Part machining surface prediction method based on space-time full convolutional recurrent neural network

A cyclic neural network and parts processing technology, applied in the field of image processing, can solve problems such as inability to share information between layers, meet the requirements of production takt, improve prediction accuracy and training speed, overcome the insufficiency of fitting ability and feature extraction ability Effect

Pending Publication Date: 2021-04-16
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] The present invention aims to overcome the problem that information cannot be shared between layers in the convolutional neural network in the prior art, and provides a part processing surface prediction method based on the spatio-temporal full convolutional neural network, using the convolution of the full convolutional network The layer extracts the global features and local detail features, and fuses the

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  • Part machining surface prediction method based on space-time full convolutional recurrent neural network
  • Part machining surface prediction method based on space-time full convolutional recurrent neural network
  • Part machining surface prediction method based on space-time full convolutional recurrent neural network

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Example Embodiment

[0051]Example 1

[0052]A surface prediction method based on a space-time full convolution cycle neural network, the method comprising the following steps

[0053](1) Generate a grayscale image: Measure the top surface of the engine cylinder by using three-dimensional high resolution surface morphology measurement technology, sequentially obtain three-dimensional high-density point cloud data, which is three-dimensional coordinates in x, y, z The format shows that the X, Y direction resolution is 150μm, the Z direction measurement accuracy is 1 μm, the point cloud data density can reach 40 measurement points per square millimeter, the total number of points can reach 1 million, using the 3D point cloud Data reflects the high value mapping of the surface topography of the part to the pixel gray value, generating a grayscale image of the top surface processing process of the cylinder;

[0054](2) Get a subset: The image coordinates (211, 511) are initial sampling positions, 50 × 50 pixels are ...

Example Embodiment

[0061]Example 2

[0062]A component processing surface prediction method based on a time-space full convolution cycle neural network, the method comprising the following step (1) to acquire grayscale image: This embodiment is a direct-obtained bearing side gray image by industrial HD camera shooting. Therefore, three-dimensional high-density point cloud data conversion is not required.

[0063](2) Get a subset: The image coordinates (668, 300) are initial sampling positions, 64 × 64 pixels are initial sampling size, 32 pixels are sampled interval isometric samples 10 times, resulting in 10 samples, sampling resultsFigure 7 Indicated.

[0064](3) Space autocorrelation analysis: Analyze the spatial autocorrelation of 10 sheets. After the 10 sub-maps are vacuied, the corresponding Molan index is calculated separately, and the maximum 256 × 256 pixels of Molan Index are the highest dimension as the spatial autocorrelation.

[0065](4) grayscale image slip window cut: Take 256 × 256 pixels as the sl...

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Abstract

The invention relates to the technical field of image processing, and discloses a part machining surface prediction method based on a space-time full convolutional recurrent neural network, which aims at solving the problem that information cannot be shared between layers in a convolutional neural network in the prior art, and comprises the following steps of: (1) generating a grayscale image; (2) obtaining a sub-image set; (3) making spatial autocorrelation analysis; (4) segmenting a sliding window of the grayscale image; (5) making time autocorrelation analysis; and (6) constructing the space-time full convolutional recurrent neural network. Global features and local detail features are extracted by using a convolutional layer of a full convolutional network, the extracted global features and local detail features are fused through a skip layer structure, and a ConvLSTM unit is introduced into the full convolutional network (FCN). The network has certain memorability on the basis that global feature and local feature extraction and fusion can be completed, and then the part machining surface prediction precision is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for predicting the processing surface of a part based on a spatio-temporal fully convolutional cyclic neural network. Background technique [0002] Part surface topography is a comprehensive reflection of process parameters and process information, and is also the key to ensuring product quality and realizing design functions. For the same processing condition, the continuous measurement of the surface topography data of multiple parts contains the evolution information of the processing process, which can realize the topography prediction. By mining the process information of the processing system contained in the surface topography data of historical parts, the change rule of part topography can be grasped, and the surface topography of future processed parts can be predicted, and then the surface tolerance and tool wear status can be analyzed according to the...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T17/00G06N3/04G06N3/08
Inventor 邵益平谭健
Owner ZHEJIANG UNIV OF TECH
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