Volume 14, Issue 7

Screwless Extrusion for Natural Fiber Composites: A Critical Review of Legacy Data and Future Sustainability Implications

Author

Charitidis J. Panagiotis

Abstract

Abstract:

Screwless extrusion, leveraging viscoelastic normal stress effects rather than mechanical screw propulsion, presents a transformative approach for processing natural fiber-reinforced thermoplastics (NFRTCs). This review critically evaluates the Galea et al. (2004) legacy data through contemporary sustainability frameworks. The technique demonstrates exceptional fiber preservation (85% retention versus 30-55% for conventional methods), superior energy efficiency (50-67% reduction), and chemical-free processing enabling fiber loadings up to 40% without coupling agents. Statistical analysis reveals F-statistics exceeding 45.7 (p < 0.001) with effect sizes (η²) > 0.90, while mechanical performance achieves 87% of MAPP-enhanced composite properties without chemical additives. Comprehensive sustainability analysis demonstrates 42-48% reduction in total system impact, 92% recyclability, and economic viability with €1.0-1.2 million capital investment reductions. Scale-up challenges from laboratory throughput (10-15 g/min) to industrial requirements (100+ kg/h) are addressed through geometric scaling relationships and Industry 4.0 integration. This analysis positions screwless extrusion as a strategically relevant technology for sustainable composite manufacturing, enabling fully bio-based composite systems while achieving SDG alignment through 50-67% energy reduction and 35% carbon footprint reduction.

References:

1. Quintana, E., Valls, C. and Roncero, M.B., 2024. Dissolving-grade pulp: a sustainable source for fiber production. Wood Science and Technology, 58, pp.23–85. https://doi.org/10.1007/s00226-023-01519-w.
2. Hakimi, A., Abedi, Z. and Dadashian, F., 2021. Increasing energy and material consumption efficiency by application of material and energy flow cost accounting system (Case study: turbine blade production). Sustainability, 13, p.4832. https://doi.org/10.3390/su13094832.
3.  Kiruthika, A.V., 2024. A review of leaf fiber reinforced polymer composites. Journal of Engineering and Applied Science, 71, p.24. https://doi.org/10.1186/s44147-024-00365-2.
4. Berzin, F., Beaugrand, J., Dobosz, S., Budtova, T. and Vergnes, B., 2017. Lignocellulosic fiber breakage in a molten polymer. Part 3: Modeling of the dimensional change of the fibers during compounding by twin screw extrusion. Composites Part A: Applied Science and Manufacturing, 101, pp.422–431. https://doi.org/10.1016/j.compositesa.2017.07.009.
5. Guo, M., Li, X. and Maia, J.M., 2021. Fiber length distribution in twin-screw extrusion of fiber-reinforced polymer composites: A comparison between shear and extensional mixing. International Polymer Processing, 36(4), pp.350–357. https://doi.org/10.1515/ipp-2020-3978.
6. Malatyali, H., Schöppner, V., Rabeneck, N., Hanselle, F., Austermeier, L. and Karch, D., 2021. A model for the carbon fiber breakage along the twin-screw extruder. SPE Polymers, 2, pp.145–152. https://doi.org/10.1002/pls2.10040.
7. Lee, C.H., Khalina, A. and Lee, S.H., 2021. Importance of interfacial adhesion condition on characterization of plant-fiber-reinforced polymer composites: A review. Polymers, 13, p.438. https://doi.org/10.3390/polym13030438.
8. Maxwell, B. and Scalora, A.J., 1959. Elastic melt extrusion using viscoelastic normal stress effects. Modern Plastics Magazine, October, p.107.
9. Galea, T., Mills, T., Halliwell, R. and Jayaraman, K., 2004. Screwless extrusion of natural fibre-reinforced thermoplastic composites. In: L. Ye, Y.-W. Mai and Z. Su, eds. Composite Technologies for 2020. Cambridge, U.K.: Woodhead Publishing, pp.27–32. https://doi.org/10.1533/9781845690625.9.459.
10. Jayaraman, K. and Halliwell, R., 2008. Blending of natural fibres and thermoplastics by screwless extrusion. Advanced Materials Research, 47–50, pp.1141–1144. https://doi.org/10.1533/9781845690625.9.459.
11. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Erlbaum. https://doi.org/10.4324/9780203771587.
12. Hilal, Y.F., Abdulvahap, P. and Georgian, B., 2024. Statistical effect sizes in sports science. Journal of Exercise Science and Physical Activity Review, 2(1), pp.164–171. https://doi.org/10.5281/zenodo.12601138.
13. Redhwi, H.H., Siddiqui, M.N. and Andrary, A.L., 2023. Durability of HDPE- and PP-based wood-plastic composites—Part 1: Mechanical properties of the composite materials. Journal of Composites Science, 7(4), p.163. https://doi.org/10.3390/jcs7040163.
14. Stark, N.M. and Rowlands, R.E., 2003. Effects of wood fiber characteristics on mechanical properties of wood/polypropylene composites. Wood and Fiber Science, 35(2), pp.167–174.
15. Tran, T.M.N., P.M.N., Lee, D.W. et al., 2025. Enhanced mechanical and thermal properties of green PP composites reinforced with bio-hybrid fibers and agro-waste fillers. Advanced Composites and Hybrid Materials, 8, p.224. https://doi.org/10.1007/s42114-025-01277-2.
16. Hermans, P. and Platzek, P., 1939. Beiträge zur Kenntnis des Deformationsmechanismus und der Feinstruktur der Hydratzellulose. Kolloid Zeitschrift, 88, p.68. https://doi.org/10.1007/BF01518890.
17. Hermans, J.J., Hermans, P.H., Vermaas, D. and Weidinger, A., 1946. Quantitative Evaluation of Orientation in Cellulose Fibres from the X-Ray Fiber Diagram. Recueil des Travaux Chimiques des Pays-Bas, 65(6), pp.427–447. https://doi.org/10.1002/recl.19460650605.
18. Guenoun, G., Schmitt, N., Roux, S. and Régnier, G., 2021. Crystalline orientation assessment in transversely isotropic semicrystalline polymer: Application to oedometric compaction of PTFE. HAL Archives Ouvertes. https://doi.org/10.1002/pen.25561.
19. Tucker, C.L., 2015. Fiber orientation descriptors: How we got here and what comes next. FPCM15 Keynote.
20. Masubuchi, Y. et al., 2016. Distribution function of fiber length in thermoplastic composites. Composites Science and Technology, 134, pp.43–48. https://doi.org/10.1016/j.compscitech.2016.08.007.
21. Ring, G.J.F. and Bacon, A.J., 2004. Multiple component analysis of fiber length distributions. Univ. of Wisconsin–Stevens Point, Dept. Paper Sci.
22. Varanasi, S., He, R. and Batchelor, W., 2013. Estimation of cellulose nanofibre aspect ratio from measurements of fibre suspension gel point. Cellulose, 20, pp.1885–1896. https://doi.org/10.1007/s10570-013-9972-9.
23. Gu, F., et al., 2018. Water retention value for characterizing fibrillation degree of cellulosic fibers at micro and nanometer scales. Cellulose, 25, pp.2861–2871. https://doi.org/10.1007/s10570-018-1765-8.
24. Lozano, L.A., et al., 2018. Efficient solvothermal synthesis of highly porous UiO-66 nanocrystals in DMF-free media. Journal of Materials Science, 53, pp.1862–1873. https://doi.org/10.1007/s10853-017-1658-5.
25. Sayah, N. and Smith, D.E., 2022. Effect of process parameters on void distribution, volume fraction, and sphericity within the bead microstructure of large-area additive manufacturing polymer composites. Polymers, 14(23), p.5107. https://doi.org/10.3390/polym14235107.
26. Pillai, R.S.S., et al., 2021. Effect of fiber loading rate on various properties of the fiber reinforced polymer composites. In: Rangappa, S.M., et al., eds. Fracture Failure Analysis of Fiber Reinforced Polymer Matrix Composites. Singapore: Springer, ch. 2. https://doi.org/10.1007/978-981-16-0642-7_2.
27. Bahlouli, S., et al., 2023. Effect of fiber loading on thermal properties of cellulosic Washingtonia reinforced HDPE biocomposites. Polymers, 15(13), p.2910. https://doi.org/10.3390/polym15132910.
28. Kirkpatrick, S., 1973. Percolation and conduction. Reviews of Modern Physics, 45, pp.574–588. https://doi.org/10.1103/RevModPhys.45.574.
29. Meng, Q. and Wang, Z., 2015. Theoretical analysis of interfacial debonding and fiber pull-out in fiber-reinforced polymer-matrix composites. Archive of Applied Mechanics, 85, pp.745–759. https://doi.org/10.1007/s00419-015-0987-6.
30. Chandran, M.S. and Padmanabhan, K., 2019. Microbond fibre bundle pullout technique to evaluate the interfacial adhesion of polyethylene and polypropylene self reinforced composites. Applied Adhesion Science, 7, p.5. https://doi.org/10.1186/s40563-019-0121-z.
31. Thomason, J.L. and Rudeiros-Fernández, J.L., 2021. Thermal degradation behaviour of natural fibres at thermoplastic composite processing temperatures. Polymer Degradation and Stability, 188, p.109594. https://doi.org/10.1016/j.polymdegradstab.2021.109594.
32. Guo, J.J., Yan, H., Dai, J., Hu, Z.D. and Yang, J.J., 2016. Evaluation of thermal degradation of high density polyethylene by principal component analysis. Materials Science Forum, 852, pp.632–638. https://doi.org/10.4028/www.scientific.net/MSF.852.632
33. Al-Ashaab, A., Varro, B., Coley, D., Luna, M.I., Madrigal, J., Prokhorov, S., Roszak, J. and Tola, P., 2017. Sustainability assessment of plastic sheet piling. Cranfield University.
34. Bastos, J., Monforti-Ferrario, F. and Melica, G., 2024. GHG Emission Factors for Electricity Consumption. European Commission, JRC136340.
35. Yan, Y., Pillay, S. and Ning, H., 2025. Innovative continuous polypropylene fiber composite filament for material extrusion. Progress in Additive Manufacturing, 10, pp.93–105. https://doi.org/10.1007/s40964-024-00605-0.
36. Spoerk, M., Arbeiter, F., Raguž, I., Holzer, C. and Gonzalez-Gutierrez, J., 2019. Mechanical recyclability of polypropylene composites produced by material extrusion-based additive manufacturing. Polymers, 11(8), p.1318. https://doi.org/10.3390/polym11081318.
37. Njobet, N.L., 2012. Energy analysis in the extrusion of plastics. Arcada University of Applied Sciences. [online] Available at: https://www.theseus.fi/bitstream/handle/10024/53774/NANA_LEVI.pdf
38. Leksic, I., Stefanic, N. and Veza, I., 2020. The impact of using different lean manufacturing tools on waste reduction. Advances in Production Engineering & Management, 15(1), pp.81–92. https://doi.org/10.14743/apem2020.1.351.
39. Podara, C., et al., 2024. Recent trends of recycling and upcycling of polymers and composites: A comprehensive review. Recycling, 9(3), p.37. https://doi.org/10.3390/recycling9030037.
40. Confederation of European Paper Industries (CEPI), 2025. Harmonised European laboratory test method to assess recyclability of paper and board products. [online] Available at: https://www.cepi.org/wp-content/uploads/2025/02/Cepi-Test-Method-27.02_FINAL.pdf
41. Ekins, P. and Zenghelis, D., 2021. The costs and benefits of environmental sustainability. Sustainability Science, 16, pp.949–965. https://doi.org/10.1007/s11625-021-00910-5.
42. Seider, W.D., Seader, J.D., Lewin, D.R. and Widagdo, S., 2004. Product and Process Design Principles: Synthesis, Analysis, and Evaluation. 2nd ed. Hoboken, NJ: Wiley.
43. British Safety Council, 2025. Embedding social sustainability in occupational health and safety. [online] Available at: https://www.britsafe.org
44. OSHA, 2024. Reducing chemical exposure in industrial processing: Best practices and case studies. [online] Available at: https://www.osha.gov
45. European Commission, 2023. Sustainable supply chains and regional economic development. Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs.
46. Fraunhofer Institute for Building Physics (IBP), 2024. Circular economy in forestry and regional bioeconomy. [online] Available at: https://www.ibp.fraunhofer.de
47. European Chemicals Agency (ECHA), 2023. REACH regulation and simplification strategies. [online] Available at: https://echa.europa.eu
48. Ecochain Technologies, 2019. Environmental footprint comparison of conventional and bio-based composites. [online] Available at: https://ecochain.com
49. Earthster Environmental Data Services, 2024. Life cycle impact assessment of composite manufacturing technologies.
50. Scherer, L., et al., 2023. Biodiversity impact assessment considering land use intensities and fragmentation. Environmental Science & Technology, 57(48), pp.19612–19623. https://doi.org/10.1021/acs.est.3c04191.
51. Vidal-Legaz, B., et al. (2016). Land-use related environmental indicators for life cycle assessment. European Commission, JRC. https://eplca.jrc.ec.europa.eu/uploads/QMS_H08_MonscenReff_del-land-use_FINAL.pdf
52. Slay, C. M. (2019). A review of biodiversity and land-use metrics, indices, and methodologies as related to agricultural products. The Sustainability Consortium. https://saiplatform.org/uploads/Library/Biodiversity%20and%20Land%20Use%20Business%20Report.pdf
53. United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda
54. Lokesh, K. S., Ramachandra, C. G., & Mayya, D. S. (2024). Challenges faced in processing of composites. In S. B. Boppana et al. (Eds.), Structural composite materials (Chap. 17). Springer. https://doi.org/10.1007/978-981-99-5982-2_17
55. Stonecash, M. (2024). Scaling up sustainable solutions for fiber-reinforced composites. Composites World. https://www.compositesworld.com/articles/scaling-up-sustainable-solutions-for-materials-of-the-future-2
56. Meegan, J. (2023). Some of the challenges faced by the composites industry in its bid to become more sustainable. RSC Sustainability, 1, 1737–1742. https://doi.org/10.1039/D3SU00200D
57. Gomes, G., Freire, P., Prilepsky, J. E., & Turitsyn, S. K. (2025). Geometric clustering for hardware-efficient implementation of chromatic dispersion compensation. Journal of Lightwave Technology, 43(12), 5676–5688. https://doi.org/10.1109/JLT.2025.3553443
58. Wan, W., et al. (2025). Fuzzy clustering for low-complexity time domain chromatic dispersion compensation scheme in coherent optical fiber communication systems. arXiv Preprint. https://arxiv.org/abs/2503.12482
59. Alaghemandi, M., & Alamandi, M. (2025). Heat transfer in composite materials: Mechanisms and applications. Qeios. https://doi.org/10.32388/EFEDMT
60. Keulen, C. (2024). Heat transfer in composites processing. Knowledge in Practice Centre. https://compositeskn.org/KPC/images/f/f3/Heat_Transfer_in_Composites_Processing-ed6FHPN5pG89dB.pdf
61. Theng, S., Ang, A., Elammaran, J., et al. (2025). A review on industrial optimization approach in polymer matrix composites manufacturing. International Polymer Processing. https://doi.org/10.1515/ipp-2024-0121
62. Kukwi, T., Shan, C., Li, P., et al. (2025). Continuous improvement in composite manufacturing: A review of automated fiber placement process evolution and future research prospects. Applied Composite Materials. https://doi.org/10.1007/s10443-025-10325-5
63. Chen, Y.-P., Karkaria, V., Tsai, Y.-K., et al. (2025). Real-time decision-making for digital twin in additive manufacturing with model predictive control using time-series deep neural networks. Journal of Manufacturing Systems, 80, 412–424. https://doi.org/10.1016/j.jmsy.2025.03.009
64. Nele, L., Mattera, G., Yap, E. W., et al. (2024). Towards the application of machine learning in digital twin technology: A multi-scale review. Discover Applied Sciences, 6(502). https://doi.org/10.1007/s42452-024-06206-4
65. Kherbache, M., Maimour, M., & Rondeau, E. (2021). When digital twin meets network softwarization in the industrial IoT: Real-time requirements case study. Sensors, 21(24), 8194. https://doi.org/10.3390/s21248194
66. ISO/IEC. (2024). Standardization strategy on IoT and digital twin. https://sesko.fi/wp-content/uploads/2024/06/10-2024-ISO-IEC-Standardization-strategy-on-IoT-and-Digital-Twin-v1.0.pdf
67. Fraunhofer IML. (2024). Technological requirements and integration of digital twins into existing IT infrastructures. https://www.iml.fraunhofer.de/en/fields_of_activity/material-flow-systems/software_engineering/digital-twin-in-logistics/technological-requirements-and-integration-of-digital-twins-into-existing-it-infrastructures.html
68. Lin, S.-W., Watson, K., Shao, G., Stojanovic, L., & Zarkout, B. (2023). Digital twin core conceptual models and services. Industry IoT Consortium. https://www.iiconsortium.org/wp-content/uploads/sites/2/2023/10/Digital-Twin-Core-Conceptual-Models-and-Services_20231102.pdf
69. Swathi, B., Polyakov, S. V., Kandavalli, S. R., et al. (2024). Enhancing hybrid manufacturing with AI-driven real-time adaptive process control: Integrating machine learning models and robotic systems. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-024-14155-w
70. AspenTech. (2024). Adaptive process control: Increase plant performance. https://www.aspentech.com/en/applications/msc/adaptive-process-control
71. ATS. (2024). Adaptive manufacturing: Real-time data and modular systems. https://www.advancedtech.com/blog/adaptive-manufacturing/
72. Dassault Systèmes. (2024). Sustainable modular production. https://www.3ds.com/manufacturing/future-proof-systems/modular-production-sustainable-and-modular-manufacturing
73. Tencom. (2024). The benefits of modular design in manufacturing. https://www.tencom.com/blog/the-benefits-of-modular-design-in-manufacturing
74. Thomasnet. (2023). RFID use for supply chain: Benefits, uses, and future trends. https://www.thomasnet.com/insights/rfid-in-supply-chain/
75. Puica, E. (2023). Improving supply chain management by integrating RFID with IoT shared database: Proposing a system architecture. In I. Maglogiannis, L. Iliadis, J. MacIntyre, & M. Dominguez (Eds.), Artificial Intelligence Applications and Innovations (Vol. 676, Chap. 13). Springer. https://doi.org/10.1007/978-3-031-34107-6_13
76. Akar, S., Kinchin, C., & Kurup, P. (2022). Techno-economic analysis for shear assisted processing and extrusion (ShAPE) of high-strength aluminum alloys (NREL/TP-7A40-80038). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy22osti/80038.pdf
77. Ruth-Trumpold, R. (2024). Economic viability in design technology. https://www.ruthtrumpold.id.au/destech/?page_id=162
78. National Competitiveness and Productivity Council. (2024). Competitiveness and productivity framework bulletin 24-2. https://www.competitiveness.ie/media/h2tfee50/20240326-ncpc-bulletin-competitiveness-framework_-final.pdf
79. Pankhania, T. B., Jariya, A. H., & Modi, V. K. (2013). Productivity and competitiveness – An analysis. International Journal of Research in Engineering and Technology, 2(12), 82–89. https://ijret.org/volumes/2013v02/i12/IJRET20130212082.pdf
80. Ashkenas, R., & Matta, N. (2021, January). How to scale a successful pilot project. Harvard Business Review. https://hbr.org/2021/01/how-to-scale-a-successful-pilot-project
81. Echo Global. (2025). Technology implementation plan: Step-by-step guide. https://echoglobal.tech/technology-implementation-plan/
82. Alpha Solutions. (2024). A step-by-step guide to building your technology implementation plan. https://www.alpha-solutions.com/us/insight/technology-implementation-plan
83. NASA. (2024). 6.4 Technical risk management. https://www.nasa.gov/reference/6-4-technical-risk-management/
84. Gutiérrez Cubillos, P., & Pastén, R. (2023). Nonlinear risks: A unified framework. Theory and Decision, 95, 11–32. https://doi.org/10.1007/s11238-022-09912-w
85. Lawrence, A. A. (2024). Risk management strategies in large-scale infrastructure projects. IOSR Journal of Business and Management, 26(6), 38–43. https://www.iosrjournals.org/iosr-jbm/papers/Vol26-issue6/Ser-2/E2606023843.pdf
86. Zong, Z., & Guan, Y. (2025). AI-driven intelligent data analytics and predictive analysis in Industry 4.0: Transforming knowledge, innovation, and efficiency. Journal of the Knowledge Economy, 16, 864–903. https://doi.org/10.1007/s13132-024-02001-z
87. Deloitte. (2017). Industry 4.0 and predictive technologies for asset maintenance. Deloitte Insights. https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/industry-4-0/using-predictive-technologies-for-asset-maintenance.html
88. GoodData. (2025). Predictive analytics in manufacturing: The future of production. https://www.gooddata.com/blog/predictive-analytics-in-manufacturing-what-it-means-for-the-future-of-production/
89. PRé Sustainability. (2022). Real-time, large-scale LCA with ECOFACT. https://pre-sustainability.com/articles/real-time-large-scale-lca-with-ecofact/
90. Real-Time LCA. (2024). Understand, improve and document the building’s climate footprint. https://realtimelca.com/en-us/

DOI

https://doi.org/10.62226/ijarst20252571

PAGES : 1620-1630 | 0 VIEWS | 0 DOWNLOADS

Download Full Article