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Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing.

Ray-I Chang¹, Jia-Ying Lin¹, Yu-Hsin Hung²

¹Department of Engineering Science and Ocean Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

This study introduces a query-based learning method for textile manufacturing, improving efficiency and reducing resource waste. The new regression algorithm achieved a lower mean squared error, enhancing product quality prediction.

Keywords:

data communication ensemble learning predictive maintenance process parameter textile

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Area of Science:

Textile Manufacturing
Data Analytics
Machine Learning

Background:

Traditional textile manufacturing relies on trial-and-error for parameter adjustment, leading to inefficiencies.
Optimizing production parameters is crucial for enhancing textile product quality and reducing resource waste.

Purpose of the Study:

To develop an efficient and economical method for textile manufacturing using data analytics.
To improve the prediction of product quality by leveraging existing manufacturing data.

Main Methods:

A query-based learning method in regression analytics was proposed, utilizing existing manufacturing data.
The model training involved dynamic interactions with its solution space and the incorporation of new training patterns derived from quality factor validation.

Main Results:

The proposed query-based regression algorithm achieved a mean squared error of 0.0153.
This performance is superior to traditional regression methods, which had an average mean squared error of 0.020.

Conclusions:

The developed query-based learning method significantly enhances the efficiency and effectiveness of textile manufacturing.
The trained model, deployed as an API, offers cloud-based analytics and an auto-notification service for improved quality control.