Yarn Strength CV Prediction Using Principal Component Analysis and Automatic Relevance Determination on Bayesian Platform

Zhang, Baowei; Song, Jiuxiang; Wang, Yonghua; Wei, Jingdian; Jiang, Hao; Feng, Lizeng

doi:10.1007/s40034-021-00216-2

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Used at first to predict stock prices Huang et al (2005), Tay and Cao (2001) and Wei (2012), it is considered a robust tool used in different domains such as modelling (Mart ınez-Mart ınez et al, 2017), optimisation (Garmsiri and Jalal, 2014), engineering (Ashrafi et al, 2010) and prediction. In this study, we will use machine learning regression models and because parameters of OEE are correlated, this prediction will be done using regression models namely: automatic relevance determination (ARD) (Zhang et al, 2021), Bayesian Ridge Regression (Wheeler and Calder, 2006) and, Least Angle Regression (LAR). In the first section, we will see related works for lean six sigma versus OEE, machine learning regression models; at the end of this section we will see the contribution of this paper in the field of lean six sigma and continuous improvement processes.…”

Section: Continuous Improvement and Machine Learningmentioning

confidence: 99%

“…, 2010) and prediction. In this study, we will use machine learning regression models and because parameters of OEE are correlated, this prediction will be done using regression models namely: automatic relevance determination (ARD) (Zhang et al. , 2021), Bayesian Ridge Regression (Wheeler and Calder, 2006) and, Least Angle Regression (LAR).…”

Section: Introductionmentioning

confidence: 99%

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Contribution of machine learning in continuous improvement processes

Imane

Aoula

Achouyab

2022

JQME

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PurposeThe aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user.Design/methodology/approachThis work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions.FindingsThe three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression.Originality/valueAs the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO2 trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator “used to measure manufacturing efficiency” which is the overall equipment effectiveness used in the authors’ case to measure the improvement of the production system.

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Section: Continuous Improvement and Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contribution of machine learning in continuous improvement processes

Imane

Aoula

Achouyab

2022

JQME

View full text Add to dashboard Cite

show abstract

“…Chaudhari et al studied the speed drafting system's effect on yarn strength CV [14]; Rutkowski studied the yarn treated by yarn purification technology and yarn strength CV changes [15]. B. Zhang et al proposed a P-ARD algorithm for yarn strength CV prediction by combining principal component analysis (PCA) with automatic relevance determination (ARD) [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Yarn Quality Based on Actual Production

Zhang¹,

Zhang²,

Xu³

2023

Journal of Internet Technology

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<p>In recent decades, the neural network approach to predicting yarn quality indicators has been recognized for its high accuracy. Although using neural networks to predict yarn quality indicators has a high accuracy advantage, its relationship understanding between each input parameter and yarn quality indicators may need to be corrected, i.e., increasing the raw cotton strength, the final yarn strength remains the same or decreases. Although this is normal for prediction algorithms, actual production need is more of a trend for individual parameter changes to predict a correct yarn, i.e., raw cotton strength increase should correspond to yarn strength increase. This study proposes a yarn quality prediction method based on actual production by combining nearest neighbor, particle swarm optimization, and expert experience to address the problem. We Use expert experience to determine the upper and lower limits of parameter weights, the particle swarm optimization finds the optimal weights, and then the nearest neighbor algorithm is used to calculate the predicted values of yarn indexes. Finally, the current problems and the rationality of the method proposed in this paper are verified by experiments.</p> <p> </p>

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Yarn Hairiness Prediction by Generalized Regression Neural Network based on Harris Hawk Optimization

Song

Fan

2022

J. Inst. Eng. India Ser. E

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Yarn Strength CV Prediction Using Principal Component Analysis and Automatic Relevance Determination on Bayesian Platform

Cited by 5 publications

References 19 publications

Contribution of machine learning in continuous improvement processes

Contribution of machine learning in continuous improvement processes

Prediction of Yarn Quality Based on Actual Production

Yarn Hairiness Prediction by Generalized Regression Neural Network based on Harris Hawk Optimization

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