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Tool feature recognition method based on deep learning

A feature recognition and deep learning technology, applied in the field of tool feature recognition based on deep learning, can solve the problems of poor practicability and achieve good practicability

Active Publication Date: 2022-06-21
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to overcome the shortcomings of poor practicability of the existing non-standard special tool design method, the present invention provides a tool feature recognition method based on deep learning

Method used

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  • Tool feature recognition method based on deep learning
  • Tool feature recognition method based on deep learning
  • Tool feature recognition method based on deep learning

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Embodiment Construction

[0031] The specific steps of the tool feature recognition method based on deep learning of the present invention are as follows:

[0032] Step 1. Tool feature extraction: First, it is necessary to construct the input data of the tool feature extraction network, intercept the three views of the tool head with a resolution of 256×256, and process it into a binary grayscale pixel matrix to obtain 256×256× The structure information matrix of 3:

[0033] Y str =[[Y M,str ],[Y S,str ],[Y T,str ]] (1)

[0034] In the formula, Y str It is a pixel matrix of tool structure information, its dimension is 256×256×3, including three pixel matrices Y of front view, side view and top view M,str , Y S,str , Y T,str .

[0035] Construct the tool size information matrix, that is, take the binary representation of each size data of the tool (64-bit floating point type) as the tool size vector, classify it by length, angle, diameter, and sort by size to form a matrix with a dimension of 6...

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Abstract

The invention discloses a tool feature recognition method based on deep learning, which is used to solve the technical problem of poor practicability of the existing non-standard special tool design method. The technical solution is to start with image vision, dig out the relationship between the machining features of the workpiece and the two-dimensional engineering drawing of the tool, and realize the reuse of the tool design scheme. First, based on the convolutional deep belief network generation model, the structure information and size information of the two-dimensional engineering drawing are fused, and the feature extraction of the high-dimensional tool image is performed to obtain the low-dimensional tool feature vector; then combined with the CDBN discriminant model and transfer learning, in the reuse On the basis of tool feature extraction network parameters, the relationship between processing features and tool feature vectors is mined; finally, through the two steps of tool classification based on Sofmax and similarity matching based on the cosine method of included angle, the tool feature vector to historical tool design scheme is realized. The mapping is good in practicability.

Description

technical field [0001] The invention relates to a non-standard special tool design method, in particular to a tool feature recognition method based on deep learning. Background technique [0002] The design of non-standard special tools is a design process that relies heavily on design experience and knowledge. When designing a tool design scheme, designers usually compare new tool design requirements with previous tool design examples based on personal design experience. With the mature design experience and knowledge, the previous tool design examples are modified to meet the design requirements of the new tool. [0003] At present, the main methods of tool design knowledge reuse are: case-based reasoning method, expert system-based method, rule-based reasoning method and so on. The document "Research on a tool matching method based on case-based reasoning, Aviation Manufacturing Technology, 2015, vol.474(5), 77-81" discloses a tool-matching method based on case-based rea...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/44G06V10/764G06K9/62
CPCG06V10/44G06F18/24147G06F18/241
Inventor 王明微许青周竞涛
Owner NORTHWESTERN POLYTECHNICAL UNIV
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