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Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network

An injection molding process, BP neural network technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as large warping deformation

Inactive Publication Date: 2016-10-26
GUANGXI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0003] Aiming at the deficiencies of the prior art, the present invention proposes an injection molding process for automotive interior panels based on BP neural network, and aims at the relatively large warping deformation during CAE analysis during injection molding, and seeks to optimize the minimum warping of plastic parts Optimum injection molding process parameters, reducing the number of actual injection molding trials and trial costs, reducing workload and improving work efficiency

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  • Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network
  • Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network
  • Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network

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Embodiment Construction

[0016] The present invention will be described in detail below, and the description in this part is only exemplary and explanatory, and should not have any limiting effect on the protection scope of the present invention.

[0017] A kind of automotive interior panel injection molding process based on BP neural network, comprises the following steps:

[0018] A. Establish a plastic mold flow CAE analysis model;

[0019] B. Use moliflow2015 software to simulate the injection molding process parameters of plastic parts, and determine the settable process parameters that need to be considered to solve the problem of excessive product warpage by applying CAE analysis and optimization;

[0020] C. Use Mintab to carry out the Tugachi method plane orthogonal test design on the above-mentioned process parameters, and use CAE software for warpage analysis to obtain the warpage deformation results at each factor level, use Mintab to analyze the influence weight of factors, and initially ...

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Abstract

The invention discloses an automotive trim panel injection moulding technology based on a BP (Back Propagation) neural network. The automotive trim panel injection moulding technology comprises the following steps: establishing a plastic part mold flow CAE (Computer Aided Engineering) analysis model; simulating the injection moulding technical parameters of the plastic part, and determining technical parameters which can be set; preliminarily optimizing the optimum value domain, which is improved on the basis of buckling, of each technical parameter; carrying out prediction model training on an obtained experiment result by the BP neural network to obtain a prediction model; and on the basis of the optimum value domain, designing a quadratic stereo orthorhombic optimum value domain optimization scheme, applying the neural network model to predict, and directly optimizing an optimal parameter domain value. The automotive trim panel injection moulding technology aims at the problem of large buckling deformation during CAE analysis when injection imoulding is carried out, the minimum buckling and the optimal injection moulding technical parameters of the plastic part are optimized, practical injection moulding mold testing frequencies and mold testing cost are lowered, workload is lowered, and working efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of automobile manufacturing, in particular to a BP neural network-based injection molding process for automobile interior panels. Background technique [0002] After the injection molding of automotive interior panels, large warping deformation often occurs. If the large warping deformation is not resolved, it is necessary to conduct a comprehensive analysis of various factors in the injection molding process and related process parameters. Work Larger volume, lower efficiency. Contents of the invention [0003] Aiming at the deficiencies of the prior art, the present invention proposes an injection molding process for automotive interior panels based on BP neural network, and aims at the relatively large warping deformation during CAE analysis during injection molding, and seeks to optimize the minimum warping of plastic parts The optimal injection molding process parameters reduce the number of actual i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/15G06F30/367
Inventor 李炜黄力李瑞娟罗永有
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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