US 2026/0115985 A1Published application

Method for Predicting Multiple Qualities of Finished Products Using Feature Encoding with Machine Learning and Production Equipment

AssigneeNational Taiwan Normal University

InventorsKun-Cheng Ke, Jui-Chih Wang

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Abstract

The present invention provides a method for predicting multiple qualities of finished products using feature encoding with machine learning, applicable for monitoring the manufacturing process in a production equipment. The method includes an experimental step, a data preprocessing step, a feature learning step, and a model calibration step. In experimental step, the production equipment produces a finished product under at least one process condition and obtains a detection data stream. In data preprocessing step, an autoencoder encodes the detection data stream to obtain a feature data stream, from which a quality index can be calculated. In feature learning step, a prediction module learns from the feature data stream and the quality index. In model calibration step, the prediction module is trained to learn the quality of the finished product to enhance the accuracy of the predictions made by the prediction module.

Claims10

Dependent claims

A method for predicting multiple qualities of finished products using feature encoding with machine learning, applicable for monitoring the manufacturing process in a production equipment, comprising:

an experimental step, wherein at least one process condition is set on the production equipment to manufacture a finished product, and a detection module is installed on the production equipment to obtain a detection data stream related to the finished product, which contains at least one quality indicator of the finished product;
a data preprocessing step, wherein an autoencoder encodes the detection data stream to obtain a feature data stream, and the detection data stream is derived from data obtained through monitoring the manufacturing process;
a feature learning step, wherein a prediction module that includes a multilayer perceptron learns from the feature data stream;
and
a model calibration step, wherein the prediction module with the multilayer perceptron is used to learn and compare the quality of the finished product, and if the comparison results deviate beyond a preset threshold, the feature learning step is re-executed to allow the prediction module to simulate and refine the relationship between the feature data stream and the finished product quality, thereby enabling the prediction module to predict finished product quality and enhances the accuracy of its predictions.

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Dependencies are inferred from the claim text.