Prediction Model of Rotor Yarn Quality Based on CNN-LSTM

Hu, Zhenlong

doi:10.1155/2022/3955047

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…This model is based on inputs such as garment ease allowances, digital pressures, and fabric mechanical properties measured within a three-dimensional (3D) virtual environment. Reference [14] presented a prediction model for rotor yarn quality based on CNN and LSTM. This model accurately forecasts final yarn quality indicators given specific conditions.…”

Section: Production Process Inspection and Analysis Of Textile And Ap...mentioning

confidence: 99%

“…In [11], the ranked position weight and region approach method are used to overcome bottlenecks in the garment industry. Some scholars are currently focusing on bottleneck analysis based on data science methods such as convolutional neural network (CNN) and long short-term memory (LSTM) in the textile industry [12][13][14]. The above studies reflect somewhat the intelligent decision analysis of the textile and apparel production process.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Wang,

Liu,

2023

Machines

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There is a lack of high correlation and reuse potential among multiple manufacturing data for textiles and apparel. Moreover, the material flow traceability between production workstations is not clear, making it difficult to detect potential production bottlenecks. This paper proposes a knowledge graph embedded time serial data-driven bottleneck analysis of textile and apparel production processes. Firstly, a dynamic information association model is established to organize global manufacturing information, including the static data and time-series data features. Also, a textile-corpus-oriented knowledge extraction model is designed to construct a time-series knowledge graph for textile and apparel production (TKG4TA). Then, a temporal knowledge-driven production process bottleneck prediction model is presented based on manufacturing knowledge in the textile and apparel industry. Of these, textile knowledge is transformed into embeddings using a graph convolutional network (GCN). In turn, the context-associated information features are learned by the long short-term memory (LSTM) to predict the bottlenecks in the textile and apparel production process. Finally, a typical process flow in a shirt manufacturing workshop is used as a case study. It shows that the F1 value of the proposed method for named entity recognition and relationship extraction is up to 80.3%, and 50.6%, respectively. The performance of the proposed model for bottleneck prediction is improved by 8.2% and 14.92% compared to only the use of GCN or LSTM in the mean absolute error. This model may provide a solid foundation for the temporal knowledge-graph-driven bottleneck analysis of shirt manufacturing.

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Section: Production Process Inspection and Analysis Of Textile And Ap...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Wang,

Liu,

2023

Machines

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“…Moreover, the Adam algorithm has the capability to compute an adaptive learning rate for each parameter, enabling the model to accelerate convergence and minimize fluctuations. The high computational efficiency and low memory storage have positioned the Adam algorithm as one of the most frequently and popularly utilized optimizers [33,51,85,86]. As shown in Figure 3, most studies used the TensorFlow platform to develop work.…”

Section: Selection Of Optimizersmentioning

confidence: 99%

A Study of Optimization in Deep Neural Networks for Regression

et al. 2023

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Due to rapid development in information technology in both hardware and software, deep neural networks for regression have become widely used in many fields. The optimization of deep neural networks for regression (DNNR), including selections of data preprocessing, network architectures, optimizers, and hyperparameters, greatly influence the performance of regression tasks. Thus, this study aimed to collect and analyze the recent literature surrounding DNNR from the aspect of optimization. In addition, various platforms used for conducting DNNR models were investigated. This study has a number of contributions. First, it provides sections for the optimization of DNNR models. Then, elements of the optimization of each section are listed and analyzed. Furthermore, this study delivers insights and critical issues related to DNNR optimization. Optimizing elements of sections simultaneously instead of individually or sequentially could improve the performance of DNNR models. Finally, possible and potential directions for future study are provided.

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“…As a network sensitive to time sequences, the LSTM neural network has found widespread application in scenarios involving sequential data. Hu Zhenlong [16] optimized the input feature parameters of the LSTM neural network through the use of the convolutional neural networks (CNN) algorithm. Furthermore, they fine-tuned cotton fiber performance indicators and production parameters based on the sequence of yarn processing.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cotton Yarn Quality Based on Attention-GRU

Dai,

Jin,

et al. 2023

Applied Sciences

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With the diversification of spinning order varieties and process parameters, the conventional method of determining production plans through trial spinning no longer satisfies the processing requirements of enterprises. Currently, deficiencies exist in predicting spinning quality relying on manual experience and traditional methods. The back propagation (BP) neural network within the realm of deep learning theory faces challenges in handling time series data, while the long short-term memory (LSTM) neural network, despite its intricate mechanism, exhibits an overall lower predictive accuracy. Consequently, a more precise predictive methodology is imperative to assist production personnel in efficiently ascertaining cotton-blending schemes and processing parameters, thereby elevating the production efficiency of the enterprise. In response to this challenge, we propose an attention-GRU-based cotton yarn quality prediction model. By employing the attention mechanism, the model is directed towards the input features most significantly impacting yarn quality. Real-world performance indicators of raw cotton and process parameters are utilized to predict yarn tensile strength. A comparative analysis is conducted against prediction results of BP, LSTM, and gated recurrent unit (GRU) neural networks that do not incorporate the attention mechanism. The outcomes reveal that the GRU model enhanced with the attention mechanism demonstrates reductions of 56.3%, 38.5%, and 36.4% in root mean square error (RMSE), along with 0.367%, 0.158%, and 0.190% in mean absolute percentage error (MAPE), respectively. The model attains a coefficient of determination R-squared of 0.954, indicating a high degree of fitness. This study underscores the potential of the proposed attention-GRU model in refining cotton yarn quality prediction and its consequential implications for process optimization and enhanced production efficiency within textile enterprises.

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Prediction Model of Rotor Yarn Quality Based on CNN-LSTM

Cited by 10 publications

References 16 publications

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes

A Study of Optimization in Deep Neural Networks for Regression

Prediction of Cotton Yarn Quality Based on Attention-GRU

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