Energy-Efficient Prediction in Textile Manufacturing: Enhancing Accuracy and Data Efficiency With Ensemble Deep Transfer Learning

2026-01-19

IEEE Access

AI Summary

This study addresses the challenge of high energy consumption in traditional textile manufacturing and the limited applicability of deep neural networks (DNNs) in production output prediction due to data scarcity caused by the high cost of sensor deployment. To overcome this, the authors propose an Ensemble Deep Transfer Learning (EDTL) framework that uniquely integrates ensemble learning with transfer learning. EDTL leverages models pretrained on data-rich production lines and incorporates a feature alignment layer to enhance cross-line generalization, enabling effective knowledge transfer to data-scarce lines. Evaluated on a real-world textile factory dataset, EDTL achieves a 5.66% improvement in prediction accuracy and a 3.96% gain in robustness compared to conventional DNNs when only 20%–40% of training data is available, significantly enhancing both data efficiency and model performance.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Traditional textile factories consume substantial energy, making energy-efficient production optimization crucial for sustainability and cost reduction. Meanwhile, deep neural networks (DNNs), which are effective for factory output prediction and operational optimization, require extensive historical data—posing challenges due to high sensor deployment and data collection costs. To address this, we propose Ensemble Deep Transfer Learning (EDTL), a novel framework that enhances prediction accuracy and data efficiency by integrating transfer learning with an ensemble strategy and a feature alignment layer. EDTL pretrains DNN models on data-rich production lines (source domain) and adapts them to data-limited lines (target domain), reducing dependency on large datasets. Experiments on real-world textile factory datasets show that EDTL improves prediction accuracy by 5.66% and enhances model robustness by 3.96% compared to conventional DNNs, particularly in data-limited scenarios (20%–40% data availability). This research contributes to energy-efficient textile manufacturing by enabling accurate predictions with fewer data requirements, providing a scalable and cost-effective solution for smart production systems.

Problem

• energy-efficient manufacturing
• textile production
• data scarcity
• deep neural networks
• predictive modeling

Innovation

• Ensemble Deep Transfer Learning
• energy-efficient manufacturing
• data efficiency
• feature alignment
• textile production optimization