CN117340495A - Equipment quality control system based on artificial intelligence - Google Patents
Equipment quality control system based on artificial intelligence Download PDFInfo
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- CN117340495A CN117340495A CN202311654923.4A CN202311654923A CN117340495A CN 117340495 A CN117340495 A CN 117340495A CN 202311654923 A CN202311654923 A CN 202311654923A CN 117340495 A CN117340495 A CN 117340495A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups
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- 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
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/414—Structure of the control system, e.g. common controller or multiprocessor systems, interface to servo, programmable interface controller
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Abstract
The invention discloses an equipment quality control system based on artificial intelligence, in particular to the technical field of intelligent control, various parameters in the welding process are acquired in real time through a sensor and monitoring equipment, the acquired data are analyzed through an algorithm to obtain an analysis result, and the welding parameters are automatically adjusted according to preset welding specifications and requirements to realize optimal welding quality and efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to an equipment quality control system based on artificial intelligence.
Background
The utility model provides a device quality control system based on artificial intelligence is a system for controlling hollow plate welding machine, aims at improving accuracy, efficiency and the security of welding process, and this system utilizes advanced technique and intelligent algorithm, realizes carrying out automation and intelligent control to key parameter in the welding process, and hollow plate welding is a common welding method, and wide application is in the manufacturing especially in the manufacturing and the processing of hollow plate, and hollow plate welding machine intelligent control system is designed to the control demand in the welding process of hollow plate.
The invention collects various parameters in the welding process in real time through the sensor and the monitoring equipment, analyzes the collected data through the algorithm and the model to obtain key performance indexes and trends in the welding process, automatically adjusts welding parameters such as welding current, voltage, speed and the like according to preset welding specifications and requirements to realize optimal welding quality and efficiency, in addition, the system can timely adjust the parameters and maintain a stable welding process according to real-time analysis results and control algorithms, monitors the running state of the equipment and abnormal conditions in the welding process in real time, immediately gives an alarm and takes corresponding measures once faults, anomalies or exceeds a set threshold value, and records the collected data as the basis of subsequent data analysis and optimization, and the data can be used in the aspects of statistical analysis, quality tracing, process optimization and the like.
Hollow plate welding is a common metal processing method for manufacturing parts of hollow and hollow structures, with the development of manufacturing industry and the improvement of automation level, the demand for intelligent control systems of hollow plate welding machines is increasing, traditional hollow plate welding machines use traditional control methods, operators need to manually adjust welding parameters, and lack automation and intellectualization functions, and the traditional control methods have problems including low production efficiency, difficult guarantee of welding quality, high requirements on the capabilities and experience of operators, and the like.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides an artificial intelligence-based device quality control system to solve the above-mentioned problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: an artificial intelligence based device quality control system comprising:
the target determination module: the method comprises the steps of determining a target hollow plate according to welding requirements, and sending the determined target hollow plate to a database;
and a data acquisition module: extracting a target hollow board stored in a database, acquiring parameter information of the target hollow board through feature extraction, and sending the parameter information to a data analysis module;
and a data analysis module: analyzing the parameter information of the target hollow plate through a mathematical model, and adjusting welding parameters according to an analysis result;
the algorithm module: automatically detecting bad welding gaps and welding firmness existing in a welding process by adopting a machine learning technology, and sending the bad welding gaps and the welding firmness to a risk assessment module;
risk assessment module: evaluating risk coefficients of the hollow plate welding machine according to poor welding gaps and welding firmness existing in the welding process, and sending evaluation results to a control module;
and the control module is used for: performing fault prediction on the target hollow plate welding machine according to the risk coefficient and the historical data of the target hollow plate welding machine, and taking preventive and treatment measures in advance;
and the man-machine interaction module is used for: displaying the fault prediction result of the target hollow plate welding machine, and arranging professional technicians to process according to the provided measures;
database: for storing various parameter information of the hollow board, and historical fault data.
Preferably, the parameter information of the target hollow plate comprises a hollow plate size parameter, a hollow plate material parameter, a hollow plate positioning parameter, a hollow plate clamping force, a hollow plate surface treatment parameter, a hollow plate heating mode and a hollow plate heating temperature; the welding parameters include welding current, welding voltage, welding speed, welding time, welding pressure, welding temperature, and welding angle.
Preferably, the preprocessing of the captured image information of the hollow plate of the target by the image processing technology specifically includes:
target hollow plate by camera equipmentShooting to obtain all image videos of the target hollow plate, dividing all image videos of the target hollow plate into a plurality of sub-images according to the number of framesLike the video block, the sub-image video block is subjected to pixel value extraction through the image processing library, and is marked as +.>Will->Represented by vectors, i.e +.>、Will be->、/>Substitution formulaThe method comprises the steps of carrying out a first treatment on the surface of the Where n is denoted as the size of the video block, < >>The closer the value is to 0, the higher the similarity expressed as adjacent image video blocks, the calculation of adjacent video blocks is performed for each sub video block, denoted +.>Comparing +.>Value, discard->The value is +.>And obtaining the image video of the new target hollow plate.
Preferably, the detecting the corner point of the new image video of the target hollow plate by using the mathematical model specifically includes:
coordinates are provided for each point on the imageLet its local autocorrelation matrix be +.>，The method comprises the steps of carrying out a first treatment on the surface of the Wherein->And->Represented as gradients of the image in x and y directions, respectively, A, B, C is a characteristic value of M,/->Is the weight of a pixel point, and calculates a corner response function according to the eigenvalue of the local autocorrelation matrix>，/>race/>The method comprises the steps of carrying out a first treatment on the surface of the Wherein K is expressed as an empirical parameter, a threshold value is set +.>，The method comprises the steps of carrying out a first treatment on the surface of the R is higher than +.>And (3) identifying the pixel points as corner points, performing feature matching on the target hollow plate according to the corner points, and tracking the position and the gesture of the target hollow plate in real time through the matched corner points.
Preferably, the calculating the poor welding gap existing in the welding process according to the deviation of the angular point position of the target hollow plate specifically includes:
the distance between the vernier caliper and the surface of the welded joint and measured by closely contacting the vernier caliper and the surface of the welded joint is recorded as，/>Expressed as the width of the welded joint, the vernier caliper is placed on both sides of the weld to ensure that the vernier caliper is in full contact with both sides of the weld, and the width of the weld is measured to be +.>The number of weld joints of the target welding machine joint is obtained as +.>Let bad welding gap be +>Then->The method comprises the steps of carrying out a first treatment on the surface of the Wherein D is expressed as that the gap between the two welds of the welded joint is uniform and constant, +.>The gap between the two welds, denoted as the welded joint, is not uniform and constant.
Preferably, the algorithm module evaluates the welding firmness by automatically detecting the dragging pressure of the target hollow plate welding machine, and specifically comprises the following steps:
the maximum static dragging pressure of the target hollow plate welding machine is set as P, the value of P can be calculated through a formula,the method comprises the steps of carrying out a first treatment on the surface of the First, a minimum pressure is applied to the target hollow plate welder +.>As initial pressure, every>Time, for the target hollow plate welder displacement increment +.>Taking the ith measurement, the i+1th measurement is obtained with the pressure to be added to the target hollow plate welder being +.>，/>Expressed as a fixed pressure, when the displacement increment of the hollow plate welder is measured at a time>Exceeding the set value->When the pressure applied to the target hollow plate welder is no longer changing, then the system is at a frequency +.>Pressure value on measuring target hollow plate welder +.>Taking u values, then ∈>The method comprises the steps of carrying out a first treatment on the surface of the The weld firmness of the target hollow plate welder was recorded as +.>ThenThe method comprises the steps of carrying out a first treatment on the surface of the The greater the drag pressure of the target hollow plate welder, the more firm the weld indicated as the target hollow plate welder.
Preferably, the risk assessment module performs risk coefficient assessment on the target hollow plate welding machine, and specifically includes:
through the imageThe processing equipment counts the bad welding gap of the target hollow plate welding machine and marks the bad welding gap asWelding firmness of the target hollow plate welding machine is +.>Poor welding gap with target hollow plate welding machine>Substituting formula to calculate risk coefficient of target hollow plate welding machine>Then->The method comprises the steps of carrying out a first treatment on the surface of the When->At a constant value, poor welding gap of the target hollow plate welding machine>The larger the value, the greater the risk factor representing the target hollow plate welder.
Preferably, the control module predicts the fault of the target hollow plate welding machine, and specifically includes:
extracting a historical fault data set of the target hollow plate welding machine in a database, and calculating a risk threshold of the target hollow plate welding machine through a mathematical modelRisk factor of the target hollow plate welder +.>Risk threshold for welding with target hollow plate>Comparing to obtain comparison result->Then->The method comprises the steps of carrying out a first treatment on the surface of the When->When the fault risk exists in the target hollow plate welding machine, the control module outputs a processing instruction to the man-machine interaction module, and when the fault risk exists in the target hollow plate welding machine +.>And when the target hollow plate welding machine is in a normal working state.
The invention has the technical effects and advantages that:
1. the intelligent control system can automatically optimize welding parameters according to specific welding process requirements, and ensure stability and consistency of a welding process, so that welding quality is improved, and welding defects are reduced.
2. The intelligent control system can realize automatic control, reduce manual intervention, improve production efficiency, and automatically adjust welding speed and power according to the requirements of products, thereby realizing rapid and efficient production.
3. The intelligent control system can adjust energy according to actual needs, so that energy waste is reduced to the greatest extent, energy is reasonably utilized through accurate control of welding parameters, and energy consumption is reduced.
Drawings
FIG. 1 is a diagram illustrating a system module connection according to 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.
Referring to fig. 1, the present invention provides an artificial intelligence based device quality control system, comprising: the system comprises a target determining module, a data acquisition module, a data analysis module, an algorithm module, a risk assessment module, a control module, a man-machine interaction module and a database.
The target determining module is respectively connected with the database and the data acquisition module, the data acquisition module is connected with the data analysis module, the data analysis module is connected with the algorithm module, the algorithm module is connected with the risk assessment module, the risk assessment module is connected with the control module, the control module is respectively connected with the database and the man-machine interaction module, and the man-machine interaction module is connected with the database.
The target determining module is used for determining the target hollow plate according to the welding requirement and sending the determined target hollow plate to the database.
The data acquisition module extracts the target hollow plate stored in the database, acquires the parameter information of the target hollow plate through feature extraction, and sends the parameter information to the data analysis module.
And the data analysis module analyzes the parameter information of the target hollow plate through a mathematical model and adjusts the welding parameters according to the analysis result.
In one possible design, the parameter information of the target hollow plate includes a hollow plate size parameter, a hollow plate material parameter, a hollow plate positioning parameter, a hollow plate clamping force, a hollow plate surface treatment parameter, and a hollow plate heating mode and temperature; the welding parameters include welding current, welding voltage, welding speed, welding time, welding pressure, welding temperature, and welding angle.
The algorithm module automatically detects poor welding gaps and welding firmness existing in the welding process by adopting a machine learning technology and sends the poor welding gaps and the welding firmness to the risk assessment module.
In one possible design, the preprocessing of the captured image information of the hollow plate of the target by the image processing technology specifically includes:
target hollow plate by camera equipmentShooting to obtain all image videos of the target hollow plate, dividing all image videos of the target hollow plate into a plurality of sub-image video blocks according to the number of frames, extracting pixel values of the sub-image video blocks through an image processing library, and marking the sub-image video blocks as +.>Will->Represented by vectors, i.e +.>、Will be->、/>Substitution formulaThe method comprises the steps of carrying out a first treatment on the surface of the Where n is denoted as the size of the video block, < >>The closer the value is to 0, the higher the similarity expressed as adjacent image video blocks, the calculation of adjacent video blocks is performed for each sub video block, denoted +.>Comparing +.>Value, discard->The value is +.>Is of (1)And obtaining the image video of the new target hollow plate.
Further, the detecting the corner point of the new image video of the target hollow plate through the mathematical model specifically includes:
coordinates are provided for each point on the imageLet its local autocorrelation matrix be +.>，The method comprises the steps of carrying out a first treatment on the surface of the Wherein->And->Represented as gradients of the image in x and y directions, respectively, A, B, C is a characteristic value of M,/->Is the weight of a pixel point, and calculates a corner response function according to the eigenvalue of the local autocorrelation matrix>，/>race/>The method comprises the steps of carrying out a first treatment on the surface of the Wherein K is expressed as an empirical parameter, a threshold value is set +.>，The method comprises the steps of carrying out a first treatment on the surface of the R is higher than +.>Is identified as a corner point, rootAnd carrying out feature matching on the target hollow plate according to the corner points, and tracking the position and the gesture of the target hollow plate in real time through the matched corner points.
In one possible design, the calculating the bad welding gap existing in the welding process according to the deviation of the angular point position of the target hollow plate specifically includes:
the distance between the vernier caliper and the surface of the welded joint and measured by closely contacting the vernier caliper and the surface of the welded joint is recorded as，/>Expressed as the width of the welded joint, the vernier caliper is placed on both sides of the weld to ensure that the vernier caliper is in full contact with both sides of the weld, and the width of the weld is measured to be +.>The number of weld joints of the target welding machine joint is obtained as +.>Let bad welding gap be +>Then->The method comprises the steps of carrying out a first treatment on the surface of the Wherein D is expressed as that the gap between the two welds of the welded joint is uniform and constant, +.>The gap between the two welds, denoted as the welded joint, is not uniform and constant.
In one possible design, the algorithm module evaluates the firmness of the weld by automatically detecting the dragging pressure of the target hollow plate welder, specifically comprising:
the maximum static dragging pressure of the target hollow plate welding machine is set as P, the value of P can be calculated through a formula,the method comprises the steps of carrying out a first treatment on the surface of the First, a minimum pressure is applied to the target hollow plate welder +.>As initial pressure, every>Time, for the target hollow plate welder displacement increment +.>Taking the ith measurement, the i+1th measurement is obtained with the pressure to be added to the target hollow plate welder being +.>，/>Expressed as a fixed pressure, when the displacement increment of the hollow plate welder is measured at a time>Exceeding the set value->When the pressure applied to the target hollow plate welder is no longer changing, then the system is at a frequency +.>Pressure value on measuring target hollow plate welder +.>Taking u values, then ∈>The method comprises the steps of carrying out a first treatment on the surface of the The weld firmness of the target hollow plate welder was recorded as +.>ThenThe method comprises the steps of carrying out a first treatment on the surface of the When the hollow plate is weldedThe larger the dragging pressure of the welding machine, the more firm the welding of the target hollow plate welding machine is.
And the risk assessment module assesses risk coefficients of the hollow plate welding machine according to poor welding gaps and welding firmness existing in the welding process, and sends assessment results to the control module.
In one possible design, the risk assessment module performs risk coefficient assessment on the target hollow plate welding machine, and specifically includes:
counting bad welding gaps of a target hollow plate welding machine through image processing equipment, and marking the bad welding gaps asWelding firmness of the target hollow plate welding machine is +.>Poor welding gap with target hollow plate welding machine>Substituting formula to calculate risk coefficient of target hollow plate welding machine>Then->The method comprises the steps of carrying out a first treatment on the surface of the When->At a constant value, poor welding gap of the target hollow plate welding machine>The larger the value, the greater the risk factor representing the target hollow plate welder.
And the control module predicts faults of the target hollow plate welding machine according to the risk coefficient and the historical data of the target hollow plate welding machine and takes preventive measures in advance.
And the man-machine interaction module displays a fault prediction result of the target hollow plate welding machine and arranges professional technicians to process according to the provided measures.
In one possible design, the control module predicts a failure of the target hollow plate welder, specifically including:
extracting a historical fault data set of the target hollow plate welding machine in a database, and calculating a risk threshold of the target hollow plate welding machine through a mathematical modelRisk factor of the target hollow plate welder +.>Risk threshold for welding with target hollow plate>Comparing to obtain comparison result->Then->The method comprises the steps of carrying out a first treatment on the surface of the When->When the fault risk exists in the target hollow plate welding machine, the control module outputs a processing instruction to the man-machine interaction module, and when the fault risk exists in the target hollow plate welding machine +.>And when the target hollow plate welding machine is in a normal working state.
The database is used for storing various parameter information of the hollow board and historical fault data.
In this embodiment, various parameters in the welding process are collected in real time through the sensor and the monitoring device, collected data are analyzed through the algorithm and the model to obtain key performance indexes and trends in the welding process, welding parameters such as welding current, voltage, speed and the like are automatically adjusted according to preset welding specifications and requirements to achieve optimal welding quality and efficiency, in addition, the system can timely adjust the parameters and keep a stable welding process according to real-time analysis results and control algorithms, the running state of the device and abnormal conditions in the welding process are monitored in real time, once faults, abnormal conditions or exceeding a set threshold value are detected, an alarm is sent out immediately, corresponding measures are taken, collected data are recorded, and the data can be used as the basis for subsequent data analysis and optimization, and can be used for statistical analysis, quality tracing, process optimization and the like.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. An artificial intelligence based device quality control system, comprising:
the target determination module: the method comprises the steps of determining a target hollow plate according to welding requirements, and sending the determined target hollow plate to a database;
and a data acquisition module: extracting a target hollow board stored in a database, acquiring parameter information of the target hollow board through feature extraction, and sending the parameter information to a data analysis module;
and a data analysis module: analyzing the parameter information of the target hollow plate through a mathematical model, and adjusting welding parameters according to an analysis result;
the algorithm module: automatically detecting bad welding gaps and welding firmness existing in a welding process by adopting a machine learning technology, and sending the bad welding gaps and the welding firmness to a risk assessment module;
risk assessment module: evaluating risk coefficients of the hollow plate welding machine according to poor welding gaps and welding firmness existing in the welding process, and sending evaluation results to a control module;
and the control module is used for: performing fault prediction on the target hollow plate welding machine according to the risk coefficient and the historical data of the target hollow plate welding machine, and taking preventive and treatment measures in advance;
and the man-machine interaction module is used for: displaying the fault prediction result of the target hollow plate welding machine, and arranging professional technicians to process according to the provided measures;
database: for storing various parameter information of the hollow board, and historical fault data.
2. The artificial intelligence based equipment quality control system of claim 1, wherein the parameter information of the target hollow plate includes a hollow plate size parameter, a hollow plate material parameter, a hollow plate positioning parameter, a hollow plate clamping force, a hollow plate surface treatment parameter, and a hollow plate heating mode and temperature; the welding parameters include welding current, welding voltage, welding speed, welding time, welding pressure, welding temperature, and welding angle.
3. The artificial intelligence-based equipment quality control system according to claim 1, wherein the preprocessing of the captured image information of the hollow plate of the object by the image processing technology specifically comprises:
target hollow plate by camera equipmentShooting to obtain all image videos of the target hollow plate, dividing all image videos of the target hollow plate into a plurality of sub-image video blocks according to the number of frames, extracting pixel values of the sub-image video blocks through an image processing library, and marking the sub-image video blocks as +.>Will->Represented by vectors, i.e +.>、Will be->、/>Substitution formulaThe method comprises the steps of carrying out a first treatment on the surface of the Where n is denoted as the size of the video block, < >>The closer the value is to 0, the higher the similarity expressed as adjacent image video blocks, the calculation of adjacent video blocks is performed for each sub video block, denoted +.>Comparing +.>Value, discard->The value is +.>And obtaining the image video of the new target hollow plate.
4. The artificial intelligence-based equipment quality control system according to claim 3, wherein the corner detection is performed on the new image video of the target hollow plate through a mathematical model, and the system specifically comprises:
coordinates are provided for each point on the imageLet its local autocorrelation matrix be +.>ThenThe method comprises the steps of carrying out a first treatment on the surface of the Wherein->And->Represented as gradients of the image in x and y directions, respectively, A, B, C is a characteristic value of M,/->Is the weight of a pixel point, and calculates a corner response function according to the eigenvalue of the local autocorrelation matrix>，/>race/>The method comprises the steps of carrying out a first treatment on the surface of the Wherein K is expressed as an empirical parameter, a threshold value is set +.>ThenThe method comprises the steps of carrying out a first treatment on the surface of the R is higher than +.>And (3) identifying the pixel points as corner points, performing feature matching on the target hollow plate according to the corner points, and tracking the position and the gesture of the target hollow plate in real time through the matched corner points.
5. The artificial intelligence based equipment quality control system according to claim 4, wherein the calculating of the poor welding gap existing in the welding process according to the deviation of the angular point position of the target hollow plate specifically comprises:
the distance between the vernier caliper and the surface of the welded joint and measured by closely contacting the vernier caliper and the surface of the welded joint is recorded as，/>Expressed as the width of the welded joint, the vernier caliper is placed on both sides of the weld to ensure that the vernier caliper is in full contact with both sides of the weld, and the width of the weld is measured to be +.>The number of weld joints of the target welding machine joint is obtained as +.>Let bad welding gap be +>ThenThe method comprises the steps of carrying out a first treatment on the surface of the Wherein D is expressed as that the gap between the two welds of the welded joint is uniform and constant, +.>The gap between the two welds, denoted as the welded joint, is not uniform and constant.
6. The system of claim 1, wherein the algorithm module evaluates weld firmness by automatically detecting a target hollow plate welder dragging pressure, and comprises:
setting the maximum static dragging pressure of the target hollow plate welding machine as P, and calculating the value of P according to a formulaThe method comprises the steps of carrying out a first treatment on the surface of the First, a minimum pressure is applied to the target hollow plate welder +.>As initial pressure, every>Time, for the target hollow plate welder displacement increment +.>Taking the ith measurement, the i+1th measurement is obtained with the pressure to be added to the target hollow plate welder being +.>，/>Expressed as a fixed pressure, when the displacement increment of the hollow plate welder is measured at a time>Exceeding the set value->When the pressure applied to the target hollow plate welder is no longer changing, then the system is at a frequency +.>Pressure value on measuring target hollow plate welder +.>Taking u values, then ∈>The method comprises the steps of carrying out a first treatment on the surface of the The weld firmness of the target hollow plate welder was recorded as +.>ThenThe method comprises the steps of carrying out a first treatment on the surface of the The greater the drag pressure of the target hollow plate welder, the more firm the weld indicated as the target hollow plate welder.
7. The equipment quality control system based on artificial intelligence according to claim 1, wherein the risk assessment module assesses risk factors of the target hollow plate welder, and specifically comprises:
counting bad welding gaps of a target hollow plate welding machine through image processing equipment, and marking the bad welding gaps asWelding firmness of the target hollow plate welding machine is +.>Poor welding gap with target hollow plate welding machine>Substituting formula to calculate risk coefficient of target hollow plate welding machine>Then->The method comprises the steps of carrying out a first treatment on the surface of the When->At a constant value, poor welding gap of the target hollow plate welding machine>The larger the value, the greater the risk factor representing the target hollow plate welder.
8. The equipment quality control system based on artificial intelligence according to claim 7, wherein the control module performs fault prediction on the target hollow plate welding machine, and specifically comprises:
extracting a historical fault data set of the target hollow plate welding machine in a database, and calculating a risk threshold of the target hollow plate welding machine through a mathematical modelRisk factor of the target hollow plate welder +.>Risk threshold for welding with target hollow plate>Comparing to obtain comparison result->Then->The method comprises the steps of carrying out a first treatment on the surface of the When->When the fault risk exists in the target hollow plate welding machine, the control module outputs a processing instruction to the man-machine interaction module, and when the fault risk exists in the target hollow plate welding machine +.>And when the target hollow plate welding machine is in a normal working state.
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Citations (15)
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