A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process

Xu, Qingquan; Dong, Jie; Peng, Kaixiang; Yang, Xuyan

doi:10.1016/j.eswa.2023.121682

Cited by 8 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the traditional diagnostic methods based on mathematical model analysis, in recent years, with the development of artificial intelligence technology, intelligent diagnostic methods have been applied to the field of multivariate process quality diagnosis, and the diagnostic methods based on artificial neural networks (ANNs) [21][22][23], Bayesian networks [24][25][26], support vector machines (SVMs) [27][28][29], etc. have been widely applied.…”

Section: Intelligent Diagnosis Methodsmentioning

confidence: 99%

Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on the Optimal Typical Correlated Component Pair Group

Niu,

Cheng,

Qiu

2024

Processes

View full text Add to dashboard Cite

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new approach based on the optimal typical correlated component pair group (OTCCPG) is proposed. Firstly, the theorem of correlation decomposition is proved to decompose the correlation of all the quality components as serial correlations of component pairs, and then according to the transitivity of correlations of component pairs, the decomposition result is represented by a correlation set of typical correlated component pairs. Finally, an algorithm for OTCCPG based on the maximum correlation spanning tree (MCST) is proposed, and T2 control charts to monitor the correlations of component pairs in OTCCPG are established to form the correlation diagnostic system. Theoretical analysis and practice prove that the proposed method could reduce the space complexity of the diagnostic system greatly.

show abstract

Section: Intelligent Diagnosis Methodsmentioning

confidence: 99%

Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on the Optimal Typical Correlated Component Pair Group

Niu,

Cheng,

Qiu

2024

Processes

View full text Add to dashboard Cite

show abstract

“…Consequently, the likelihood of production sys-Engineering Management in Production and Services tem failures and the associated hazards related to product quality have escalated. When faults occur in the production process, specific product quality indicators can fluctuate, leading to subpar quality (Xu et al, 2024).…”

Section: Enhance Quality Control Process By Aimentioning

confidence: 99%

“…The rapid advancement of information technologies makes it crucial to utilise them for monitoring and achieving stable, precise control over industrial processes and product quality (Xu et al, 2024). To address challenges in industrial process monitoring, fault diagnosis, and product quality control, experts and scholars have proposed the application of AI (Hartung et al, 2022;Zeng et al, 2022;Xu et al, 2024), including GAI as evidenced in recent studies (Narasimhan, 2023;Raja, 2023;Wang et al, 2019). The utilisation of GAI holds the potential to enhance quality control processes by effectively detecting and identifying defects and anomalies in various products.…”

Section: Enhance Quality Control Process By Aimentioning

confidence: 99%

Generative AI in the Manufacturing Process: Theoretical Considerations

Doanh,

Dufek,

Ejdys

et al. 2023

Engineering Management in Production and Services

View full text Add to dashboard Cite

The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.

show abstract

“…In addition to the traditional diagnostic methods based on mathematical model analysis, in recent years, with the development of artificial intelligence technology, intelligent diagnostic methods are applied to the field of multivariate process quality diagnosis, and the diagnostic methods based on artificial neural network (ANN) 25 – 28 , Bayesian network 29 – 32 , support vector machine (SVM) 33 – 35 , etc. have been widely applied.…”

Section: Introductionmentioning

confidence: 99%

A multivariate process quality correlation diagnosis method based on grouping technique

Niu,

Cheng,

Qiu

2024

Sci Rep

View full text Add to dashboard Cite

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new diagnostic method based on quality component grouping is proposed. Firstly, three theorems describing the properties of the covariance matrix of multivariate process quality are established based on the statistical viewpoint of product quality, to prove the correlation decomposition theorem, which decomposes the correlation of all the quality components into a series of correlations of components pairs, and then by using the factor analysis method, all quality components are grouped in order to maximize the correlations in the same groups and minimize the ones between different groups. Finally, on the basis of correlations between different groups are ignored, T2 control charts of component pairs in the same groups are established to form the diagnostic model. Theoretical analysis and practice prove that for the multivariate process quality whose the correlations between different components vary considerably, the grouping technique enables the size of the correlation diagnostic model to be drastically reduced, thus allowing the proposed method can be used as a generalized theoretical model for the correlation diagnosis.

show abstract

A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process

Cited by 8 publications

References 43 publications

Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on the Optimal Typical Correlated Component Pair Group

Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on the Optimal Typical Correlated Component Pair Group

Generative AI in the Manufacturing Process: Theoretical Considerations

A multivariate process quality correlation diagnosis method based on grouping technique

Contact Info

Product

Resources

About