FNPS Society

Main Menu

  • Home
  • Amalgamation
  • Terms of trade
  • Monotonic
  • G-8
  • Financial Affairs

FNPS Society

Header Banner

FNPS Society

  • Home
  • Amalgamation
  • Terms of trade
  • Monotonic
  • G-8
  • Financial Affairs
Monotonic
Home›Monotonic›Machine learning assisted optimization of the process of mixing polyphenylene sulfide with an elastomer using a high speed twin screw extruder

Machine learning assisted optimization of the process of mixing polyphenylene sulfide with an elastomer using a high speed twin screw extruder

By Richard Lyons
December 15, 2021
22
0
  • 1.

    Masamoto, J. Poly (p-phenylene sulfide). In Polymer Data Manual (ed. Mark, JE) 714-721 (Oxford University Press, 1999).

    Google Scholar

  • 2.

    Fink, JK Poly (phenylene sulfide). In High performance polymers 2nd edition (ed. Mark, JE) 129-151 (Elsevier, 2014).

    Google Scholar

  • 3.

    Zuo, PY, Tcharkhtchi, A., Shirinbayan, M., Fitoussi, J. & Bakir, F. Global survey of poly (phenylene sulfide) from synthesis and process to applications: a review. Macromole. Check out. Ing. 304, 1800686 (2019).

    Google Scholar

  • 4.

    Isayev, AI (ed.) Encyclopedia of Polymer Blends Flight. 1 (Wiley, 2010).

    Google Scholar

  • 5.

    Subramanian, MN Mixtures of polymers and composites: chemistry and technology (Wiley, 2017).

    Google Scholar

  • 6.

    Masamoto, J. & Kubo, K. Elastomer cured poly (phenylene sulfide). Polym. Ing. Sci. 36, 265-270 (1996).

    CAS Google Scholar

  • seven.

    Lee, SI & Chun, BC Effect of EGMA content on tensile and impact properties of EGMA poly (phenylene sulfide) blends. Polymer 39, 6441-6447 (1998).

    CAS Google Scholar

  • 8.

    Horiuchi, S. & Ishii, Y. Reactive mixtures of poly (phenylene sulfide) and low density polyethylene: morphology, tribology and moldability. Polym. J. 32, 339-347 (2000).

    CAS Google Scholar

  • 9.

    Oyama, HT, Matsushita, M. & Furuta, M. High performance reactive mixtures composed of poly (p-phenylene sulfide) and ethylene copolymers. Polym. J. 43, 991-999 (2011).

    CAS Google Scholar

  • ten.

    Gui, H. et al. Structure, properties and mechanism of reactive compatibility of the epoxy with the polyphenylene sulfide / polyamide elastomer. J. Appl. Polym. Sci. 130, 3411-3420 (2013).

    CAS Google Scholar

  • 11.

    Nara, S., Sagawa, H., Saito, H. & Oyama, HT Synergistic enhancement of poly (phenylene sulfide) by poly (phenylsulfone) and poly (ethylene-ran-methacrylate-ran-glycidyl methacrylate). J. Appl. Polym. Sci. 138, e49994 (2021).

    Google Scholar

  • 12.

    Wu, SH Phase structure and adhesion in polymer blends: A criterion for rubber toughness. Polymer 26, 1855-1863 (1985).

    CAS Google Scholar

  • 13.

    Wu, SH A generalized criterion for rubber hardening: The critical thickness of the matrix ligament. J. Appl. Polym. Sci. 35, 549-561 (1988).

    CAS Google Scholar

  • 14.

    Isayev, AI (ed.) Encyclopedia of Polymer Blends Flight. 2 (Wiley, 2011).

    Google Scholar

  • 15.

    Brunton, S. & Kutz, J. Data-driven science and engineering: machine learning, dynamic systems and control (Cambridge University Press, 2019).

    MATH Google Scholar

  • 16.

    Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials computing: recent applications and perspectives. NPJ calculation. Check out. 3, 54 (2017).

    Google Scholar ADS

  • 17.

    Butler, KT, Davies, DW, Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547-555 (2018).

    ADS CAS PubMed Google Scholar

  • 18.

    Medford, AJ, Kunz, MR, Ewing, SM, Borders, T. & Fushimi, R. Knowledge Extraction from Data Using Catalyst Computing. ACS Catal. 8, 7403-7429 (2018).

    CAS Google Scholar

  • 19.

    Himanen, L., Geurts, A., Foster, AS & Rinke, P. Data-driven materials science: state, challenges and perspectives. Av. Sci. 6, 190808 (2019).

    Google Scholar

  • 20.

    Barnard, AS & Opletal, G. Prediction of structure / property relationships in multidimensional nanoparticle data using the integration of t-distributed stochastic neighbors and machine learning. Nanoscale 11, 23165-23172 (2019).

    Google School CAS PubMed Fellow

  • 21.

    Tran, HD et al. Machine learning predictions of polymer properties with the polymer genome. J. Appl. Phys. 128, 171104 (2020).

    Google Scholar ADS

  • 22.

    Chen, G. et al. De novo design assisted by machine learning of organic molecules and polymers: opportunities and challenges. Polymers 12, 163 (2020).

    CAS PubMed Central Google Scholar

  • 23.

    Kojima, T., Washio, T., Hara, S. & Koishi, M. Synthesis of computer simulation and machine learning to obtain the best material properties of loaded rubber. Sci. representing ten, 18127 (2020).

    CAS PubMed PubMed Central Google Scholar

  • 24.

    Li, C. et al. Rapid Bayesian optimization for the synthesis of short polymeric fiber materials. Sci. representing seven, 5683 (2017).

    ADS PubMed PubMed Central Google Scholar

  • 25.

    Pellegrino, F. et al. Machine learning approach to elucidate and predict the role of synthesis parameters on the shape and size of TiO2 nanoparticles. Sci. representing ten, 18910 (2020).

    ADS PubMed PubMed Central Google Scholar

  • 26.

    Damiati, SA, Rossi, D., Joensson, HN & Damiati, S. Artificial intelligence application for rapid fabrication of size adjustable PLGA microparticles in microfluidics. Sci. representing ten, 19517 (2020).

    ADS CAS PubMed PubMed Central Google Scholar

  • 27.

    Mekki-Berrada, F. et al. Two-step machine learning enables optimized synthesis of nanoparticles. NPJ calculation. Check out. seven, 55 (2021).

    ADS CAS Google Scholar

  • 28.

    Tomiyama, H., Fukuzawa, Y. & Fukuzawa, D. Automatic optimization of the screw configuration of a co-rotating twin screw extruder meshing using an artificial intelligence algorithm. Seikei-Kakou 30, 162-169 (2018).

    ADS CAS Google Scholar

  • 29.

    Altarazi, S., Allaf, R. & Alhindawi, F. Machine learning models to predict and classify the tensile strength of polymer films made through different production processes. Materials 12, 1475 (2019).

    ADS CAS PubMed Central Google Scholar

  • 30.

    Casteran, F. et al. Application of machine learning tools for improving reactive extrusion simulation. Macromole. Check out. Ing. 305, 2000375 (2020).

    CAS Google Scholar

  • 31.

    Breiman, L. Random forests. Mach. To learn. 45, 5-32 (2001).

    Google Scholar article

  • 32.

    Shimizu, H., Li, YJ, Kaito, A. & Sano, H. Formation of nanostructured PVDF / PA11 mixtures using high shear processing. Macromolecules 38, 7880-7883 (2005).

    ADS CAS Google Scholar

  • 33.

    Li, YJ & Shimizu, H. Manufacture of nanostructured polycarbonate / poly (methyl methacrylate) blends with improved optical and mechanical properties by high shear processing. Polym. Ing. Sci. 51, 1437-1445 (2011).

    CAS Google Scholar

  • 34.

    Teyssandier, F., Cassagnau, P., Gérard, JF, Mignard, N. & Melis, F. Morphology and mechanical properties of PA12 / plasticized starch mixtures prepared by high shear extrusion. Check out. Chem. Phys. 133, 913-923 (2012).

    CAS Google Scholar

  • 35.

    Farahanchi, A., Malloy, R. & Sobkowicz, MJ Effects of ultra-high speed twin screw extrusion on thermal and mechanical degradation of polystyrene. Polym. Ing. Sci. 56, 743-751 (2016).

    CAS Google Scholar

  • 36.

    Farahanchi, A., Boehm, E., Orbey, N. & Malloy, R. The effect of ultra-high speed twin screw extrusion on the properties of the nanocomposite ABS / organic clay mixture. Polym. Ing. Sci. 57, 60-68 (2017).

    CAS Google Scholar

  • 37.

    Farahanchi, A. & Sobkowicz, MJ Kinetic and process modeling of thermal and mechanical degradation in very high speed twin screw extrusion. Polym. Degrade. Stabilize. 138, 40-46 (2017).

    CAS Google Scholar

  • 38.

    Farahanchi, A., Malloy, RA & Sobkowicz, MJ Extreme shear treatment for exfoliation of organic clay in nanocomposites with incompatible polymers. Polymer 145, 117-126 (2018).

    CAS Google Scholar

  • 39.

    Sui, general practitioner et al. A comparative study of high shear force and compatibilizer on phase morphologies and properties of polypropylene / polylactide (PP / PLA) blends. Polymer 154, 119-127 (2018).

    CAS Google Scholar

  • 40.

    Raj, A., Samuel, C., Malladi, N. & Prashantha, K. Improved (thermo) mechanical properties in bio-based poly (L-lactide) / poly (amide-12) blends using high extrusion processing. shear without compatibilizers. Polym. Ing. Sci. 60, 1902-1916 (2020).

    CAS Google Scholar

  • 41.

    Abeykoon, C., Martin, PJ, Kelly, AL & Brown, EC A review and evaluation of melting temperature sensors for polymer extrusion. Sense. Actuator A-Phys. 182, 16-27 (2012).

    CAS Google Scholar

  • 42.

    Vera-Sorroche, J., Kelly, AL, Brown, EC & Coates, PD Infrared measurement of the melting temperature of the single-screw extrusion. Polym. Ing. Sci. 55, 1059-1066 (2015).

    CAS Google Scholar

  • 43.

    Emin, MA, Teumer, T., Schmitt, W., Radle, M. & Schuchmann, HP Measurement of the actual melting temperature in a twin-screw extrusion process of starch-based dies via an infrared sensor. J. Food Eng. 170, 119–124 (2016).

    Google Scholar

  • 44.

    ImageJ. https://imagej.nih.gov/ij/.

  • 45.

    Python. https://www.python.org/.

  • 46.

    Scikit-learn. https://scikit-learn.org/stable/.

  • Related posts:

    1. The pandemic diary: from masks to music, these ladies from a refuge in Chennai proceed their resilience
    2. Lithium niobate crystal movie for built-in photonics functions
    3. World Main Producers Evaluation, Dynamics and Forecast 2020-2026
    4. Scientific & Precision Options Thrive As Their First Anniversary Approaches |

    Categories

    • Amalgamation
    • Financial Affairs
    • G-8
    • Monotonic
    • Terms of trade
    • TERMS AND CONDITIONS
    • PRIVACY AND POLICY