signal, and send the analog electrical signal to the data acquisition card, where the data acquisition card is configured to convert the analog electrical signal into a digital signal and send the digital signal to the edge industrial personal computer, and the edge industrial personal computer is configured to perform data reduction based on the digital signal to obtain the state parameter of the processing device 20 during the processing. Specifically, the sensor 131 converts the state parameters during processing into analog electrical signals; after the electric signal enters the data acquisition card, the analog electric signal is converted into a digital signal through AD sampling; the digital signals are sent to the edge industrial personal computer through a communication protocol, and a data acquisition program deployed in the edge industrial personal computer restores the digital signals through protocol analysis to obtain state parameters of the processing equipment 20.
In a specific embodiment, the sensor 131 includes at least one of a strain sensor, an acceleration sensor, an acoustic emission sensor, a force sensor, and a displacement sensor. The strain sensor is arranged on a stripper plate of the processing die, the acceleration sensor and the sound emission sensor are arranged on a machine table of the processing equipment 20, the force sensor is arranged on a stand column of the processing equipment 20, and the displacement sensor is arranged between an upper die plate and a lower die plate of the processing die.
The application also provides a stamping monitoring system, which comprises a die identity recognition device, wherein the die identity recognition device at least comprises a tag and a die identity recognition module. The label is used for being arranged on the punching machine, and is used for sending radio frequency signals to the die identity recognition module, wherein the radio frequency signals comprise label information representing the die identity of the punching die on the punching machine; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, receiving tag information sent by all the tags under the corresponding preset working frequencies, and further determining the die identity of the stamping die on the stamping machine based on the tag information sent by all the tags under the corresponding preset working frequencies. That is, the tag is used to be attached to a punch provided with a punch die, so that tag information representing the die identity of the punch die on the punch can be returned; the die identity recognition module can confirm the die identity of the stamping die on the stamping machine based on the label information, so that the die identity of the stamping die on the stamping machine is known, and whether the stamping die on the stamping machine is replaced or not is conveniently determined subsequently. Therefore, through setting up the mould identity recognition device of being connected with the punching machine, can intelligent and accurately discern the mould identity of stamping die on the punching machine, whole journey need not manual operation and intervenes, has realized the intellectuality of processing mould identity monitoring, has saved the human cost.
If the technical scheme of the application relates to personal information, the product applying the technical scheme of the application clearly informs the personal information processing rule before processing the personal information and obtains the autonomous agreement of the individual. If the technical scheme of the application relates to sensitive personal information, the product applying the technical scheme of the application obtains individual consent before processing the sensitive personal information, and simultaneously meets the requirement of 'explicit consent'. For example, a clear and remarkable mark is set at a personal information acquisition device such as a camera to inform that the personal information acquisition range is entered, personal information is acquired, and if the personal voluntarily enters the acquisition range, the personal information is considered as consent to be acquired; or on the device for processing the personal information, under the condition that obvious identification/information is utilized to inform the personal information processing rule, personal authorization is obtained by popup information or a person is requested to upload personal information and the like; the personal information processing rule may include information such as a personal information processor, a personal information processing purpose, a processing mode, and a type of personal information to be processed.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (14)
1. A monitoring system, characterized in that,
the monitoring system comprises a die identity recognition device; the die identity recognition device at least comprises:
a label and a mold identity recognition module;
the tag is arranged on processing equipment and used for sending a radio frequency signal to the die identity recognition module, the radio frequency signal comprises tag information, and the tag information is used for representing the die identity of a processing die on the processing equipment; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, receiving tag information sent by all the tags under the corresponding preset working frequencies, and further determining the die identity of the processing die on the processing equipment based on the tag information sent by all the tags under the corresponding preset working frequencies.
2. The monitoring system of claim 1, wherein the tag information includes a strength of a radio frequency signal transmitted by the tag; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, and comprises the following components: the die identity recognition module is used for obtaining the radio frequency signal intensity of each tag in the tag group under each working frequency and determining the preset working frequency of each tag based on the radio frequency signal intensity of each tag under each working frequency.
3. The monitoring system according to claim 2, wherein the preset operating frequency is an optimal operating frequency, the die identity recognition module comprises an antenna, a signal demodulator and a die identity recognition module, the antenna is used for receiving the radio frequency signal and transmitting the radio frequency signal to the signal demodulator, and the signal demodulator is used for demodulating the radio frequency signal to obtain the tag information; the determining the preset working frequency of each tag based on the radio frequency signal intensity of each tag under each working frequency includes: for each tag, the die identity recognition module is used for taking the working frequency with the maximum radio frequency signal intensity as the optimal working frequency of the tag; each tag in the tag group is a tag in the antenna action area, and tag information of each tag in the tag group is read by the signal demodulator and then sent to the die identity recognition module.
4. The monitoring system of claim 2, wherein the tag information further includes a mold identification number of a corresponding working mold; the determining the mold identity of the processing mold on the processing equipment based on the label information sent by each label at the corresponding preset working frequency comprises the following steps: the die identity recognition module is used for obtaining the reading success rate corresponding to each tag, and taking the die identity identification number corresponding to the strongest radio frequency signal strength and highest reading success rate as the die identity of the processing die.
5. The monitoring system of claim 1, further comprising a condition recognition device and a data acquisition device, wherein the data acquisition device is connected with the processing equipment, the condition recognition device is respectively connected with the data acquisition device and the die identity recognition module, the data acquisition device is used for acquiring state parameters in the processing process of the processing equipment, and the condition recognition device is used for determining the current condition of the processing equipment based on the state parameters in the processing process of the processing equipment and the die identity of a processing die on the processing equipment.
6. The monitoring system of claim 5, wherein the current operating conditions of the processing equipment include abnormal production, including shut down, machine adjustment, and mold change; the working condition identification device is used for determining the current working condition of the processing equipment based on the state parameters in the processing process of the processing equipment and the mold identity of the processing mold on the processing equipment, and comprises the following components: the working condition identification device is used for determining that the current working condition of the processing equipment is die changing in response to the fact that the die identity of the processing die on the processing equipment is different from the historical identity corresponding to the historical die on the processing equipment; responding to the fact that the die identity of a processing die on the processing equipment is the same as the corresponding historical identity of a historical die on the processing equipment, and a new state parameter is not received within a preset time period after the state parameter is received for the last time, and determining that the current working condition of the processing equipment is shutdown; and determining that the current working condition of the processing equipment is a tuning machine in response to the fact that the die identity of the processing die on the processing equipment is the same as the historical identity corresponding to the historical die on the processing equipment and the time interval between the moment when the state parameter is received currently and the moment when the state parameter is received last time is greater than an interval threshold value.
7. The monitoring system of claim 5, wherein the current operating conditions of the processing equipment include normal production; the working condition identification device is used for determining the current working condition of the processing equipment based on the state parameters in the processing process of the processing equipment and the mold identity of the processing mold on the processing equipment, and comprises the following components: and determining that the current working condition of the processing equipment is normal production according to the fact that the time interval between the current time when the state parameter is received and the time when the state parameter is received is equal to an interval threshold value or that a new state parameter is not received within a preset time period after the state parameter is received, and the die identity of the processing die on the processing equipment is the same as the historical identity corresponding to the historical die on the processing equipment.
8. The monitoring system of claim 5, wherein the current operating condition includes abnormal production, the monitoring system further comprising a data analysis device and a control device, the control device being respectively coupled to the operating condition identification device and the data analysis device, the data acquisition device being coupled to the data analysis device, the data analysis device being configured to determine at least a processing mode of the processing tool, the control device being configured to suspend operation of the data analysis device in response to the current operating condition of the processing apparatus being abnormal production.
9. The monitoring system according to claim 8, wherein the data analysis device comprises a parameter learning module, the parameter learning module is connected with the data acquisition device, and the parameter learning module is used for learning and generating a parameter range corresponding to a normal processing mode based on state parameters in the processing process of the processing equipment; the control device is also used for restarting the operation of the data analysis device in response to the recovery of the current working condition of the processing equipment to normal production; the data analysis means are also adapted to re-learn and generate new parameter ranges based on the received new state parameters after a restart.
10. The monitoring system of claim 8, wherein the data analysis device comprises a quality check module, a cleaning module, a feature extraction module, a parameter learning module, and a processing pattern recognition module; the quality checking module is connected with the data acquisition device, the quality checking module, the cleaning module and the feature extraction module are sequentially connected, the feature extraction module is also respectively connected with the parameter learning module and the processing mode identification module, and the parameter learning module is connected with the processing mode identification module.
11. The monitoring system according to claim 8, further comprising an abnormality execution device connected to the data analysis device and the control device, respectively, the abnormality execution device being at least for adjusting the processing mode of the processing apparatus based on the processing mode of the processing die; the control device is also used for responding to the current working condition of the processing equipment to be abnormal production and suspending the operation of the abnormal execution device.
12. The monitoring system of claim 5, further comprising a data analysis device; the data acquisition device comprises a sensor, a data acquisition card and an edge industrial personal computer, the data acquisition card is respectively connected with the sensor and the edge industrial personal computer, the sensor is arranged on the processing equipment, and the edge industrial personal computer is connected with the data analysis device and the working condition identification device; the sensor is used for converting a state parameter in the processing process of the processing equipment into an analog electric signal and sending the analog electric signal to the data acquisition card, the data acquisition card is used for converting the analog electric signal into a digital signal and sending the digital signal to the edge industrial personal computer, and the edge industrial personal computer is used for carrying out data reduction based on the digital signal to obtain the state parameter.
13. The monitoring system of claim 12, wherein the sensor comprises at least one of a strain sensor, an acceleration sensor, an acoustic emission sensor, a force sensor, and a displacement sensor; the strain sensor is arranged on a stripper plate of the processing die, the acceleration sensor and the acoustic emission sensor are arranged on a machine table of the processing equipment, the force sensor is arranged on a stand column of the processing equipment, and the displacement sensor is arranged between an upper template and a lower template of the processing die.
14. A punch monitoring system is characterized in that,
the stamping monitoring system comprises a die identity recognition device; the die identity recognition device at least comprises:
a label and a mold identity recognition module;
the label is arranged on the punching machine and used for sending radio frequency signals to the die identity recognition module, the radio frequency signals comprise label information, and the label information is used for representing the die identity of the punching die on the punching machine; the die identity recognition module is used for determining preset working frequencies of all the tags in the tag group including the tags, receiving tag information sent by all the tags under the corresponding preset working frequencies, and further determining the die identity of the stamping die on the stamping machine based on the tag information sent by all the tags under the corresponding preset working frequencies.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311122970.4A CN117798218A (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
| CN202310838463.4A CN116571592B (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310838463.4A CN116571592B (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311122970.4A Division CN117798218A (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116571592A true CN116571592A (en) | 2023-08-11 |
| CN116571592B CN116571592B (en) | 2023-10-27 |
Family
ID=87536158
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311122970.4A Pending CN117798218A (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
| CN202310838463.4A Active CN116571592B (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311122970.4A Pending CN117798218A (en) | 2023-07-10 | 2023-07-10 | Monitoring system and punching monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN117798218A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002355872A (en) * | 2001-05-31 | 2002-12-10 | Asahi Sangyo Kaisha Ltd | Injection molding apparatus |
| CN101101633A (en) * | 2007-07-25 | 2008-01-09 | 中兴通讯股份有限公司 | Electronic tag scanning method and system |
| CN101983117A (en) * | 2008-02-28 | 2011-03-02 | 安大略模具国际公司 | Systems and methods for real-time monitoring of die use or yield |
| US20110132533A1 (en) * | 2008-08-06 | 2011-06-09 | Koegl Martin | Form-liner having improved identification capability |
| CN202715731U (en) * | 2012-07-04 | 2013-02-06 | 金丰机器工业股份有限公司 | Mold identification device of punching machine |
| CN103235555A (en) * | 2013-04-18 | 2013-08-07 | 重庆大学 | On-line machine tool equipment utilization state monitoring method and device based on power information |
| CN103760820A (en) * | 2014-02-15 | 2014-04-30 | 华中科技大学 | Evaluation device of state information of machining process of numerical control milling machine |
| GB201805870D0 (en) * | 2018-04-09 | 2018-05-23 | Pcl Ceramics Ltd | A moulding system, apparatus and method |
| CN110569932A (en) * | 2019-08-27 | 2019-12-13 | 广汽乘用车(杭州)有限公司 | RFID inventory management system |
| CN115034274A (en) * | 2022-08-10 | 2022-09-09 | 深圳市信润富联数字科技有限公司 | Mold history management method, system, device, equipment and readable storage medium |
| CN115358249A (en) * | 2022-08-23 | 2022-11-18 | 南瑞集团有限公司 | Active RFID system frequency band adaptation method and device |
| CN115352116A (en) * | 2022-10-20 | 2022-11-18 | 深圳市信润富联数字科技有限公司 | Automatic die change method, system, device, equipment and readable storage medium |
-
2023
- 2023-07-10 CN CN202311122970.4A patent/CN117798218A/en active Pending
- 2023-07-10 CN CN202310838463.4A patent/CN116571592B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002355872A (en) * | 2001-05-31 | 2002-12-10 | Asahi Sangyo Kaisha Ltd | Injection molding apparatus |
| CN101101633A (en) * | 2007-07-25 | 2008-01-09 | 中兴通讯股份有限公司 | Electronic tag scanning method and system |
| CN101983117A (en) * | 2008-02-28 | 2011-03-02 | 安大略模具国际公司 | Systems and methods for real-time monitoring of die use or yield |
| US20110132533A1 (en) * | 2008-08-06 | 2011-06-09 | Koegl Martin | Form-liner having improved identification capability |
| CN202715731U (en) * | 2012-07-04 | 2013-02-06 | 金丰机器工业股份有限公司 | Mold identification device of punching machine |
| CN103235555A (en) * | 2013-04-18 | 2013-08-07 | 重庆大学 | On-line machine tool equipment utilization state monitoring method and device based on power information |
| CN103760820A (en) * | 2014-02-15 | 2014-04-30 | 华中科技大学 | Evaluation device of state information of machining process of numerical control milling machine |
| GB201805870D0 (en) * | 2018-04-09 | 2018-05-23 | Pcl Ceramics Ltd | A moulding system, apparatus and method |
| CN110569932A (en) * | 2019-08-27 | 2019-12-13 | 广汽乘用车(杭州)有限公司 | RFID inventory management system |
| CN115034274A (en) * | 2022-08-10 | 2022-09-09 | 深圳市信润富联数字科技有限公司 | Mold history management method, system, device, equipment and readable storage medium |
| CN115358249A (en) * | 2022-08-23 | 2022-11-18 | 南瑞集团有限公司 | Active RFID system frequency band adaptation method and device |
| CN115352116A (en) * | 2022-10-20 | 2022-11-18 | 深圳市信润富联数字科技有限公司 | Automatic die change method, system, device, equipment and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117798218A (en) | 2024-04-02 |
| CN116571592B (en) | 2023-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108620950B (en) | Method and system for monitoring machining state of turning tool | |
| CN110252851B (en) | Abnormality detection method and abnormality detection device in press working | |
| CN103760820A (en) | Evaluation device of state information of machining process of numerical control milling machine | |
| CN107756139A (en) | Tool state detection system and method | |
| US20180299944A1 (en) | Production Management Method and System Using Power Consumption Features | |
| CN110308352A (en) | A kind of intelligence probe test platform and its test method | |
| CN109141625B (en) | Online state monitoring method for ball screw pair | |
| CN113341879A (en) | Data acquisition and monitoring method and system for numerical control machine tool | |
| CN116571592B (en) | Monitoring system and punching monitoring system | |
| CN115903669A (en) | High-stability intelligent machine manufacturing system | |
| CN109598309B (en) | Detection system and monitoring method of metal packaging punching machine | |
| CN114925752A (en) | Method for monitoring abnormal cutting of plate of linear cutting machine based on Internet of things | |
| CN111766836A (en) | Smart manufacturing method and system | |
| CN213580097U (en) | Vehicle offline detection system | |
| CN110456732B (en) | Punch press fault monitoring system with learning function | |
| CN212633982U (en) | Digital metal stamping device | |
| WO2021145156A1 (en) | Abnormality diagnosis system and abnormality diagnosis method | |
| CN116050781A (en) | Enterprise intelligent management system based on industrial Internet | |
| CN112509170A (en) | Method for collecting mold production field data | |
| CN112547970A (en) | Molding simulation system and method for production of washing machine inner barrel stamping part | |
| CN110176133A (en) | A kind of machine tool data acquisition method | |
| CN111883461B (en) | Flexible surface mounting equipment and method for manufacturing IGBT power assembly | |
| CN114074141A (en) | Digital metal stamping device and method | |
| CN111694310A (en) | Intelligent data acquisition and analysis system and method thereof | |
| CN113659709B (en) | Switching station operation monitoring system and method based on voice recognition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |