CN118938786B - Industrial automation equipment control system based on intelligent network - Google Patents
Industrial automation equipment control system based on intelligent network Download PDFInfo
- Publication number
- CN118938786B CN118938786B CN202411425782.3A CN202411425782A CN118938786B CN 118938786 B CN118938786 B CN 118938786B CN 202411425782 A CN202411425782 A CN 202411425782A CN 118938786 B CN118938786 B CN 118938786B
- Authority
- CN
- China
- Prior art keywords
- value
- machine tool
- tool
- preset
- wear
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
-
- 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]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Numerical Control (AREA)
Abstract
The invention belongs to the technical field of automatic control, and particularly relates to an industrial automation equipment control system based on an intelligent network, wherein the system comprises a processor, a machine tool state monitoring module, a self-adaptive control module, a sustainability evaluation module and an early warning terminal; according to the invention, the pre-processing analysis module analyzes based on the clamping judgment symbol and the cutter judgment symbol, the numerical control machine tool processes the workpiece when the qualified signal is generated, the processing stability and the processing effect of the numerical control machine tool are ensured, the working state data of the numerical control machine tool is collected in real time through the machine tool state monitoring module in the processing process, the self-adaptive control module optimizes the processing process of the numerical control machine tool based on the working state data, and the operation sustainability analysis is carried out on the numerical control machine tool through the sustainability evaluation module, so that the numerical control machine tool is timely rest when the low sustainability signal is generated, the quality and the efficiency of a processed product are further ensured, and the intelligent and automatic level is high.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to an industrial automation equipment control system based on an intelligent network.
Background
The industrial automation equipment control system of the intelligent network is an essential component in modern industrial production. The system deeply merges the technologies of computer, communication, control and artificial intelligence, and realizes the automation, the intellectualization and the informatization of the production process by constructing a high-efficiency and intelligent network system.
The numerical control machine is a specific application of an industrial automation equipment control system in the field of machine tool machining, is called a numerical control machine, processes the machine tool by utilizing a numerical control technology, combines traditional mechanical processing with modern computer technology, and controls the movement and the machining process of the machine tool through a preset program, thereby realizing high-precision workpiece machining.
At present, when the operation supervision of the numerical control machine tool is carried out, the preparation condition is difficult to reasonably judge before the numerical control machine tool processes a workpiece, the processing process is automatically optimized in the processing process, the risk of continuous operation of the numerical control machine tool cannot be accurately evaluated, and the operation safety of the numerical control machine tool and the processing quality are not guaranteed;
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an industrial automation equipment control system based on an intelligent network, which solves the problems that the prior art is difficult to reasonably judge the preparation condition before a numerical control machine tool processes a workpiece and automatically optimize the processing process in the processing process, and the risk of continuous operation of the numerical control machine tool cannot be accurately evaluated, so that the operation safety of the numerical control machine tool is not guaranteed, and the processing quality is improved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
An industrial automation equipment control system based on an intelligent network is used for processing control of a numerical control machine tool and comprises a processor, a machine tool state monitoring module, an adaptive control module, a sustainability evaluation module and an early warning terminal;
the machine tool state monitoring module integrates various sensors, collects working state data of the numerical control machine tool in real time, and sends the working state data of the numerical control machine tool to the self-adaptive control module through the processor;
The self-adaptive control module processes and analyzes working state data of the numerical control machine tool by utilizing a deep learning algorithm, predicts future working states or machining effects of the numerical control machine tool, automatically adjusts control parameters of the numerical control machine tool based on a prediction result, and optimizes the machining process of the numerical control machine tool;
The sustainability evaluation module analyzes the operation sustainability of the numerical control machine tool, generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis, sends the high sustainability signal or the low sustainability signal to the early warning terminal through the processor, and sends out corresponding early warning when the early warning terminal receives the low sustainability signal.
Further, the processor is in communication connection with a pre-processing analysis module, before processing the corresponding workpiece, the pre-processing analysis module acquires a clamping judgment symbol JP-1 or JP-2 and a cutter judgment symbol DP-1 or DP-2, performs intersection analysis on the clamping judgment symbol and the cutter judgment symbol, generates a preparation qualified signal if JP-2U DP-2 is acquired, and generates a preparation abnormal signal if the rest conditions are the same;
when the preparation qualified signal is generated, the processor enables the numerical control machine tool to process the workpiece, when the preparation abnormal signal is generated, the preparation abnormal signal is sent to the early warning terminal through the processor, and the early warning terminal sends early warning when the processing abnormal signal is received.
Further, the pre-processing analysis module is in communication connection with the workpiece clamping analysis module and the cutter detection and evaluation module, the workpiece clamping analysis module acquires a clamp on the numerical control machine tool, analyzes the clamping performance of the clamp on the corresponding workpiece, gives a clamping judgment symbol JP-1 or JP-2 through analysis, and sends the clamping judgment symbol JP-1 or JP-2 to the pre-processing analysis module;
The tool detection and evaluation module acquires a tool for processing operation on the numerical control machine tool, performs visual scanning on the tool, analyzes the condition of the tool based on the visual scanning image of the tool, gives a tool judgment symbol DP-1 or DP-2 through analysis, and sends the tool judgment symbol DP-1 or DP-2 to the pre-processing analysis module.
Further, the specific analysis process of the workpiece clamping analysis module is as follows:
When the clamp clamps the corresponding workpiece, a contact position of the clamp and the corresponding workpiece is obtained, a plurality of detection points are set on the contact position of the clamp, pressure applied to the corresponding workpiece by the corresponding detection points is collected and marked as compression data, the compression data are compared with a preset compression data range in numerical value, if the compression data are not in the preset compression data range, the corresponding detection points are marked as abnormal points, and if the abnormal points do not exist, a clamping judgment symbol JP-2 is given.
Further, if the pressure difference points exist, the number of the pressure difference points is collected, the ratio of the number of the pressure difference points to the total number of the detection points is calculated to obtain pressure difference detection condition values, deviation values of the compression data of the corresponding pressure difference points compared with a preset compression data range are marked as pressure difference amplitude measurement values, average value calculation is carried out on all the pressure difference amplitude measurement values to obtain pressure difference amplitude table values, and the pressure difference amplitude measurement value with the largest value is marked as pressure difference amplitude risk value;
the clamp state value is obtained by carrying out numerical calculation on the pressure difference detection condition value, the pressure difference amplitude table value and the pressure difference amplitude risk value, the clamp state value is compared with a preset clamp state threshold value in a numerical mode, if the clamp state value exceeds the preset clamp state threshold value, a clamp judgment symbol JP-1 is given, and if the clamp state value does not exceed the preset clamp state threshold value, a clamp judgment symbol JP-2 is given.
Further, the specific analysis process of the tool detection and evaluation module is as follows:
the method comprises the steps of capturing a wear area on a cutter based on a visual scanning image of the cutter, collecting the area of the corresponding wear area, marking the wear surface detection value of the corresponding wear area, collecting the average wear amount of the corresponding wear area, marking the wear detection value of the corresponding wear area, carrying out numerical calculation on the wear surface detection value and the wear detection value to obtain a wear risk table value, carrying out numerical comparison on the wear risk table value and a preset wear risk table threshold value, marking the corresponding wear area as a high risk area if the wear risk table value exceeds the preset wear risk table threshold value, and giving a cutter judgment symbol DP-1 if the high risk area exists on the cutter.
Further, if no high-risk area exists on the cutter, carrying out average calculation on the wear risk table values of all the wear areas to obtain a wear comprehensive detection value, marking the number of the wear areas as a wear numerical detection value, and marking the wear risk table value with the largest numerical value as a wear amplitude detection value;
The method comprises the steps of carrying out numerical calculation on a comprehensive abrasion detection value, a numerical abrasion detection value and a numerical abrasion detection value to obtain a cutter state value, carrying out numerical comparison on the cutter state value and a preset cutter state threshold value, giving a cutter judgment symbol DP-1 if the cutter state value exceeds the preset cutter state threshold value, and giving a cutter judgment symbol DP-2 if the cutter state value does not exceed the preset cutter state threshold value.
Further, the specific analysis procedure for the operation sustainability analysis is as follows:
acquiring the starting time of the current running of the numerical control machine tool, and marking the interval time between the current time and the starting time as the starting duration;
The processing time length of the corresponding workpiece is acquired, the processing time length is compared with a corresponding preset processing time length threshold value, if the processing time length exceeds the corresponding preset processing time length threshold value, the corresponding processing time length is marked as a high delay time length, the number of the high delay time length in the starting time length is acquired, and the ratio of the number of the high delay time length to the total number of the processed workpieces in the starting time length is calculated to obtain a delay processing numerical condition value;
the number of the scrapped workpieces after being processed by the numerical control machine tool in the starting duration is obtained, and the ratio of the number of the scrapped workpieces to the total number of the processed workpieces in the starting duration is calculated to obtain a scrapped processing numerical condition value;
The method comprises the steps of carrying out numerical calculation on a starting duration, a slow processing condition value and a scrapping processing condition value to obtain a sustainable detection value, carrying out numerical comparison on the sustainable detection value and a preset sustainable detection threshold value, generating a low sustainable signal if the sustainable detection value exceeds the preset sustainable detection threshold value, and generating a high sustainable signal if the sustainable detection value does not exceed the preset sustainable detection threshold value.
Further, the processor is in communication connection with the machine tool maintenance decision module, and before the numerical control machine tool is ready to be started, the machine tool maintenance decision module judges the maintenance urgency of the numerical control machine tool through analysis, and the specific analysis process is as follows:
acquiring the total operation time length of the numerical control machine tool in a historical stage, acquiring the occurrence times of the single continuous operation time length of the numerical control machine tool in the historical stage exceeding a preset single continuous operation time length threshold value, marking the occurrence times as risk operation frequency, and marking the occurrence times of the maintenance interval time length exceeding the preset maintenance interval time length threshold value in the historical stage as high interval dimension frequency values;
Numerical calculation is carried out on the running total duration, the risk running frequency and the high interval dimension frequency value to obtain a numerical control machine tool detection value, a plurality of groups of preset numerical control machine tool detection value ranges are preset, and each group of preset numerical control machine tool detection value ranges corresponds to a group of preset maintenance interval duration threshold values respectively;
The method comprises the steps of collecting the last maintenance time of the numerical control machine tool, calculating the time difference between the current time and the last maintenance time to obtain the maintenance adjacent interval time, comparing the maintenance adjacent interval time with a target threshold range in a numerical value mode, generating an emergency maintenance signal if the maintenance adjacent interval time exceeds the target threshold range, sending the emergency maintenance signal to an early warning terminal through a processor, and sending corresponding early warning when the early warning terminal receives the emergency maintenance signal.
Furthermore, the invention also provides an industrial automation equipment control method based on the intelligent network, which comprises the following steps:
the method comprises the steps that firstly, before machining corresponding workpieces, a pre-machining analysis module analyzes based on clamping judgment symbols and cutter judgment symbols, and generates a preparation qualified signal or a preparation abnormal signal through analysis;
step two, when a preparation qualified signal is generated, the numerical control machine tool processes a workpiece, and a machine tool state monitoring module collects working state data of the numerical control machine tool in real time;
step three, the self-adaptive control module processes and analyzes the working state data of the numerical control machine by using a deep learning algorithm, automatically adjusts control parameters of the numerical control machine, and optimizes the machining process of the numerical control machine;
Step four, the sustainability evaluation module carries out operation sustainability analysis on the numerical control machine tool, and generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis;
and fifthly, when the preparation abnormal signal or the low sustainability signal is generated, the early warning terminal sends out early warning.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, the pre-processing analysis module analyzes based on the clamping judgment symbol and the cutter judgment symbol, so that the numerical control machine tool processes a workpiece when a preparation qualified signal is generated, the processing stability and the processing effect of the numerical control machine tool are ensured, the processing process of the numerical control machine tool is analyzed and optimized based on the working state data through the self-adaptive control module in the processing process, the operation sustainability analysis is performed on the numerical control machine tool through the sustainability evaluation module, and the numerical control machine tool is timely settled when a low sustainability signal is generated, so that the quality and the efficiency of a processed product are further ensured;
2. In the invention, the maintenance urgency of the numerical control machine tool is judged before the numerical control machine tool is ready to be started by the machine tool maintenance decision module, and the early warning terminal is enabled to send out early warning when the emergency maintenance signal is generated, so that an operator is reminded to temporarily not start the numerical control machine tool and maintain and overhaul the numerical control machine tool, thereby ensuring the safe and stable operation of the numerical control machine tool and having high level of intellectualization and automation.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
FIG. 2 is a system block diagram of a second embodiment of the present invention;
fig. 3 is a flow chart of a method according to a third embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an industrial automation equipment control system based on an intelligent network, which is shown in fig. 1 and is used for processing control of a numerical control machine tool, and comprises a processor, a pre-processing analysis module, a machine tool state monitoring module, a self-adaptive control module, a sustainability evaluation module and an early warning terminal;
Before processing corresponding workpieces, a pre-processing analysis module acquires a clamping judgment symbol JP-1 or JP-2 and a cutter judgment symbol DP-1 or DP-2, performs intersection analysis on the clamping judgment symbol and the cutter judgment symbol, generates a preparation qualified signal if JP-2U DP-2 is acquired, and generates a preparation abnormal signal if the rest conditions are met;
when the preparation qualified signal is generated, the processor enables the numerical control machine tool to process the workpiece, when the preparation abnormal signal is generated, the preparation abnormal signal is sent to the early warning terminal through the processor, and the early warning terminal sends out early warning when receiving the processing abnormal signal so as to remind an operator to adjust the clamp or replace the cutter, so that the processing stability and the processing effect of the numerical control machine tool on the workpiece are ensured.
Furthermore, the pre-processing analysis module is in communication connection with the workpiece clamping analysis module and the cutter detection and evaluation module, the workpiece clamping analysis module acquires a clamp on the numerical control machine tool, analyzes the clamping performance of the clamp on the corresponding workpiece, gives a clamping judgment symbol JP-1 or JP-2 through analysis, sends the clamping judgment symbol JP-1 or JP-2 to the pre-processing analysis module, can accurately feed back the clamping performance of the clamp on the workpiece, provides information support for the analysis process of the pre-processing analysis module, and ensures the accuracy of the analysis result, and the specific analysis process of the workpiece clamping analysis module is as follows:
When the clamp clamps the corresponding workpiece, a contact position of the clamp and the corresponding workpiece is obtained, a plurality of detection points are set on the contact position of the clamp, the pressure applied to the corresponding workpiece by the corresponding detection points is collected and marked as compression data, and the compression data are compared with a preset compression data range in a numerical mode;
if the compaction data is not in the preset compaction data range, the corresponding detection point is marked as a compaction point if the pressure performance of the corresponding detection point is not in accordance with the requirement, and if the compaction point does not exist, the clamp is indicated to have better clamping performance on the corresponding workpiece, a clamping judgment symbol JP-2 is given;
If the pressure abnormal points exist, acquiring the number of the pressure abnormal points, calculating the ratio of the number of the pressure abnormal points to the total number of the detection points to obtain pressure abnormal detection condition values, marking deviation values of the compression data of the corresponding pressure abnormal points compared with a preset compression data range as pressure abnormal measurement values, carrying out mean value calculation on all the pressure abnormal measurement values to obtain pressure abnormal table values, and marking the pressure abnormal measurement value with the largest value as pressure abnormal risk value;
Carrying out numerical calculation on the differential pressure detection condition value FY, the differential pressure table value FN and the differential pressure risk value FS through a formula FX=c1×FY+ (c2×FN+c3×FS)/2 to obtain a clamp state value FX, wherein c1, c2 and c3 are preset proportionality coefficients, and c1> c2> c3>0;
And (3) comparing the clamp state value FX with a preset clamp state threshold value, if the clamp state value FX exceeds the preset clamp state threshold value, indicating that the clamp of the numerical control machine tool is poor in clamping performance synthesis of the corresponding workpiece, assigning a clamping judgment symbol JP-1, and if the clamp state value FX does not exceed the preset clamp state threshold value, indicating that the clamp of the numerical control machine tool is good in clamping performance synthesis of the corresponding workpiece, assigning a clamping judgment symbol JP-2.
The cutter detection evaluation module acquires a cutter used for processing operation on the numerical control machine tool, performs visual scanning on the cutter, analyzes the condition of the cutter based on the visual scanning image of the cutter (extracts a wear area through an image processing technology and performs wear measurement), gives a cutter judgment symbol DP-1 or DP-2 through analysis, and sends the cutter judgment symbol DP-1 or DP-2 to the pre-processing analysis module, so that the quality condition of the cutter can be accurately fed back, information support is provided for the analysis process of the pre-processing analysis module, and the accuracy of the analysis result is further ensured, wherein the specific analysis process of the cutter detection evaluation module is as follows:
Capturing the abrasion areas on the cutter based on the visual scanning image of the cutter, acquiring the areas of the corresponding abrasion areas and marking the abrasion surface detection values of the abrasion areas, and acquiring the average abrasion amounts of the corresponding abrasion areas and marking the abrasion detection values of the abrasion areas;
carrying out numerical calculation on the wear surface detection value MY and the wear detection value ML through a formula MS=wq1+wq2×ML to obtain a wear risk table value MS, wherein wq1 and wq2 are preset proportional coefficients with values larger than zero, and the larger the numerical value of the wear risk table value MS is, the larger the adverse effect of a corresponding wear area on the quality of a cutter is shown;
Comparing the abrasion risk table value MS with a preset abrasion risk table threshold value, and if the abrasion risk table value MS exceeds the preset abrasion risk table threshold value, indicating that the corresponding abrasion area has larger adverse effect on the quality of the cutter, marking the corresponding abrasion area as a high risk area;
If the high-risk area does not exist on the cutter, carrying out average calculation on the wear risk table values of all the wear areas to obtain a wear comprehensive detection value, marking the number of the wear areas as a wear numerical detection value, and marking the wear risk table value with the largest numerical value as a wear amplitude detection value;
Carrying out numerical calculation on the abrasion comprehensive detection value QW, the abrasion numerical detection value QR and the abrasion amplitude detection value QK through a formula QY=up2+QR+ (up1+up3+QK)/2 to obtain a cutter state value QY, wherein up1, up2 and up3 are preset proportional coefficients with values larger than zero, and the larger the numerical value of the cutter state value QY is, the worse the cutter quality performance of the numerical control machine tool is comprehensively indicated;
And (3) comparing the cutter state value QY with a preset cutter state threshold value, if the cutter state value QY exceeds the preset cutter state threshold value, indicating that the cutter quality of the numerical control machine tool is poor in comprehensive performance, giving a cutter judgment symbol DP-1, and if the cutter state value QY does not exceed the preset cutter state threshold value, indicating that the cutter quality of the numerical control machine tool is good in comprehensive performance, giving a cutter judgment symbol DP-2.
The machine tool state monitoring module integrates various sensors, collects working state data (including spindle load, cutter temperature and the like) of the numerical control machine tool in real time, and sends the working state data of the numerical control machine tool to the self-adaptive control module through the processor;
the self-adaptive control module processes and analyzes working state data of the numerical control machine by using a deep learning algorithm (namely a deep learning model such as a convolutional neural network, a cyclic neural network, a long-short-term memory network and the like), predicts the future working state or processing effect of the numerical control machine, automatically adjusts control parameters of the numerical control machine such as feed rate, cutting force and the like based on the prediction result so as to optimize the processing process of the numerical control machine, remarkably improve the processing precision and the production efficiency, and has high level of intellectualization and automation.
The method comprises the steps that a sustainability evaluation module carries out operation sustainability analysis on a numerical control machine tool, a high sustainability signal or a low sustainability signal is generated through the operation sustainability analysis, the high sustainability signal or the low sustainability signal is sent to an early warning terminal through a processor, the early warning terminal sends out corresponding early warning when receiving the low sustainability signal, so that an operator is reminded to pause the operation of the numerical control machine tool according to the requirement, the numerical control machine tool can be reset in time, the subsequent operation potential safety hazards are avoided, the quality and the efficiency of processed products are guaranteed, and the specific analysis process of the operation sustainability analysis is as follows:
acquiring the starting time of the current running of the numerical control machine tool, and marking the interval time between the current time and the starting time as the starting duration;
The processing time length of the corresponding workpiece is acquired, the processing time length is compared with a corresponding preset processing time length threshold value, if the processing time length exceeds the corresponding preset processing time length threshold value, the corresponding processing time length is marked as a high delay time length, the number of the high delay time length in the starting time length is acquired, and the ratio of the number of the high delay time length to the total number of the processed workpieces in the starting time length is calculated to obtain a delay processing numerical condition value;
the number of the scrapped workpieces after being processed by the numerical control machine tool in the starting duration is obtained, and the ratio of the number of the scrapped workpieces to the total number of the processed workpieces in the starting duration is calculated to obtain a scrapped processing numerical condition value;
By the formula Carrying out numerical calculation on the starting duration TS, the slow processing digital value TX and the scrapped processing digital value TF to obtain a sustainable detection value TP, wherein hy1, hy2 and hy3 are preset proportionality coefficients, hy3> hy2> hy1>0, and the larger the value of the sustainable detection value TP is, the larger the potential safety hazard that the numerical control machine continues to operate is indicated;
And comparing the sustainable detection value TP with a preset sustainable detection threshold value, if the sustainable detection value TP exceeds the preset sustainable detection threshold value, indicating that the potential safety hazard of the numerical control machine tool to continue to operate is large, generating a low sustainable signal, and if the sustainable detection value TP does not exceed the preset sustainable detection threshold value, indicating that the potential safety hazard of the numerical control machine tool to continue to operate is small, generating a high sustainable signal.
In the second embodiment, as shown in fig. 2, the difference between the first embodiment and the second embodiment is that the processor is communicatively connected to the machine tool maintenance decision module, and before the numerical control machine tool is ready to be started, the machine tool maintenance decision module determines the urgency of maintenance of the numerical control machine tool by analysis, and the specific analysis process is as follows:
acquiring the total operation time length of the numerical control machine tool in a historical stage, acquiring the occurrence times of the single continuous operation time length of the numerical control machine tool in the historical stage exceeding a preset single continuous operation time length threshold value, marking the occurrence times as risk operation frequency, and marking the occurrence times of the maintenance interval time length exceeding the preset maintenance interval time length threshold value in the historical stage as high interval dimension frequency values;
Numerical calculation is carried out on the running total duration LZ, the risk running frequency LK and the high interval dimension frequency LW through a formula LF=e1×LZ/(e2+e3) +e2×LK+e3×LW to obtain a numerical control machine tool detection value LF, wherein e1, e2 and e3 are preset proportionality coefficients, and e3> e2> e1>0;
presetting a plurality of groups of preset numerical control machine tool detection value ranges, wherein each group of preset numerical control machine tool detection value ranges corresponds to a group of preset maintenance adjacent interval time threshold values respectively; the numerical control machine tool detection value LF is compared with all the preset numerical control machine tool detection value ranges one by one, and the preset numerical control machine tool detection value range containing the corresponding numerical control machine tool detection value LF is marked as a target threshold range;
The method comprises the steps of collecting the last adjacent maintenance time for the maintenance of the numerical control machine, calculating the time difference between the current time and the last adjacent maintenance time to obtain the maintenance adjacent interval time, comparing the maintenance adjacent interval time with a target threshold range in a numerical value mode, generating an emergency maintenance signal if the maintenance adjacent interval time exceeds the target threshold range, sending the emergency maintenance signal to an early warning terminal through a processor, sending a corresponding early warning when the early warning terminal receives the emergency maintenance signal, reminding an operator to start the numerical control machine temporarily, and maintaining and overhauling the numerical control machine, so that safe and stable operation of the numerical control machine is ensured.
In the third embodiment, as shown in fig. 3, the difference between the present embodiment and the first and second embodiments is that the method for controlling the industrial automation device based on the intelligent network according to the present invention includes the following steps:
the method comprises the steps that firstly, before machining corresponding workpieces, a pre-machining analysis module analyzes based on clamping judgment symbols and cutter judgment symbols, and generates a preparation qualified signal or a preparation abnormal signal through analysis;
step two, when a preparation qualified signal is generated, the numerical control machine tool processes a workpiece, and a machine tool state monitoring module collects working state data of the numerical control machine tool in real time;
step three, the self-adaptive control module processes and analyzes the working state data of the numerical control machine by using a deep learning algorithm, automatically adjusts control parameters of the numerical control machine, and optimizes the machining process of the numerical control machine;
Step four, the sustainability evaluation module carries out operation sustainability analysis on the numerical control machine tool, and generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis;
and fifthly, when the preparation abnormal signal or the low sustainability signal is generated, the early warning terminal sends out early warning.
The working principle of the invention is that before processing corresponding workpieces, the processing front analysis module analyzes based on the clamping judgment symbol and the cutter judgment symbol to generate a ready signal or a ready abnormal signal, the numerical control machine is used for processing the workpieces when generating the ready signal, an operator is reminded to adjust a clamp or replace a cutter when generating the ready abnormal signal, the processing stability and the processing effect of the numerical control machine are ensured, the working state data of the numerical control machine are collected in real time through the machine state monitoring module in the processing process, the self-adaptive control module optimizes the processing process of the numerical control machine based on the working state data, the processing precision and the production efficiency are remarkably improved, the operation sustainability analysis is carried out on the numerical control machine through the sustainability evaluation module, the high sustainability signal or the low sustainability signal is generated, the numerical control machine is timely rest when generating the low sustainability signal, the subsequent operation safety hidden trouble is avoided, the quality and the efficiency of the processed products are further ensured, and the intelligent and automatic level is high.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411425782.3A CN118938786B (en) | 2024-10-14 | 2024-10-14 | Industrial automation equipment control system based on intelligent network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411425782.3A CN118938786B (en) | 2024-10-14 | 2024-10-14 | Industrial automation equipment control system based on intelligent network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118938786A CN118938786A (en) | 2024-11-12 |
| CN118938786B true CN118938786B (en) | 2025-02-07 |
Family
ID=93360391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411425782.3A Active CN118938786B (en) | 2024-10-14 | 2024-10-14 | Industrial automation equipment control system based on intelligent network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118938786B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106141811A (en) * | 2016-06-15 | 2016-11-23 | 武汉范特西网络科技有限公司 | Wireless mechanical working condition maintenance device |
| CN117406666A (en) * | 2023-11-17 | 2024-01-16 | 苏州工业职业技术学院 | An automated CNC machine tool control system |
| CN118278775A (en) * | 2024-05-17 | 2024-07-02 | 天开(东莞)隧道工程设备有限公司 | A fault diagnosis system for shield machine blade processing |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116040487B (en) * | 2023-03-06 | 2023-06-16 | 中国电建集团山东电力建设第一工程有限公司 | Hoisting equipment operation safety supervision system based on big data |
| CN118244044B (en) * | 2024-05-27 | 2024-08-23 | 深圳市今朝时代股份有限公司 | Failure detection method applied to super capacitor module of automobile |
-
2024
- 2024-10-14 CN CN202411425782.3A patent/CN118938786B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106141811A (en) * | 2016-06-15 | 2016-11-23 | 武汉范特西网络科技有限公司 | Wireless mechanical working condition maintenance device |
| CN117406666A (en) * | 2023-11-17 | 2024-01-16 | 苏州工业职业技术学院 | An automated CNC machine tool control system |
| CN118278775A (en) * | 2024-05-17 | 2024-07-02 | 天开(东莞)隧道工程设备有限公司 | A fault diagnosis system for shield machine blade processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118938786A (en) | 2024-11-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107738140B (en) | Method and system for monitoring state of cutter and processing equipment | |
| CN113741377A (en) | Machining process intelligent monitoring system and method based on cutting characteristic selection | |
| Bombiński et al. | Sensor signal segmentation for tool condition monitoring | |
| Tangjitsitcharoen et al. | Development of chatter detection in milling processes | |
| CN102262401A (en) | Industrial production line monitoring system | |
| CN103760820A (en) | Evaluation device of state information of machining process of numerical control milling machine | |
| KR20210065581A (en) | System and method for monitoring of machine tool abnormality | |
| Huang et al. | Online workpiece height estimation for reciprocated traveling wire EDM based on support vector machine | |
| Tiwari et al. | Tool wear prediction in end milling of Ti-6Al-4V through Kalman filter based fusion of texture features and cutting forces | |
| CN116560327A (en) | Electric tool production control method and system based on Internet of things | |
| TWM575368U (en) | Cloud computing system for intelligent machine tools | |
| CN114425700A (en) | Intelligent machine tool machining operation automatic identification control system based on machine vision | |
| CN118938786B (en) | Industrial automation equipment control system based on intelligent network | |
| US20240019838A1 (en) | Composite tool cutting monitoring system and monitoring method thereof | |
| CN116909211B (en) | Intelligent regulation and control method and system for high-precision numerical control machine tool | |
| Li et al. | Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes | |
| CN118672206A (en) | Alarm linkage method and system in product production process | |
| CN117313951B (en) | Multi-sensor fusion flexible deburring management method and system | |
| Danai | Machine tool monitoring and control | |
| CN117359393A (en) | Multi-signal cutter abrasion monitoring and controlling system in numerical control machining process | |
| Wan et al. | An on-line tool wear monitoring method based on cutting power | |
| KR102512875B1 (en) | Tool path calibration model generation method and tool path calibration method using the same | |
| JEMIELNIAK | Tool and process condition monitoring | |
| CN114247651A (en) | Online detection device for service life state of floor line cutter and floor grooving forming | |
| Ma et al. | A research on smart machine tool’s processing parameter governed with monitored vibration data |
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 |