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International Journal of Computer Integrated Manufacturing Volume 20, 2007 - Issue 8
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Original Articles

A neural network-based approach for optimising rubber extrusion lines

A. González Marcos Department of Electrical and Electronical Engineering, University of León, Campus de Vegazana s/n. 47001 – León, Castilla Y León, SpainCorrespondence[email protected]
,
A. V. Pernía Espinoza Department of Mechanical Engineering, University of La Rioja, c/ Luis de Ulloa, 20, 26004 – Logroño, La Rioja, Spain
,
F. Alba Elías Department of Mechanical Engineering, University of La Rioja, c/ Luis de Ulloa, 20, 26004 – Logroño, La Rioja, Spain
&
A. García Forcada Metzeler Automotive Profile Systems Iberica, S.A. La Portalada, Barrio de Varea, 26006 – Logroño, La Rioja, Spain
Pages 828-837 | Published online: 25 Jun 2008

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Susanta Kumar Dey, R. Ganesh Narayanan & G. Saravana Kumar. (2012) Computing the tensile behaviour of tailor welded blanks made of dual-phase steel by neural network-based expert system. International Journal of Computer Integrated Manufacturing 25:2, pages 158-176.
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Unchalisa Taetragool & Tiranee Achalakul. (2011) Method for failure pattern analysis in disk drive manufacturing. International Journal of Computer Integrated Manufacturing 24:9, pages 834-846.
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F. Javier Martínez-de-Pisón, AlphaV. Pernía, Julio Blanco, Ana González & Ruben Lostado. (2010) Control Model for an Elastomer Extrusion Process Obtained via a Comparative Analysis of Data Mining and Artificial Intelligence Techniques. Polymer-Plastics Technology and Engineering 49:8, pages 779-790.
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Claudia Barreto Cabrera, Joaquín B. Ordieres Meré, Manuel Castejon Limas & Juan José del Coz Díaz. (2010) A data-driven manufacturing support system for rubber extrusion lines. International Journal of Production Research 48:8, pages 2219-2231.
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Articles from other publishers (20)

Christoph Thon, Marvin Röhl, Somayeh Hosseinhashemi, Arno Kwade & Carsten Schilde. (2023) Artificial Intelligence and Evolutionary Approaches in Particle Technology. KONA Powder and Particle Journal.
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Sujit Sharma, Debottam Goswami, Mohit Goswami, Arghya Deb, Bhojraj Padhan & Santanu Chattopadhyay. (2023) Computational fluid dynamics modeling of multicomponent elastomeric complex profile while flowing through extrusion die. Chemical Engineering Journal 451, pages 138756.
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Erik Rohkohl, Malte Schönemann, Yury Bodrov & Christoph Herrmann. (2022) A data mining approach for continuous battery cell manufacturing processes from development towards production. Advances in Industrial and Manufacturing Engineering 4, pages 100078.
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Ge He, Tao Luo, Yagu Dang, Li Zhou, Yiyang Dai & Xu Ji. (2021) Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity. RSC Advances 11:2, pages 817-829.
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Shuihua Zheng, Kaixin Liu, Yili Xu, Hao Chen, Xuelei Zhang & Yi Liu. (2020) Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes. Sensors 20:3, pages 695.
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Alexander Lewis Bowler, Serafim Bakalis & Nicholas James Watson. (2020) A review of in-line and on-line measurement techniques to monitor industrial mixing processes. Chemical Engineering Research and Design 153, pages 463-495.
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Vicente García, J. Salvador Sánchez, Luis Alberto Rodríguez-Picón, Luis Carlos Méndez-González & Humberto de Jesús Ochoa-Domínguez. (2018) Using regression models for predicting the product quality in a tubing extrusion process. Journal of Intelligent Manufacturing 30:6, pages 2535-2544.
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Wenjian Zheng, Yi Liu, Zengliang Gao & Jianguo Yang. (2018) Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers. Chemometrics and Intelligent Laboratory Systems 180, pages 36-41.
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Wenjian Zheng, Xuejin Gao, Yi Liu, Limei Wang, Jianguo Yang & Zengliang Gao. (2017) Industrial Mooney viscosity prediction using fast semi-supervised empirical model. Chemometrics and Intelligent Laboratory Systems 171, pages 86-92.
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Weiya Jin, Yi Liu & Zengliang Gao. (2017) Fast property prediction in an industrial rubber mixing process with local ELM model. Journal of Applied Polymer Science 134:41, pages 45391.
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Ruben Urraca, Alpha Pernía-Espinoza, Ismael Díaz & Andres Sanz-Garcia. (2016) Practical methodology for validating constitutive models for the simulation of rubber compounds in extrusion processes. The International Journal of Advanced Manufacturing Technology 90:5-8, pages 2377-2387.
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Yi Liu, Yu Fan, Lichun Zhou, Fujiang Jin & Zengliang Gao. (2016) Ensemble Correntropy-Based Mooney Viscosity Prediction Model for an Industrial Rubber Mixing Process. Chemical Engineering & Technology 39:10, pages 1804-1812.
Crossref
Yi Liu & Zengliang Gao. (2015) Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models. Journal of Applied Polymer Science 132:6, pages n/a-n/a.
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E. Sodupe-Ortega, R. Urraca, J. Antonanzas, M. Alia-Martinez, A. Sanz-Garcia & F. J. Martínez-de-Pisón. 2015. Project Management and Engineering. Project Management and Engineering 235 245 .
Ruben Urraca-Valle, Enrique Sodupe-Ortega, Alpha Pernía-Espinoza & Andres Sanz-Garcia. 2014. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13 171 180 .
A. González-Marcos, J. Ordieres-Meré, V. Muñoz-Munilla & F. Alba-Elías. (2011) An intelligent supervision system for open loop controlled processes. Journal of Intelligent Manufacturing 24:1, pages 15-24.
Crossref
Yan-chen Gao, Jun Ji, Hai-qing Wang & Ping Li. (2010) Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling. Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling.
J. Ordieres-Meré, F. J. Martínez-de-Pisón-Ascacibar, A. González-Marcos & I. Ortiz-Marcos. (2008) Comparison of models created for the prediction of the mechanical properties of galvanized steel coils. Journal of Intelligent Manufacturing 21:4, pages 403-421.
Crossref
Diancai Yang, Yi Liu, Yugang Fan & Haiqing Wang. (2009) Online prediction of Mooney viscosity in industrial rubber mixing process via adaptive kernel learning method. Online prediction of Mooney viscosity in industrial rubber mixing process via adaptive kernel learning method.
Enrique Alegre, Joaquín Barreiro, Manuel Castejón & Sir Suarez. 2008. Image Analysis and Recognition. Image Analysis and Recognition 1101 1110 .

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