CN113095568B - Intelligent assembly method and platform - Google Patents
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- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
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- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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Abstract
The invention provides an intelligent assembly method and a platform, wherein the method comprises the following steps: selecting a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part, and calculating to obtain an optimal assembly sequence; importing an assembly model according to the optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics; after the assembly parts are led in, analyzing the characteristics of the parts for matching, and automatically assembling according to the assembly constraint to form a sub-assembly body; and judging whether the assembly of each part is correct or not by interference. By the scheme, automatic assembly of product parts can be realized, the assembly efficiency of the parts is improved, and the labor cost is reduced.
Description
Technical Field
The invention relates to the field of intelligent manufacturing, in particular to an intelligent assembling method and an intelligent assembling platform.
Background
With the coming of a new technological revolution and an industrial revolution, intelligent manufacturing becomes an important trend in the development of the current manufacturing industry. In the product assembly process, problems are increasing due to various types, different types and complex structures of products.
The current product assembly process often needs manual operation, according to the assembly sequence that sets for, with the help of erecting tool, assembles each part together, obtains final product, and its each link needs artifical the participation, and this kind of mode product assembly efficiency is low and the cost of labor is on the high side.
Disclosure of Invention
In view of this, the embodiment of the invention provides an intelligent assembly method and a platform, so as to solve the problems of low assembly efficiency and high labor cost of the existing product.
In a first aspect of an embodiment of the present invention, an intelligent assembling method is provided, including:
selecting a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part, and calculating to obtain an optimal assembly sequence;
importing an assembly model according to the optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics;
after the assembly parts are led in, analyzing the characteristics of the parts for matching, and automatically assembling according to the assembly constraint to form a sub-assembly body;
and judging whether the assembly of each part is correct or not by interference.
In a second aspect of an embodiment of the present invention, there is provided an intelligent assembly platform, including:
the assembly sequence planning module is used for selecting a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part and calculating to obtain an optimal assembly sequence;
the part loading module is used for importing an assembly model according to the optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics;
the automatic assembly module is used for analyzing the characteristics of the parts for matching after the assembly parts are led in, and automatically assembling the parts according to the assembly constraint to form a sub-assembly body;
and the interference detection module is used for judging whether the assembly of each part is correct or not in an interference manner.
In a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to the first aspect of the embodiments of the present invention.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method provided in the first aspect of the embodiments of the present invention.
In the embodiment of the invention, the optimal assembly sequence is calculated, the assembly model is introduced, the assembly characteristics are automatically identified, the assembly constraint and the assembly type are judged for automatic assembly, automatic interference detection is carried out after assembly is finished, and finally automatic assembly is realized, so that the assembly efficiency can be effectively improved, the assembly process is simplified, the labor cost is reduced, and the application in actual production is facilitated.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of an intelligent assembly method according to an embodiment of the present invention;
FIG. 2 is another schematic flow diagram of an intelligent assembly method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent assembling platform according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons skilled in the art without any inventive work shall fall within the protection scope of the present invention, and the principle and features of the present invention shall be described below with reference to the accompanying drawings.
The terms "comprises" and "comprising," when used in this specification and claims, and in the accompanying drawings and figures, are intended to cover non-exclusive inclusions, such that a process, method or system, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements.
Referring to fig. 1, fig. 1 is a schematic flow chart of an intelligent assembling method according to an embodiment of the present invention, including:
s101, selecting a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part, and calculating to obtain an optimal assembly sequence;
the condition of the pre-assembled parts refers to the condition of the parts to be assembled, and in the process of assembling the parts to form an assembly body, due to the difference of the number of the parts and the assembling requirement, the selected assembling sequence planning algorithm is also different.
The assembly sequence planning algorithm is a planning algorithm which shortens assembly time and reduces assembly complexity as much as possible on the premise of meeting geometric constraint, so that a reasonable and feasible assembly sequence is obtained.
When the number of the assembled parts exceeds a first preset value, selecting a parallel assembly sequence planning algorithm based on the sub-assembly body to calculate an optimal assembly sequence;
and when the number of the assembled parts is lower than a second preset value, selecting a quantum-behavior-based particle swarm assembly sequence planning algorithm to calculate an optimal assembly sequence.
The first preset value and the second preset value are both preset quantity values, so that reference is provided for selecting an assembly sequence planning algorithm, and the first preset value and the second preset value are only used for distinguishing different quantity values. It should be understood that the first preset value and the second preset value may be equal.
It should be noted that the parallel assembly sequence planning algorithm based on the sub-assembly body is to firstly judge parts capable of forming the sub-assembly body according to the definition of the sub-assembly body, obtain an effective sub-assembly body of the assembly body by combining a connection matrix and an interference matrix of the parts, perform parallelization analysis by using the obtained effective sub-assembly body, and finally obtain an optimized assembly sequence. The number of parts involved in assembly can be simplified, the planning process can be simplified, and the assembly efficiency can be improved.
The quantum-behavior-based particle swarm assembly sequence planning algorithm is characterized in that according to the characteristics of assembly sequence planning, the positions and the speeds of particles and related logical operations are defined in a sequencing space, a comprehensive evaluation function is constructed based on the set feasibility of an assembly sequence, the stability of assembly operation, the aggregation of the assembly operation and the assembly redirection times, and then the particles are quantized to enable the particles to traverse the whole space to search for a global optimal solution. The method can overcome the problem of local convergence, has few control parameters and simple process, and ensures the global optimum of the assembly planning.
S102, importing an assembly model according to an optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics;
the assembly model generally comprises part information, matching information and a spatial position relation under an assembly relation, an assembly file is generated based on the constructed assembly model, assembly characteristic information can be obtained through recognition according to the assembly file, and then corresponding parts are loaded.
Wherein, still include before leading into the assembly model: and traversing the assembly model and establishing a corresponding assembly information base.
The identified assembly features may result in assembly constraints and an assembly type for subsequent assembly of the part.
S103, after the assembly parts are led in, analyzing the characteristics of the parts for matching, and automatically assembling according to the assembly constraint to form a sub-assembly body;
the part characteristics comprise part matching information such as assembly constraint, assembly type and the like besides the characteristic information of the part. And automatically assembling the loaded parts according to the assembly constraint to form a sub-assembly, wherein the sub-assembly is formed by assembling a plurality of parts, and the sub-assembly can also be assembled to obtain a total assembly.
And S104, judging whether the assembly of each part is correct or not by interference.
Optionally, if the assembly is judged to be abnormal, the assembly problem is displayed; and if the assembly is judged to be correct, traversing to detect whether the assembly of the rest parts is correct or not until the assembly interference detection of all parts is finished.
It can be understood that the intelligent assembly realized by the embodiment of the invention mainly comprises the steps that the assembly model can identify the characteristics needing to be assembled according to a determined algorithm in the assembly process, the type needing to be matched is automatically selected through the characteristics, and then the automatic assembly is realized according to the correct matching type. If the standard component is assembled in the assembling process, feature modeling can be carried out by identifying the features of the standard component, the established standard component is automatically assembled according to the assembling conditions, interference detection is carried out in the assembling process, and whether the assembled parts are reasonable or not is verified.
In another embodiment of the present invention, as shown in fig. 2, an assembly sequence planning algorithm is selected according to the condition of assembly parts, if there are many assembly parts, parallel assembly sequence planning based on a sub-assembly body is selected, and if there are few assembly parts and the precision is high, particle swarm assembly sequence planning based on quantum behaviors is selected to obtain an optimal assembly sequence, and the assembly sequence is obtained and imported into an assembly model according to the assembly sequence. After the assembly characteristics of the parts are identified, automatic assembly is carried out, interference detection is carried out on the assembled parts, if the parts are matched, whether the current part is the last part or not is judged, if the current part is the last part, the assembly is finished, and if the current part is not the last part, automatic assembly is carried out again; if the parts are not matched, a problem is displayed to carry out correction and adjustment.
For the assembly model, a UF _ ASSEM _ ask _ root _ part _ occ function is used for acquiring a root node of the assembly tree, and a UF _ ASSEM _ ask _ part _ occ _ ch i l dren function is called for acquiring child node parts under the root node until all child node parts of the assembly model are traversed.
In the embodiment, the assembly information base used for pairing is established by traversing the assembly model information, the assembly constraint and the assembly type are judged by automatically identifying the assembly characteristics of the assembly model, automatic interference detection is carried out after assembly is finished, and finally automatic assembly is realized, so that the assembly efficiency can be effectively improved, and the labor cost is reduced.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by functions and internal logic of the process, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 3 is a schematic structural diagram of an intelligent assembly platform according to an embodiment of the present invention, where the platform includes:
the assembly sequence planning module 310 is configured to select a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part, and calculate to obtain an optimal assembly sequence;
when the number of the assembled parts exceeds a first preset value, selecting a parallel assembly sequence planning algorithm based on the sub-assembly body to calculate an optimal assembly sequence; and when the number of the assembled parts is lower than a second preset value, selecting a quantum-behavior-based particle swarm assembly sequence planning algorithm to calculate an optimal assembly sequence.
The part loading module 320 is used for importing an assembly model according to the optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics;
wherein the part loading module 320 comprises:
and the information base establishing unit is used for traversing the assembly model and establishing a corresponding assembly information base.
The automatic assembly module 330 is used for analyzing the characteristics of the parts for matching after the assembly parts are led in, and automatically assembling the parts according to the assembly constraint to form a sub-assembly body;
and the interference detection module 340 is used for judging whether the assembly of each part is correct or not in an interference manner.
Specifically, if the assembly is judged to be abnormal, the assembly problem is displayed; and if the assembly is judged to be correct, traversing and detecting whether the assembly of the rest parts is correct or not until the assembly interference detection of all the parts is finished.
It should be noted that the intelligent assembly platform is a virtual assembly platform designed based on the secondary development of the UG 8.0(Unigraphics NX) system.
Based on the platform that this embodiment provided, can automatic identification part information, select assembly information according to part information is automatic, plans the assembly sequence of optimizing automatically, judges part assembly route, moves the part to corresponding position, realizes reasonable assembly, improves assembly efficiency.
It is understood that, in one embodiment, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program performs steps S101 to S104 as in the first embodiment, and the processor implements product part automatic assembly when executing the computer program.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by instructing the relevant hardware through a program, where the program may be stored in a computer-readable storage medium, and when the program is executed, the program includes steps S101 to S104, and the storage medium includes, for example, ROM/RAM.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An intelligent assembly method, comprising:
selecting a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part, and calculating to obtain an optimal assembly sequence;
wherein selecting a corresponding assembly sequence planning algorithm according to the pre-assembled part condition further comprises:
when the number of the assembled parts exceeds a first preset value, selecting a parallel assembly sequence planning algorithm based on the sub-assembly body to calculate an optimal assembly sequence;
when the number of the assembly parts is lower than a second preset value, selecting a quantum-behavior-based particle swarm assembly sequence planning algorithm to calculate an optimal assembly sequence;
importing an assembly model according to the optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics;
after the assembly parts are led in, analyzing the characteristics of the parts for matching, and automatically assembling according to the assembly constraint to form a sub-assembly body;
and judging whether the assembly of each part is correct or not by interference.
2. The method of claim 1, wherein the importing assembly models according to the optimal assembly sequence further comprises, before building assembly files based on the assembly models:
traversing the assembly model and establishing a corresponding assembly information base.
3. The method of claim 1, wherein said interfering determining whether the parts are properly assembled further comprises:
if the assembly is judged to be abnormal, displaying the assembly problem;
and if the assembly is judged to be correct, traversing and detecting whether the assembly of the rest parts is correct or not until the assembly interference detection of all the parts is finished.
4. An intelligent assembly platform, comprising:
the assembly sequence planning module is used for selecting a corresponding assembly sequence planning algorithm according to the condition of the pre-assembled part and calculating to obtain an optimal assembly sequence;
wherein said selecting a corresponding assembly sequence planning algorithm based on pre-assembled part conditions further comprises:
when the number of the assembled parts exceeds a first preset value, selecting a parallel assembly sequence planning algorithm based on the sub-assembly body to calculate an optimal assembly sequence;
when the number of the assembly parts is lower than a second preset value, selecting a quantum-behavior-based particle swarm assembly sequence planning algorithm to calculate an optimal assembly sequence;
the part loading module is used for importing an assembly model according to the optimal assembly sequence, establishing an assembly file based on the assembly model, and loading corresponding assembly parts by identifying assembly characteristics;
the automatic assembly module is used for analyzing the characteristics of the parts for matching after the assembly parts are led in, and automatically assembling according to the assembly constraint to form a sub-assembly body;
and the interference detection module is used for judging whether the assembly of each part is correct or not in an interference manner.
5. The platform of claim 4, wherein the part loading module comprises:
and the information base establishing unit is used for traversing the assembly model and establishing a corresponding assembly information base.
6. The platform of claim 4, wherein the interference determining whether the parts are assembled correctly further comprises:
if the assembly is judged to be abnormal, displaying the assembly problem;
and if the assembly is judged to be correct, traversing and detecting whether the assembly of the rest parts is correct or not until the assembly interference detection of all the parts is finished.
7. An electronic device comprising a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the steps of the intelligent assembly method as claimed in any one of claims 1 to 3 are implemented when the computer program is executed by the processor.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the intelligent assembly method according to any one of claims 1 to 3.
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| US20160364670A1 (en) * | 2015-06-10 | 2016-12-15 | The Regents Of The University Of Michigan | Assembly system configuration |
| CN106547943A (en) * | 2016-09-26 | 2017-03-29 | 武汉开目信息技术股份有限公司 | A kind of assembling progress monitoring method and device based on threedimensional model |
| CN109636117A (en) * | 2018-11-15 | 2019-04-16 | 北京理工大学 | A kind of assembly information integrated management system |
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| CN111832170A (en) * | 2020-07-13 | 2020-10-27 | 重庆大学 | WebGL-based three-dimensional model assembling method, system and related device |
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