Assembly time evaluating method based on artificial neural network and virtual assembly

An artificial neural network and assembly time technology, which is applied in biological neural network models, special data processing applications, instruments, etc., can solve problems such as inability to evaluate system online optimization, poor effect, and long product assembly cycle

Inactive Publication Date: 2014-03-05
XIAN TECHNOLOGICAL UNIV
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Problems solved by technology

[0011] The purpose of the embodiment of the present invention is to provide an assembly time evaluation method based on artificial neural network and virtual assembly, aiming at solving the problem that the traditional assembly process design needs to establish a large number of physical models t

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  • Assembly time evaluating method based on artificial neural network and virtual assembly
  • Assembly time evaluating method based on artificial neural network and virtual assembly
  • Assembly time evaluating method based on artificial neural network and virtual assembly

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Embodiment Construction

[0086]In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0087] The application principle of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0088] Such as figure 1 As shown, the assembly time evaluation method based on the artificial neural network and virtual assembly of the embodiment of the present invention includes the following steps:

[0089] S101: Establish a virtual workbench, plan a DELMIA assembly sequence, plan a DELMIA path, and generate an assembly path;

[0090] S102: Use the constraint matrix method to generate the assembly sequence and establish the constraint relationship between t...

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Abstract

The invention discloses an assembly time evaluating method based on an artificial neural network and a virtual assembly. The method comprises the step of establishing a virtual working platform, planning a DELMIA assembly sequence and a DELMIA path and generating an assembly path, the step of generating the assembly sequence through a constraint matrix method and establishing the constraint relation among components, the step of preassembling the product components through a DELMIA assembly module, establishing a priority dismounting matrix, extracting the assembly sequence meeting the condition from the priority dismounting matrix and planning the assembly sequence through the strong data collecting capacity of the matrix, the step of establishing a virtual assembly work environment and the virtual assembly action process, the step of measuring the assembly path and calculating the assembly time, the step of establishing an assembly time evaluating model, the step of establishing the model and standardizing a sample, and the step of evaluating the sample. The assembly time evaluating method solves the problems that the product assembly period is long, cost is high, and the effect is poor.

Description

technical field [0001] The invention belongs to the field of computer application technology, in particular to an assembly time evaluation method based on artificial neural network and virtual assembly. Background technique [0002] With the development of computer-aided design and manufacturing technology, it has become a sharp tool that can change the low efficiency of the assembly industry in the process of product design and manufacturing. In the assembly manufacturing industry in the past, production and assembly could only be completed by assembly workers' accumulated assembly experience. Traditional optimization methods are only established at the stage of model imitation and stagnate. This kind of assembly process design optimization method is not only a problem of long experimental cycle, but also takes up too many production resources, and the uncertainty of the simulation process environment changes. Will reduce the convincing power of this model experiment metho...

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Application Information

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IPC IPC(8): G06F17/50G06N3/02
Inventor 曹岩杨丽娜杜江白瑀解彪
Owner XIAN TECHNOLOGICAL UNIV
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