Quality prediction of internal thread cold extrusion based on genetic algorithm optimized BP neural network

Hou, Hongling; Zhang, Guang-Peng; Chen, Xin; Zhao, Yongqiang

doi:10.1177/16878132221089148

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Researchers applied various machine learning methods to realize product quality prediction, Neural Network is a common and useful algorithm. Hou et al ( 2022 ) established a prediction model based on back-propagation Neural Network and Genetic Algorithm to predict the quality of the extrusion thread. The effectiveness of the method was validated by an industrial dataset.…”

Section: Related Workmentioning

confidence: 99%

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

et al. 2023

Self Cite

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Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy in real industrial scenarios. It is difficult to gather the data to train a personalized model without compromising data privacy. To address this issue, we proposed a Federated Transfer Learning framework based on Auxiliary Classifier Generative Adversarial Networks named ACGAN-FTL. In the framework, Federated Learning (FL) trains a global model on decentralized datasets of the clients with data privacy-preservation and Transfer Learning (TL) transfers the knowledge from the global model to a personalized model with a relatively small data volume. ACGAN acts as a data bridge to connect FL and TL by generating similar probability distribution data of clients since the client datasets in FL cannot be directly used in TL for data privacy-preservation. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the performance of the proposed framework. The results show that ACGAN-FTL can not only obtain acceptable performance on 0.81 accuracy, 0.86 precision, 0.74 recall, and 0.79 F1 but also ensure data privacy-preservation in the whole learning process. Compared to the baseline method without FL and TL, the former metrics have increased by 13%, 11%, 16%, and 15% respectively. The experiments verify that the performance of the proposed ACGAN-FTL framework fulfills the requirements of industrial scenarios.

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Section: Related Workmentioning

confidence: 99%

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

et al. 2023

Self Cite

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“…Each crab was cooked and cut open after completion of image acquisition to disassemble the edible parts of crab paste, yolk, and meat, and the proportion of edible parts to the total weight of each sample was recorded; a large proportion indicates superior quality, which is a more accurate rating method than observing the quality with the naked eye. The four measured data were combined with the rating results to build a dataset [33][34][35] for subsequent use.…”

Section: Equipment and Materialsmentioning

confidence: 99%

Quality Grading of River Crabs Based on Machine Vision and GA-BPNN

Wang

Zhu²,

et al. 2023

Sensors

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The prices of different quality river crabs on the market can vary several times. Therefore, the internal quality identification and accurate sorting of crabs are particularly important for improving the economic benefits of the industry. Using existing sorting methods by labor and weight to meet the urgent needs of mechanization and intelligence in the crab breeding industry is difficult. Therefore, this paper proposes an improved BP neural network model based on a genetic algorithm, which can grade the crab quality. We comprehensively considered the four characteristics of crabs as the input variables of the model, namely gender, fatness, weight, and shell color of crabs, among which gender, fatness, and shell color were obtained by image processing technology, whereas weight is obtained using a load cell. First, mature machine vision technology is used to preprocess the images of the crab's abdomen and back, and then feature information is extracted from the images. Next, genetic and backpropagation algorithms are combined to establish a quality grading model for crab, and data training is conducted on the model to obtain the optimal threshold and weight values. Analysis of experimental results reveals that the average classification accuracy reaches 92.7%, which proves that this method can achieve efficient and accurate classification and sorting of crabs, successfully addressing market demand.

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“…This stage of fabrication is critical for producing high‐quality polymer products that meet the desired specifications and performance requirements 1 . The extrusion process requires careful monitoring of various parameters, such as temperature, pressure, and flow rate, 2 to ensure that the material is processed correctly and consistently. By monitoring these parameters, manufacturers can identify potential issues early on and take corrective action to prevent defects and improve product quality.…”

Section: Introductionmentioning

confidence: 99%

Multi‐source data‐driven approach for prediction of melt density during polymer compounding

Zhang,

Chen,

Zhang

et al. 2024

Polymer Engineering & Sci

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Melt density is a crucial quality indicator for polymer composites, yet real‐time measurement remains challenging due to processing complexities. While existing machine learning methods offer solutions, they often fall short in complex compounding scenarios. This study presents a novel multi‐source data‐driven approach for measuring melt density in polycarbonate/acrylonitrile butadiene styrene blends. By incorporating ultrasonic, near‐infrared, and Raman spectra data acquired during melt processing, a deep separable convolutional neural network model is developed to predict melt density accurately. The model effectively fuses multi‐source data to establish the mapping relationship between input data and melt density output. Results demonstrate the model's ability to monitor melt density in real‐time, achieving a prediction accuracy with RMSE and R2 indexes of 0.005 g/cm3 and 0.9841, respectively. The proposed approach outperforms existing methods, showcasing its effectiveness and superiority in melt density prediction for polymer compounding processes.Highlights Establishment of the real‐time monitoring system for polymer extrusion processes. Conversion of multi‐sensor signals into time‐frequency images using wavelet decomposition. Fusion of sensor data into a three‐channel tensor‐image. Development of a data‐driven DSCNN model for predicting melt density. Implementation for online monitoring and prediction in PC/ABS compounding system.

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Quality prediction of internal thread cold extrusion based on genetic algorithm optimized BP neural network

Cited by 6 publications

References 12 publications

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

Quality Grading of River Crabs Based on Machine Vision and GA-BPNN

Multi‐source data‐driven approach for prediction of melt density during polymer compounding

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