Two-dimensional contour packing sequencing method based on depth learning

A technology of deep learning and two-dimensional outline, which is applied in the field of two-dimensional outline layout and sequencing based on deep learning, to achieve high efficiency and good layout effect

Active Publication Date: 2019-03-29
YANSHAN UNIV
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Problems solved by technology

[0006] In order to solve the shortcomings of the existing two-dimensional contour layout and sequencing methods and improve the final quality of layout, the present invention provides a two-dimensional contour layout and sequencing method based on deep learning

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  • Two-dimensional contour packing sequencing method based on depth learning
  • Two-dimensional contour packing sequencing method based on depth learning
  • Two-dimensional contour packing sequencing method based on depth learning

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[0017] In order to detail the technical content, structural features, achieved goals and effects of the present invention, the following will be described in detail in conjunction with the accompanying drawings.

[0018] Based on the intelligent optimization algorithm to solve the problem of layout and sequencing, it can improve the utilization rate of materials and reduce the cost of enterprises. The present invention designs a two-dimensional contour layout and sequencing method based on deep learning, such as figure 1 As shown, it specifically includes the following steps:

[0019] S1: Obtain the historical big data of m nesting parts in the nesting field;

[0020] S2: Preprocess the above-mentioned nesting historical big data, and calibrate the nesting sequence of all nested parts to obtain a nesting sequence matrix Y of the nested parts, where Y is a row vector of 1*m;

[0021] S3: Extract the geometric features of each nesting part in the historical data, including all...

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Abstract

The invention discloses a two-dimensional contour layout and sequencing method based on deep learning, which comprises: acquiring the layout history big data and pre-processing, and calibrating all the arranged parts in the historical data to obtain a layout order matrix Y. Extract the geometric features of each sampled part to obtain the geometric feature matrix X; sorting out the sampled sequence data set PRD-T; building the deep learning model Packing-Sort-Model; inputting the PRD-T data set, and traiiung to obtain, wherein The model Packing-Sort-Model that outputs the order of the parts tobe sampled; extracting the geometric features of the parts to be sampled, obtaining the geometric feature matrix A of the parts to be sampled; inputting the geometric feature matrix of the parts to be placed into the trained In the deep learning model; calculate the layout order matrix B of the parts to be placed; perform the nesting one by one according to the layout order, and complete the layout. The invention can realize the ordering of the parts to be discharged during the layout process, and has good layout effect and high efficiency.

Description

technical field [0001] The present invention relates to a method for contour layout and sequencing, in particular to a two-dimensional contour layout and sequencing method based on deep learning. Background technique [0002] Nesting problems commonly exist in the manufacturing industry. Solving this problem is of great help to improve material utilization and reduce enterprise costs. Based on this, topics related to layout problems have extremely important research value. [0003] At present, the most widely studied problem in the field of nesting is the two-dimensional contour nesting problem, which includes positioning problems and sequencing problems. For multiple parts, the nesting order is a major factor that determines the final nesting quality. Common two-dimensional contour layout and sequencing methods mainly include: layout and sequencing methods based on heuristic algorithms and layout and sequencing methods based on intelligent optimization algorithms. [0004]...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06N3/08G06Q10/043G06N3/045
Inventor 郭保苏胡敬文李锦瑞庄集超章钦
Owner YANSHAN UNIV
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