CN116135421B - Welding processing path optimization method and system based on artificial intelligence - Google Patents
Welding processing path optimization method and system based on artificial intelligence Download PDFInfo
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- CN116135421B CN116135421B CN202310402274.2A CN202310402274A CN116135421B CN 116135421 B CN116135421 B CN 116135421B CN 202310402274 A CN202310402274 A CN 202310402274A CN 116135421 B CN116135421 B CN 116135421B
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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
- B23K37/02—Carriages for supporting the welding or cutting element
- B23K37/0252—Steering means
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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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- 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
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- 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
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to a welding processing path optimization method and a system based on artificial intelligence, which belong to the technical field of welding processing path optimization. According to the invention, the energy loss of each processing path diagram can be calculated, so that the life change caused by the energy loss is predicted according to the energy loss and the estimated life value of each motion unit of the current welding equipment, and the processing path diagram with the maximum residual life is selected, so that the processing path of the welding equipment in the processing process is more reasonable, and the service life of the welding equipment is further prolonged.
Description
Technical Field
The invention relates to the technical field of welding processing path optimization, in particular to a welding processing path optimization method and system based on artificial intelligence.
Background
In the process of manufacturing related engineering machinery equipment, multiple parts are often required to be combined, so that a complete engineering machinery equipment can be manufactured by combining the multiple parts. Welding is a common process for combining the parts, but the traditional welding technology can cause various problems when welding machine equipment, such as accidents, poor welding quality and the like easily caused by constructors in the operation process. With the continuous emergence of emerging scientific technologies, automatic welding equipment is increasingly widely applied in the field of mechanical manufacturing, and the automation degree of welding engineering is greatly improved. An automated welding apparatus is an indispensable production apparatus, which is generally referred to as a welding robot and a welding special machine. The automatic welding equipment is used in the welding work, so that the welding efficiency and the welding quality can be effectively improved, and the consistency of the welding work is improved; the equipment has higher automation degree, can greatly improve the welding efficiency and reduce the time cost of welding work; the operation difficulty of the equipment is lower, and the work difficulty of welding personnel is reduced to a certain extent. However, there is still a problem in the automated welding apparatus in that the processing paths of the welding apparatus correspond to the motion gestures of the welding apparatus, and the difference in the processing paths may affect the power consumption of the welding apparatus during the welding process, so that the use frequencies of the respective motion units are inconsistent, thereby affecting the service lives thereof. Therefore, the processing path of the welding apparatus is to be optimized.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a welding processing path optimization method and system based on artificial intelligence.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a welding processing path optimization method based on artificial intelligence, which comprises the following steps:
acquiring processing technology information of a current part to be welded, determining a processing procedure of the current part to be welded according to the processing technology information, and acquiring a processing path of the current part to be welded based on the processing procedure of the current part to be welded;
constructing a welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model;
calculating the residual life information of each motion unit in each motion gesture change path diagram based on the motion gesture change path diagram;
and determining a final processing optimization path according to the residual life information of each motion unit in each motion attitude change path diagram, and transmitting the final processing optimization path to a remote welding processing terminal.
Further, in a preferred embodiment of the present invention, processing technology information of a current part to be welded is obtained, a processing procedure of the current part to be welded is determined according to the processing technology information, and a processing path of the current part to be welded is obtained based on the processing procedure of the current part to be welded, and the method specifically includes the following steps:
Acquiring processing technology information of a current part to be welded, and determining a processing procedure of the current part to be welded according to the processing technology information;
inputting the processing procedure of the current part to be welded into an ant colony algorithm until a legal ant path is obtained, and obtaining a processing sub-path corresponding to the processing procedure of each current part to be welded;
synthesizing the processing sub-paths corresponding to the processing procedures of each current part to be welded to generate one or more processing paths of the current parts to be welded;
and constructing an ant preference path model, obtaining preference indexes according to the processing path of the current part to be welded and the ant preference path model, acquiring the processing path with the maximum preference index as the processing path of the current part to be welded, and outputting the processing path of the current part to be welded.
Further, in a preferred embodiment of the present invention, a welding equipment processing path simulation model is constructed, and a motion gesture change path diagram is obtained according to the processing path and the welding equipment processing path simulation model, and specifically includes the following steps:
constructing an initial welding equipment processing path simulation model based on a virtual reality technology, and acquiring limit motion coordinate data information of each motion unit of the welding equipment;
Acquiring contour three-dimensional coordinate information of welding equipment, constructing a welding equipment three-dimensional model diagram according to the contour three-dimensional coordinate information of the welding equipment, and acquiring a blank model diagram of a current part to be welded;
acquiring the position relation between welding equipment and a part to be welded, and inputting the three-dimensional model diagram of the welding equipment and the blank model diagram of the current part to be welded into the initial welding equipment processing path simulation model according to the position relation between the welding equipment and the part to be welded;
and constructing a welding equipment machining path simulation model according to the limit motion coordinate data information of each motion unit of the welding equipment, the machining path and the initial welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the welding equipment machining path simulation model.
Further, in a preferred embodiment of the present invention, the remaining life information of each motion unit in each motion posture change path diagram is calculated based on the motion posture change path diagram, and specifically includes the following steps:
acquiring a motion gesture change path diagram based on a time sequence of each processing path diagram, and calculating energy loss information of each motion gesture change path diagram according to the motion gesture change path diagram based on the time sequence;
Acquiring estimated life information of each motion unit of the welding equipment, and acquiring energy loss information of each motion unit in the welding equipment according to the energy loss information of each motion gesture change path diagram;
obtaining service life consumption information of each motion unit in each motion gesture change path diagram based on the energy consumption information of each motion unit in the welding equipment;
and calculating the residual life information of each motion unit in each motion attitude change path diagram according to the estimated life information of each motion unit of the welding equipment and the life consumption information of each motion unit in each motion attitude change path diagram.
Further, in a preferred embodiment of the present invention, the life consumption information of each motion unit in the motion gesture change path diagram is obtained based on the energy consumption information of each motion unit in the welding device, and specifically includes the following steps:
acquiring service life consumption information under the historical energy consumption information of each motion unit of a large number of current welding equipment through a big data network, and dividing the service life consumption information under the historical energy consumption information of each motion unit of the large number of current welding equipment into a training set and a testing set;
Constructing a life attenuation prediction model based on a convolutional neural network, inputting the training set into the life attenuation prediction model for training, adjusting parameters of the life attenuation prediction model, and storing model parameters;
testing the life attenuation prediction model through the test set until the life attenuation prediction model meets the preset requirement, and obtaining a trained life attenuation prediction model;
and inputting the energy loss information of each moving unit in the welding equipment into the trained life attenuation prediction model to acquire the life consumption information of each moving unit in each processing path diagram.
Further, in a preferred embodiment of the present invention, a final processing optimization path is determined according to the remaining life information of each motion unit in each motion gesture change path diagram, and the final processing optimization path is transmitted to a remote welding processing terminal, which specifically includes the following steps:
constructing a residual life information sorting table, inputting the residual life information of each motion unit in each motion gesture change path diagram into the residual life information sorting table for sorting, and generating a plurality of residual life information sorting tables;
Obtaining the minimum residual life value of each residual life information sorting table, and comparing the minimum residual life value of each residual life information sorting table to obtain the maximum value of the minimum residual life value;
obtaining a corresponding processing path diagram according to the maximum value of the minimum residual life value, and obtaining the corresponding processing path diagram as a final processing optimization path according to the maximum value of the minimum residual life value;
and transmitting the final processing optimization path to a remote welding processing terminal.
The second aspect of the present invention provides an artificial intelligence based welding process path optimization system, the optimization system including a memory and a processor, the memory storing an artificial intelligence based welding process path optimization method program, the artificial intelligence based welding process path optimization method program when executed by the processor implementing the steps of:
acquiring processing technology information of a current part to be welded, determining a processing procedure of the current part to be welded according to the processing technology information, and acquiring a processing path of the current part to be welded based on the processing procedure of the current part to be welded;
constructing a welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model;
Calculating the residual life information of each motion unit in each motion gesture change path diagram based on the motion gesture change path diagram;
and determining a final processing optimization path according to the residual life information of each motion unit in each motion attitude change path diagram, and transmitting the final processing optimization path to a remote welding processing terminal.
In this embodiment, a welding equipment processing path simulation model is constructed, and a motion gesture change path diagram is obtained according to the processing path and the welding equipment processing path simulation model, and specifically includes the following steps:
constructing an initial welding equipment processing path simulation model based on a virtual reality technology, and acquiring limit motion coordinate data information of each motion unit of the welding equipment;
acquiring contour three-dimensional coordinate information of welding equipment, constructing a welding equipment three-dimensional model diagram according to the contour three-dimensional coordinate information of the welding equipment, and acquiring a blank model diagram of a current part to be welded;
acquiring the position relation between welding equipment and a part to be welded, and inputting the three-dimensional model diagram of the welding equipment and the blank model diagram of the current part to be welded into the initial welding equipment processing path simulation model according to the position relation between the welding equipment and the part to be welded;
And constructing a welding equipment machining path simulation model according to the limit motion coordinate data information of each motion unit of the welding equipment, the machining path and the initial welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the welding equipment machining path simulation model.
In this embodiment, the remaining life information of each motion unit in each motion gesture change path diagram is calculated based on the motion gesture change path diagram, and specifically includes the following steps:
acquiring a motion gesture change path diagram based on a time sequence of each processing path diagram, and calculating energy loss information of each motion gesture change path diagram according to the motion gesture change path diagram based on the time sequence;
acquiring estimated life information of each motion unit of the welding equipment, and acquiring energy loss information of each motion unit in the welding equipment according to the energy loss information of each motion gesture change path diagram;
obtaining service life consumption information of each motion unit in each motion gesture change path diagram based on the energy consumption information of each motion unit in the welding equipment;
and calculating the residual life information of each motion unit in each motion attitude change path diagram according to the estimated life information of each motion unit of the welding equipment and the life consumption information of each motion unit in each motion attitude change path diagram.
In this embodiment, a final processing optimization path is determined according to the remaining life information of each motion unit in each motion gesture change path diagram, and the final processing optimization path is transmitted to a remote welding processing terminal, which specifically includes the following steps:
constructing a residual life information sorting table, inputting the residual life information of each motion unit in each motion gesture change path diagram into the residual life information sorting table for sorting, and generating a plurality of residual life information sorting tables;
obtaining the minimum residual life value of each residual life information sorting table, and comparing the minimum residual life value of each residual life information sorting table to obtain the maximum value of the minimum residual life value;
obtaining a corresponding processing path diagram according to the maximum value of the minimum residual life value, and obtaining the corresponding processing path diagram as a final processing optimization path according to the maximum value of the minimum residual life value;
and transmitting the final processing optimization path to a remote welding processing terminal.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the invention, the processing process information of the current part to be welded is acquired, the processing procedure of the current part to be welded is determined according to the processing process information, the processing path of the current part to be welded is acquired based on the processing procedure of the current part to be welded, a welding equipment processing path simulation model is further constructed, a motion gesture change path diagram is acquired according to the processing path and the welding equipment processing path simulation model, and further the residual life information of each motion unit in each motion gesture change path diagram is calculated based on the motion gesture change path diagram, so that a final processing optimization path is determined according to the residual life information of each motion unit in each motion gesture change path diagram, and the final processing optimization path is transmitted to a remote welding processing terminal. According to the invention, the energy loss of each processing path diagram can be calculated, so that the life change caused by the energy loss is predicted according to the energy loss and the estimated life value of each motion unit of the current welding equipment, and the processing path diagram with the maximum residual life is selected, so that the processing path of the welding equipment in the processing process is more reasonable, and the service life of the welding equipment is further prolonged.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates an overall method flow diagram of an artificial intelligence based welding process path optimization method;
FIG. 2 illustrates a first method flow diagram of an artificial intelligence based welding process path optimization method;
FIG. 3 illustrates a second method flow diagram of an artificial intelligence based welding process path optimization method;
FIG. 4 illustrates a system block diagram of an artificial intelligence based welding process path optimization system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a welding processing path optimization method based on artificial intelligence, which includes the following steps:
s102, acquiring processing technology information of a current part to be welded, determining a processing procedure of the current part to be welded according to the processing technology information, and acquiring a processing path of the current part to be welded based on the processing procedure of the current part to be welded;
s104, constructing a welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model;
s106, calculating the residual life information of each motion unit in each motion gesture change path diagram based on the motion gesture change path diagram;
s108, determining a final processing optimization path according to the residual life information of each motion unit in each motion gesture change path diagram, and transmitting the final processing optimization path to a remote welding processing terminal.
The invention can predict the life change caused by the energy loss according to the energy loss and the estimated life value of each motion unit of the current welding equipment by calculating the energy loss of each processing path diagram, so that the processing path diagram with the maximum residual life is selected, the processing path of the welding equipment in the processing process is more reasonable, and the service life of the welding equipment is further prolonged.
Further, in a preferred embodiment of the present invention, processing technology information of a current part to be welded is obtained, a processing procedure of the current part to be welded is determined according to the processing technology information, and a processing path of the current part to be welded is obtained based on the processing procedure of the current part to be welded, and the method specifically includes the following steps:
acquiring processing technology information of a current part to be welded, and determining a processing procedure of the current part to be welded according to the processing technology information;
inputting the processing procedure of the current part to be welded into an ant colony algorithm until a legal ant path is obtained, and obtaining a processing sub-path corresponding to the processing procedure of each current part to be welded;
synthesizing processing sub-paths corresponding to the processing procedures of each current part to be welded to generate one or more processing paths of the current parts to be welded;
And constructing an ant preference path model, obtaining preference indexes according to the processing path of the current part to be welded and the ant preference path model, acquiring the processing path with the maximum preference index as the processing path of the current part to be welded, and outputting the processing path of the current part to be welded.
It should be noted that, in this embodiment, the ant preference path model is equivalent to a database, and records the processing path of the part to be welded currently by recording the ant colony algorithm, so as to obtain the processing path with the maximum preference index as the processing path of the part to be welded currently, so that the distance of the processing path is the shortest, which is beneficial to improving the processing speed in the processing process.
Further, in a preferred embodiment of the present invention, a welding equipment processing path simulation model is constructed, and a motion gesture change path diagram is obtained according to the processing path and the welding equipment processing path simulation model, and specifically includes the following steps:
constructing an initial welding equipment processing path simulation model based on a virtual reality technology, and acquiring limit motion coordinate data information of each motion unit of the welding equipment;
acquiring contour three-dimensional coordinate information of welding equipment, constructing a welding equipment three-dimensional model diagram according to the contour three-dimensional coordinate information of the welding equipment, and acquiring a blank model diagram of a current part to be welded;
Acquiring the position relation between welding equipment and a part to be welded, and inputting a three-dimensional model diagram of the welding equipment and a blank model diagram of the current part to be welded into an initial welding equipment processing path simulation model according to the position relation between the welding equipment and the part to be welded;
and constructing a welding equipment machining path simulation model according to the limit motion coordinate data information, the machining path and the initial welding equipment machining path simulation model of each motion unit of the welding equipment, and acquiring a motion gesture change path diagram according to the welding equipment machining path simulation model.
It should be noted that, in this embodiment, since the welding apparatus may be a mechanical arm or a device similar to the mechanical arm, and the automatic welding apparatus includes a plurality of motion units, each motion unit is a driving device, such as a cylinder, a motor, and the like, the initial welding apparatus processing path simulation model is constructed by using a virtual reality technology, and since the mechanical arm has a limited motion position, it may be noted that, the determination of the welding position of one welding apparatus may be made up of a plurality of motion postures of the mechanical arm (for example, the position at the point of welding a may be implemented by a plurality of postures of the mechanical arm), that is, the welding apparatus processing path simulation model is constructed according to the limit motion coordinate data information of each motion unit of the welding apparatus, the processing path, and the initial welding apparatus processing path simulation model, where the motion posture change path diagram may represent a dynamic collection of the motion postures of the welding apparatus during the welding process, and in fact, the energy (mainly, electrical energy consumed by the motion postures of each mechanical arm is different, thereby resulting in inconsistent frequency of use of the motor. The three-dimensional model diagram of the welding equipment and the blank model diagram of the part to be welded at present can be realized through three-dimensional modeling software, such as UG software, solidWorks software and the like.
As shown in fig. 2, in a preferred embodiment of the present invention, the remaining life information of each motion unit in each motion posture change path diagram is calculated based on the motion posture change path diagram, which specifically includes the following steps:
s202, acquiring a motion gesture change path diagram based on a time sequence of each processing path diagram, and calculating energy loss information of each motion gesture change path diagram according to the motion gesture change path diagram based on the time sequence;
s204, acquiring estimated life information of each motion unit of the welding equipment, and acquiring energy loss information of each motion unit in the welding equipment according to the energy loss information of each motion gesture change path diagram;
s206, obtaining service life consumption information of each motion unit in each motion gesture change path diagram based on the energy consumption information of each motion unit in the welding equipment;
s208, calculating the residual life information of each motion unit in each motion gesture change path diagram according to the estimated life information of each motion unit of the welding equipment and the life consumption information of each motion unit in each motion gesture change path diagram.
By the method, the service life information required to be consumed by each processing path diagram can be calculated, so that the residual service life information of each motion unit is calculated, and the optimal processing path is selected according to the residual service life information of each motion unit on the welding equipment.
As shown in fig. 3, in a further preferred embodiment of the present invention, the life consumption information of each motion unit in the motion gesture change path diagram is obtained based on the energy consumption information of each motion unit in the welding device, and specifically includes the following steps:
s302, acquiring service life consumption information under the historical energy consumption information of a large number of moving units of the current welding equipment through a big data network, and dividing the service life consumption information under the historical energy consumption information of the moving units of the current welding equipment into a training set and a testing set;
s304, constructing a life attenuation prediction model based on a convolutional neural network, inputting a training set into the life attenuation prediction model for training, adjusting parameters of the life attenuation prediction model, and storing model parameters;
s306, testing the life attenuation prediction model through a test set until the life attenuation prediction model meets the preset requirement, and obtaining a trained life attenuation prediction model;
and S308, inputting the energy consumption information of each motion unit in the welding equipment into the trained life attenuation prediction model to acquire the life consumption information of each motion unit in each processing path diagram.
It should be noted that, by the method, the life consumption information of each motion unit can be predicted by the convolutional neural network.
Further, in a preferred embodiment of the present invention, a final processing optimization path is determined according to the remaining life information of each motion unit in each motion gesture change path diagram, and the final processing optimization path is transmitted to a remote welding processing terminal, which specifically includes the following steps:
constructing a residual life information sorting table, inputting the residual life information of each motion unit in each motion gesture change path diagram into the residual life information sorting table for sorting, and generating a plurality of residual life information sorting tables;
obtaining the minimum residual life value of each residual life information sorting table, and comparing the minimum residual life value of each residual life information sorting table to obtain the maximum value of the minimum residual life value;
obtaining a corresponding processing path diagram according to the maximum value of the minimum residual life value, and obtaining the corresponding processing path diagram according to the maximum value of the minimum residual life value to serve as a final processing optimization path;
and transmitting the final processing optimization path to a remote welding processing terminal.
In the practical application scenario, as the mechanical arm or the welding device similar to the mechanical arm is provided with a plurality of moving units, however, the use model of each moving unit is inconsistent due to the relation of moment, for example, the use model of a motor is inconsistent or the use model of a cylinder is inconsistent, after the residual life information of each moving unit in each moving gesture change path diagram is calculated, the minimum value of the residual life information of each moving unit in each moving gesture change path diagram is selected as a reference sample, and the optimal processing path can be effectively selected through the method.
In addition, the invention can also comprise the following steps:
judging whether the minimum residual life value is lower than a preset residual life value, if so, constructing a retrieval tag according to the minimum residual life value;
acquiring working environment data corresponding to the residual life value corresponding to the same equipment through a big data network according to the retrieval tag, acquiring working environment data of the current welding equipment, and comparing the working environment data corresponding to the residual life value corresponding to the same equipment with the working environment data of the current welding equipment to obtain a deviation rate;
If the deviation rate is lower than a preset deviation rate threshold, inputting the working environment data corresponding to the residual life value corresponding to the same equipment into a Bayesian network to acquire the failure rate corresponding to the same equipment;
if the fault rate is larger than a preset fault rate, sending out early warning information according to the fault rate, and generating related maintenance suggestions according to the early warning information.
It should be noted that, by the method, when the minimum remaining life value is lower than the preset remaining life value, if the failure rate is greater than the preset failure rate, a related suggestion is generated and early-warned, faults are found in the welding processing process in time, and the phenomenon that the welding processing process is stopped due to the fact that the machining is partially performed in the welding processing process is avoided. The working environment information may be temperature, humidity, dust level, etc.
It should be noted that the method may further include the following steps:
acquiring working environment information of current welding equipment, and analyzing potential relations of the working environment information to life information of each motion unit through a multi-factor logistic regression analysis method to acquire corresponding weight vector information;
carrying out relevance analysis on the corresponding weight vector information through a gray relevance analysis method to obtain the evaluation score of each environmental factor on the life information of each motion unit;
Judging whether the evaluation score is larger than a preset evaluation score, if so, acquiring current environment information which is the same as the current welding equipment and service life information corresponding to the service condition through a big data network;
and constructing a service life consumption curve according to the service life consumption information, acquiring life consumption information of the current welding equipment within a preset time according to the service life curve, and correcting the residual life information of each motion unit in each motion gesture change path diagram according to the life consumption information of the current welding equipment within the preset time.
It should be noted that, logistic regression is also called logistic regression analysis, which is a generalized linear regression analysis model and is commonly used in the fields of data mining economic prediction and the like. The method can correct the residual life information of each motion unit in each motion gesture change path graph according to the working environment information of the welding equipment, so that the optimal processing path is more accurate.
The second aspect of the present invention provides an artificial intelligence based welding process path optimization system, the optimization system including a memory 41 and a processor 62, the memory 41 storing an artificial intelligence based welding process path optimization method program, the artificial intelligence based welding process path optimization method program when executed by the processor 62 implementing the steps of:
Acquiring processing technology information of a current part to be welded, determining a processing procedure of the current part to be welded according to the processing technology information, and acquiring a processing path of the current part to be welded based on the processing procedure of the current part to be welded;
constructing a welding equipment processing path simulation model, and acquiring a motion gesture change path diagram according to the processing path and the welding equipment processing path simulation model;
calculating the residual life information of each motion unit in each motion gesture change path diagram based on the motion gesture change path diagram;
and determining a final processing optimization path according to the residual life information of each motion unit in each motion gesture change path diagram, and transmitting the final processing optimization path to a remote welding processing terminal.
In this embodiment, a welding equipment processing path simulation model is constructed, and a motion gesture change path diagram is obtained according to the processing path and the welding equipment processing path simulation model, and specifically includes the following steps:
constructing an initial welding equipment processing path simulation model based on a virtual reality technology, and acquiring limit motion coordinate data information of each motion unit of the welding equipment;
acquiring contour three-dimensional coordinate information of welding equipment, constructing a welding equipment three-dimensional model diagram according to the contour three-dimensional coordinate information of the welding equipment, and acquiring a blank model diagram of a current part to be welded;
Acquiring the position relation between welding equipment and a part to be welded, and inputting a three-dimensional model diagram of the welding equipment and a blank model diagram of the current part to be welded into an initial welding equipment processing path simulation model according to the position relation between the welding equipment and the part to be welded;
and constructing a welding equipment machining path simulation model according to the limit motion coordinate data information, the machining path and the initial welding equipment machining path simulation model of each motion unit of the welding equipment, and acquiring a motion gesture change path diagram according to the welding equipment machining path simulation model.
In this embodiment, the remaining life information of each motion unit in each motion posture change path diagram is calculated based on the motion posture change path diagram, and specifically includes the following steps:
acquiring a motion gesture change path diagram based on a time sequence of each processing path diagram, and calculating energy loss information of each motion gesture change path diagram according to the motion gesture change path diagram based on the time sequence;
the estimated service life information of each motion unit of the welding equipment is obtained, and the energy loss information of each motion unit in the welding equipment is obtained according to the energy loss information of each motion gesture change path diagram;
Obtaining service life consumption information of each motion unit in each motion gesture change path diagram based on the energy consumption information of each motion unit in the welding equipment;
and calculating the residual life information of each motion unit in each motion attitude change path diagram according to the estimated life information of each motion unit of the welding equipment and the life consumption information of each motion unit in each motion attitude change path diagram.
In this embodiment, a final processing optimization path is determined according to the remaining life information of each motion unit in each motion gesture change path diagram, and the final processing optimization path is transmitted to a remote welding processing terminal, which specifically includes the following steps:
constructing a residual life information sorting table, inputting the residual life information of each motion unit in each motion gesture change path diagram into the residual life information sorting table for sorting, and generating a plurality of residual life information sorting tables;
obtaining the minimum residual life value of each residual life information sorting table, and comparing the minimum residual life value of each residual life information sorting table to obtain the maximum value of the minimum residual life value;
obtaining a corresponding processing path diagram according to the maximum value of the minimum residual life value, and obtaining the corresponding processing path diagram according to the maximum value of the minimum residual life value to serve as a final processing optimization path;
And transmitting the final processing optimization path to a remote welding processing terminal.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The welding processing path optimization method based on artificial intelligence is characterized by comprising the following steps of:
acquiring processing technology information of a current part to be welded, determining a processing procedure of the current part to be welded according to the processing technology information, and acquiring a processing path of the current part to be welded based on the processing procedure of the current part to be welded;
constructing a welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model;
calculating the residual life information of each motion unit in each motion gesture change path diagram based on the motion gesture change path diagram;
determining a final processing optimization path according to the residual life information of each motion unit in each motion gesture change path diagram, and transmitting the final processing optimization path to a remote welding processing terminal;
The method comprises the steps of constructing a welding equipment machining path simulation model, and obtaining a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model, and specifically comprises the following steps:
constructing an initial welding equipment processing path simulation model based on a virtual reality technology, and acquiring limit motion coordinate data information of each motion unit of the welding equipment;
acquiring contour three-dimensional coordinate information of welding equipment, constructing a welding equipment three-dimensional model diagram according to the contour three-dimensional coordinate information of the welding equipment, and acquiring a blank model diagram of a current part to be welded;
acquiring the position relation between welding equipment and a part to be welded, and inputting the three-dimensional model diagram of the welding equipment and the blank model diagram of the current part to be welded into the initial welding equipment processing path simulation model according to the position relation between the welding equipment and the part to be welded;
and constructing a welding equipment machining path simulation model according to the limit motion coordinate data information of each motion unit of the welding equipment, the machining path and the initial welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the welding equipment machining path simulation model.
2. The welding processing path optimization method based on artificial intelligence according to claim 1, wherein the processing technology information of the current part to be welded is obtained, the processing procedure of the current part to be welded is determined according to the processing technology information, and the processing path of the current part to be welded is obtained based on the processing procedure of the current part to be welded, specifically comprising the following steps:
acquiring processing technology information of a current part to be welded, and determining a processing procedure of the current part to be welded according to the processing technology information;
inputting the processing procedure of the current part to be welded into an ant colony algorithm until a legal ant path is obtained, and obtaining a processing sub-path corresponding to the processing procedure of each current part to be welded;
synthesizing the processing sub-paths corresponding to the processing procedures of each current part to be welded to generate one or more processing paths of the current parts to be welded;
and constructing an ant preference path model, obtaining preference indexes according to the processing path of the current part to be welded and the ant preference path model, acquiring the processing path with the maximum preference index as the processing path of the current part to be welded, and outputting the processing path of the current part to be welded.
3. The welding process path optimization method based on artificial intelligence according to claim 1, wherein the remaining life information of each motion unit in each motion posture change path diagram is calculated based on the motion posture change path diagram, specifically comprising the steps of:
acquiring a motion gesture change path diagram based on a time sequence of each processing path diagram, and calculating energy loss information of each motion gesture change path diagram according to the motion gesture change path diagram based on the time sequence;
acquiring estimated life information of each motion unit of the welding equipment, and acquiring energy loss information of each motion unit in the welding equipment according to the energy loss information of each motion gesture change path diagram;
obtaining service life consumption information of each motion unit in each motion gesture change path diagram based on the energy consumption information of each motion unit in the welding equipment;
and calculating the residual life information of each motion unit in each motion attitude change path diagram according to the estimated life information of each motion unit of the welding equipment and the life consumption information of each motion unit in each motion attitude change path diagram.
4. The welding process path optimization method based on artificial intelligence according to claim 3, wherein the life consumption information of each motion unit in each motion attitude change path diagram is obtained based on the energy consumption information of each motion unit in the welding equipment, specifically comprising the following steps:
acquiring service life consumption information under the historical energy consumption information of each motion unit of a large number of current welding equipment through a big data network, and dividing the service life consumption information under the historical energy consumption information of each motion unit of the large number of current welding equipment into a training set and a testing set;
constructing a life attenuation prediction model based on a convolutional neural network, inputting the training set into the life attenuation prediction model for training, adjusting parameters of the life attenuation prediction model, and storing model parameters;
testing the life attenuation prediction model through the test set until the life attenuation prediction model meets the preset requirement, and obtaining a trained life attenuation prediction model;
and inputting the energy loss information of each moving unit in the welding equipment into the trained life attenuation prediction model to acquire the life consumption information of each moving unit in each processing path diagram.
5. The welding process path optimization method based on artificial intelligence according to claim 1, wherein a final process optimization path is determined according to remaining life information of each motion unit in each motion posture change path diagram, and the final process optimization path is transmitted to a remote welding process terminal, comprising the steps of:
constructing a residual life information sorting table, inputting the residual life information of each motion unit in each motion gesture change path diagram into the residual life information sorting table for sorting, and generating a plurality of residual life information sorting tables;
obtaining the minimum residual life value of each residual life information sorting table, and comparing the minimum residual life value of each residual life information sorting table to obtain the maximum value of the minimum residual life value;
obtaining a corresponding processing path diagram according to the maximum value of the minimum residual life value, and obtaining the corresponding processing path diagram as a final processing optimization path according to the maximum value of the minimum residual life value;
and transmitting the final processing optimization path to a remote welding processing terminal.
6. The welding processing path optimizing system based on the artificial intelligence is characterized by comprising a memory and a processor, wherein the memory stores a welding processing path optimizing method program based on the artificial intelligence, and when the welding processing path optimizing method program based on the artificial intelligence is executed by the processor, the following steps are realized:
Acquiring processing technology information of a current part to be welded, determining a processing procedure of the current part to be welded according to the processing technology information, and acquiring a processing path of the current part to be welded based on the processing procedure of the current part to be welded;
constructing a welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model;
calculating the residual life information of each motion unit in each motion gesture change path diagram based on the motion gesture change path diagram;
determining a final processing optimization path according to the residual life information of each motion unit in each motion gesture change path diagram, and transmitting the final processing optimization path to a remote welding processing terminal;
the method comprises the steps of constructing a welding equipment machining path simulation model, and obtaining a motion gesture change path diagram according to the machining path and the welding equipment machining path simulation model, and specifically comprises the following steps:
constructing an initial welding equipment processing path simulation model based on a virtual reality technology, and acquiring limit motion coordinate data information of each motion unit of the welding equipment;
Acquiring contour three-dimensional coordinate information of welding equipment, constructing a welding equipment three-dimensional model diagram according to the contour three-dimensional coordinate information of the welding equipment, and acquiring a blank model diagram of a current part to be welded;
acquiring the position relation between welding equipment and a part to be welded, and inputting the three-dimensional model diagram of the welding equipment and the blank model diagram of the current part to be welded into the initial welding equipment processing path simulation model according to the position relation between the welding equipment and the part to be welded;
and constructing a welding equipment machining path simulation model according to the limit motion coordinate data information of each motion unit of the welding equipment, the machining path and the initial welding equipment machining path simulation model, and acquiring a motion gesture change path diagram according to the welding equipment machining path simulation model.
7. The welding process path optimization system based on artificial intelligence of claim 6, wherein the remaining life information of each motion unit in each motion attitude change path graph is calculated based on the motion attitude change path graph, specifically comprising the steps of:
acquiring a motion gesture change path diagram based on a time sequence of each processing path diagram, and calculating energy loss information of each motion gesture change path diagram according to the motion gesture change path diagram based on the time sequence;
Acquiring estimated life information of each motion unit of the welding equipment, and acquiring energy loss information of each motion unit in the welding equipment according to the energy loss information of each motion gesture change path diagram;
obtaining service life consumption information of each motion unit in each motion gesture change path diagram based on the energy consumption information of each motion unit in the welding equipment;
and calculating the residual life information of each motion unit in each motion attitude change path diagram according to the estimated life information of each motion unit of the welding equipment and the life consumption information of each motion unit in each motion attitude change path diagram.
8. The welding process path optimization system based on artificial intelligence of claim 6, wherein a final process optimization path is determined based on remaining life information of each motion unit in each motion gesture change path graph, and the final process optimization path is transmitted to a remote welding process terminal, comprising the steps of:
constructing a residual life information sorting table, inputting the residual life information of each motion unit in each motion gesture change path diagram into the residual life information sorting table for sorting, and generating a plurality of residual life information sorting tables;
Obtaining the minimum residual life value of each residual life information sorting table, and comparing the minimum residual life value of each residual life information sorting table to obtain the maximum value of the minimum residual life value;
obtaining a corresponding processing path diagram according to the maximum value of the minimum residual life value, and obtaining the corresponding processing path diagram as a final processing optimization path according to the maximum value of the minimum residual life value;
and transmitting the final processing optimization path to a remote welding processing terminal.
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