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Uruk Z, Kiraz A. Artificial intelligence based prediction models for rubber compounds. JOURNAL OF POLYMER ENGINEERING 2022. [DOI: 10.1515/polyeng-2022-0166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Abstract
In the rubber industry, rheometric properties are critical in defining processing times and temperatures. These parameters of rubber compounds are determined by time-consuming and expensive laboratory studies performed in a rheometer. Artificial intelligence approaches, on the other hand, may be used to estimate rheometric properties in seconds without the need for any samples or laboratory experiments. In this research, artificial neural network, Gaussian process regression, and support vector regression techniques are used to predict minimum and maximum torque, 30% and 60% cure time of a rubber compound using both process parameters and raw material composition as input. The dataset comprises 1128 batches of the selected rubber compound. A detailed sensitivity analysis is performed to determine the best performing hyperparameters and the prediction performances are expressed as mean absolute percentage error (MAPE). Minimum, maximum, and average MAPE values are presented for each artificial intelligence technique. Besides this research contributes to fill the gap in rubber industry literature, the results obtained also strongly improve the existing literature results.
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Affiliation(s)
- Zeynep Uruk
- Department of Industrial Engineering , Sakarya University , 54187 Sakarya , Türkiye
- R&D Center, DRC Kauçuk , Sakarya , Türkiye
| | - Alper Kiraz
- Department of Industrial Engineering , Sakarya University , 54187 Sakarya , Türkiye
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Uruk Z, Kiraz A, Deniz V. A comparison of machine learning methods to predict rheometric properties of rubber compounds. J RUBBER RES 2022. [DOI: 10.1007/s42464-022-00170-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Hamad K, Kaseem M, Ayyoob M, Joo J, Deri F. Polylactic acid blends: The future of green, light and tough. Prog Polym Sci 2018. [DOI: 10.1016/j.progpolymsci.2018.07.001] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Jiang X, Xu C, Wang Y, Chen Y. Polyvinylidene Fluoride/Acrylonitrile Butadiene Rubber Blends Prepared Via Dynamic Vulcanization. J MACROMOL SCI B 2014. [DOI: 10.1080/00222348.2014.984577] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Golzar K, Amjad-Iranagh S, Modarress H. Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N222(n)]Tf2N) by Means of Artificial Intelligence Techniques. J DISPER SCI TECHNOL 2014. [DOI: 10.1080/01932691.2013.879533] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Golzar K, Amjad-Iranagh S, Modarress H. Prediction of Thermophysical Properties for Binary Mixtures of Common Ionic Liquids with Water or Alcohol at Several Temperatures and Atmospheric Pressure by Means of Artificial Neural Network. Ind Eng Chem Res 2014. [DOI: 10.1021/ie5007432] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Karim Golzar
- Department
of Chemical Engineering, Amirkabir University of Technology, No. 424,
Hafez Street, Tehran, Iran
| | - Sepideh Amjad-Iranagh
- Department
of Chemistry, Amirkabir University of Technology, No. 424, Hafez Street, Tehran, Iran
| | - Hamid Modarress
- Department
of Chemical Engineering, Amirkabir University of Technology, No. 424,
Hafez Street, Tehran, Iran
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Songsing K, Vatanatham T, Hansupalak N. Kinetics and mechanism of grafting styrene onto natural rubber in emulsion polymerization using cumene hydroperoxide–tetraethylenepentamine as redox initiator. Eur Polym J 2013. [DOI: 10.1016/j.eurpolymj.2013.01.027] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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