LSTM-based intelligent generation method for dispensing graph
A graphics and dispensing technology, applied in neural learning methods, biological neural network models, instruments, etc., to save material costs, reduce training parameters, and shorten the test cycle.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0038] Example 1
[0039] Such as figure 1 As shown, an LSTM-based dispensing graphic intelligent generation method includes the following steps:
[0040] S1, data preparation: collect input feature and output feature of automatic mount data form a data set, divide the data set as training set, verification set and test set;
[0041] S2, establish an input feature property. Vector: Prepare the input feature to get the input feature property;
[0042] S3, establish an output feature property vector: Preprocessing the output feature to obtain the output feature property;
[0043] S4, establish a LSTM model: Figure 2-3 The LSTM structure and super parameter are designed to establish a LSTM model;
[0044] S5, training LSTM model: Design LSTM model loss function and optimization algorithm, using input feature property vectors and output feature attributes, to correct ultra-parameters until the end of the end of the final LSTM model;
[0045] S6, generating dot clamping pattern: Call ...
Example Embodiment
[0046] Example 2
[0047] Such as figure 1 As shown, an LSTM-based dispensing graphic intelligent generation method includes the following steps:
[0048] S1, data preparation: Collect the input feature and output feature of automatic mount data to form a data set, divide the data set as training set, verification set and test set; input features include: numerical features, order characteristics, and nominal features; output The characteristics are the coordinate sequence of displaced graphics; the total number of samples of the data set is not less than 1000 groups, the proportion of training sets, verification sets, and test sets is: 70%, 20%, 10%;
[0049] S2, establish an input characteristic property: Figure 4 As shown, the input feature is pre-processed to obtain an input feature attribute vector;
[0050] S21, define a numerical feature, and normalize the numerical feature, obtain a numerical feature vector; the numerical feature is a chip size and a needle size;
[0051] ...
Example Embodiment
[0061] Example 3
[0062] Such as figure 1 As shown, for a set of existing automatic mounting process parameters and corresponding dispensing patch effects (given according to GJB548B evaluation criteria), the data set is 1000, design a method of LSTM-based dispensing graphics intelligent generation method. , Including the following steps:
[0063] S1, data preparation: Collect the input feature and output feature of automatic mount data to form a data set, divide the data set as training set, verification set and test set; input features include: numerical features, order characteristics, and nominal features; output The characteristics are the coordinate sequence of displaced graphics; the total number of samples of the data set is not less than 1000 groups, the proportion of training sets, verification sets, and test sets is: 70%, 20%, 10%;
[0064] S2, establish an input characteristic property: Figure 4 As shown, the input feature is pre-processed to obtain an input feature...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.
© 2023 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap