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Case-based reasoning cutter recommendation method based on BP neural network weight optimization

A BP neural network and recommendation method technology, applied in the field of case reasoning tool recommendation based on BP neural network weight optimization, can solve the problem of low efficiency, achieve the effect of objective weight, avoid subjective scoring, and accurate retrieval

Pending Publication Date: 2021-12-24
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

The recommendation system can recommend tools based on the user's past information, but the efficiency is not very high

Method used

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  • Case-based reasoning cutter recommendation method based on BP neural network weight optimization
  • Case-based reasoning cutter recommendation method based on BP neural network weight optimization
  • Case-based reasoning cutter recommendation method based on BP neural network weight optimization

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Embodiment Construction

[0019] The flow chart of a kind of case reasoning tool recommendation method based on BP neural network weight optimization of the present invention is as follows figure 1 shown. The specific operation steps are as follows:

[0020] Step 1: Determine the tool attributes in the tool case library.

[0021] According to the processing information of the tool, determine the case attributes of the tool as processing material, workpiece material hardness, workpiece material state, processing surface type, and processing size;

[0022]

[0023] Step 2: Attribute normalization

[0024] The mean method is used for normalization, and the formula expression is:

[0025]

[0026] Step 3: Calculate the correlation coefficient of the attribute

[0027]

[0028] Calculated as

[0029]

[0030] Step 4: Collect user ratings on the importance of tool indicators through questionnaires

[0031]

[0032] Step 5: Calculate the subjective weight with the BWM method

[0033] Fir...

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Abstract

The invention discloses a case-based reasoning cutter recommendation method based on BP neural network weight optimization. The method comprises the following steps: inputting machining requirements; performing normalization processing on attributes of a tool case library; calculating a grey correlation coefficient; then calculating attribute weights; performing linear weighting on the attribute weights by adopting a BWM subjective weight and an entropy evaluation method objective weight respectively, optimizing the attribute weights through a BP neural network to obtain more objective data, and finally, calculating and sorting grey correlation degree, and selecting the most suitable cutter. According to the method, the weight of case reasoning is optimized, the index weight is more objective, and the retrieval accuracy is improved.

Description

technical field [0001] The invention relates to the field of case reasoning, in particular to a case reasoning tool recommendation method based on BP neural network weight optimization. Background technique [0002] The Industrial Internet is the realization of intelligent manufacturing. With technological innovation and industrial development, the importance of manufacturing service platforms has been continuously highlighted. However, for the manufacturing industry, the cutting tool is the core tool for its processing, and there are many kinds of cutting tools. How to recommend suitable cutting tools to users has good commercial value for the enterprise. The recommendation system can recommend tools based on the user's past information, but the efficiency is not very high. Case reasoning retrieves the most similar cases based on past experience. [0003] Case reasoning retrieves the most similar cases from the previous case base and provides references for them. When a s...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06Q10/06G06N3/04G06N3/08
CPCG06F16/9535G06Q10/0639G06N3/04G06N3/084
Inventor 伊亚聪郭宏闫献国胡孔耀徐延吕娜刘成波
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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