In today’s manufacturing sector, high-quality materials that satisfy customers’ needs at a reduced cost are drawing attention in the global market. Also, as new applications are emerging, high-performance biocomposite products that complement them are required. The production of such high-performance materials requires suitable optimization techniques in the formulation/process design, not simply mixing natural fibre/filler, additives, and plastics, and characterization of the resulting biocomposites. However, a comprehensive review of the optimization strategies in biocomposite production intended for infrastructural applications is lacking. This study, therefore, presents a detailed discussion of the various optimization approaches, their strengths, and weaknesses in the formulation/process parameters of biocomposite manufacturing. The report explores the recent progress in optimization techniques in biocomposite material production to provide baseline information to researchers and industrialists in this field. Therefore, this review consolidates prior studies to explore new areas.

1. Sanjay MR, Siengchin S, Parameswaranpillai J, et al. A comprehensive review of techniques for natural fibers as reinforcement in composites: Preparation, processing and characterization. Carbohydrate Polymers. 2019, 207: 108-121. doi: 10.1016/j.carbpol.2018.11.083

2. Ejeta LO. Nanoclay/organic filler-reinforced polymeric hybrid composites as promising materials for building, automotive, and construction applications- a state-of-the-art review. Composite Interfaces. 2023, 30(12): 1363-1386. doi: 10.1080/09276440.2023.2220217

3. Yashas Gowda TG, Sanjay MR, Subrahmanya Bhat K, et al. Polymer matrix-natural fiber composites: An overview. Pham D, ed. Cogent Engineering. 2018, 5(1): 1446667. doi: 10.1080/23311916.2018.1446667

4. Neelamana IK, Thomas S, Parameswaranpillai J. Characteristics of banana fibers and banana fiber reinforced phenol formaldehyde composites‐macroscale to nanoscale. Journal of Applied Polymer Science. 2013, 130(2): 1239-1246. doi: 10.1002/app.39220

5. Fung KL, Xing XS, Li RKY, et al. An investigation on the processing of sisal fibre reinforced polypropylene composites. Composites Science and Technology. 2003, 63(9): 1255-1258. doi: 10.1016/s0266-3538(03)00095-2

6. Ejeta LO. The mechanical and thermal properties of wood plastic composites based on heat-treated composite granules and HDPE. Journal of Materials Science. 2023, 58(48): 18090-18104. doi: 10.1007/s10853-023-09169-w

7. Adhikary KB, Pang S, Staiger MP. Long-term moisture absorption and thickness swelling behaviour of recycled thermoplastics reinforced with Pinus radiata sawdust. Chemical Engineering Journal. 2008, 142(2): 190-198. doi: 10.1016/j.cej.2007.11.024

8. Majeed K, Jawaid M, Hassan A, et al. Potential materials for food packaging from nanoclay/natural fibres filled hybrid composites. Materials & Design (1980-2015). 2013, 46: 391-410. doi: 10.1016/j.matdes.2012.10.044

9. Boublia A, Lebouachera SEI, Haddaoui N, et al. State-of-the-art review on recent advances in polymer engineering: modeling and optimization through response surface methodology approach. Polymer Bulletin. 2022, 80(6): 5999-6031. doi: 10.1007/s00289-022-04398-6

10. Toupe JL, Trokourey A, Rodrigue D. Simultaneous optimization of the mechanical properties of postconsumer natural fiber/plastic composites: Processing analysis. Journal of Composite Materials. 2014, 49(11): 1355-1367. doi: 10.1177/0021998314533714

11. Faruk O, Bledzki AK, Fink HP, et al. Biocomposites reinforced with natural fibers: 2000–2010. Progress in Polymer Science. 2012, 37(11): 1552-1596. doi: 10.1016/j.progpolymsci.2012.04.003

12. Toupe JL, Trokourey A, Rodrigue D. Simultaneous optimization of the mechanical properties of postconsumer natural fiber/plastic composites: Phase compatibilization and quality/cost ratio. Polymer Composites. 2013, 35(4): 730-746. doi: 10.1002/pc.22716

13. Joseph P. Effect of processing variables on the mechanical properties of sisal-fiber-reinforced polypropylene composites. Composites Science and Technology. 1999, 59(11): 1625-1640. doi: 10.1016/s0266-3538(99)00024-x

14. Xavier SF, Tyagi D, Misra A. Influence of injection‐molding parameters on the morphology and mechanical properties of glass fiber‐reinforced polypropylene composites. Polymer Composites. 1982, 3(2): 88-96. doi: 10.1002/pc.750030207

15. Ahmad MR, Chen B, Dai JG, et al. Evolutionary artificial intelligence approach for performance prediction of bio-composites. Construction and Building Materials. 2021, 290: 123254. doi: 10.1016/j.conbuildmat.2021.123254

16. Homkhiew C, Ratanawilai T, Thongruang W. Effects of natural weathering on the properties of recycled polypropylene composites reinforced with rubberwood flour. Industrial Crops and Products. 2014, 56: 52-59. doi: 10.1016/j.indcrop.2014.02.034

17. Harper D, Wolcott M. Interaction between coupling agent and lubricants in wood–polypropylene composites. Composites Part A: Applied Science and Manufacturing. 2004, 35(3): 385-394. doi: 10.1016/j.compositesa.2003.09.018

18. Durakovic B. Design of experiments application, concepts, examples: State of the art. Periodicals of Engineering and Natural Sciences (PEN). 2017, 5(3). doi: 10.21533/pen.v5i3.145

19. Montgomery DC, Bert Keats J, Perry LA, et al. Using statistically designed experiments for process development and improvement: an application in electronics manufacturing. Robotics and Computer-Integrated Manufacturing. 2000, 16(1): 55-63. doi: 10.1016/s0736-5845(99)00057-5

20. Baş D, Boyacı İH. Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering. 2007, 78(3): 836-845. doi: 10.1016/j.jfoodeng.2005.11.024

21. Vera Candioti L, De Zan MM, Cámara MS, et al. Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta. 2014, 124: 123-138. doi: 10.1016/j.talanta.2014.01.034

22. Bhaskar P, Sahoo SK. Optimization of Machining Process by Desirability Function Analysis (DFA): A Review. CVR Journal of Science & Technology. 2020, 18(1): 138-143. doi: 10.32377/cvrjst1824

23. Sibalija TV, Majstorovic VD. An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence. Journal of Intelligent Manufacturing. 2010, 23(5): 1511-1528. doi: 10.1007/s10845-010-0451-y

24. Gu F, Hall P, Miles NJ, et al. Improvement of mechanical properties of recycled plastic blends via optimizing processing parameters using the Taguchi method and principal component analysis. Materials & Design (1980-2015). 2014, 62: 189-198. doi: 10.1016/j.matdes.2014.05.013

25. Liao SJ, Chang DY, Chen HJ, et al. Optimal process conditions of shrinkage and warpage of thin‐wall parts. Polymer Engineering & Science. 2004, 44(5): 917-928. doi: 10.1002/pen.20083

26. Ozcelik B, Erzurumlu T. Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. Journal of Materials Processing Technology. 2006, 171(3): 437-445. doi: 10.1016/j.jmatprotec.2005.04.120

27. Oktem H, Erzurumlu T, Uzman I. Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Materials & Design. 2007, 28(4): 1271-1278. doi: 10.1016/j.matdes.2005.12.013

28. Ozcelik B, Sonat I. Warpage and structural analysis of thin shell plastic in the plastic injection molding. Materials & Design. 2009, 30(2): 367-375. doi: 10.1016/j.matdes.2008.04.053

29. Mehat NM, Kamaruddin S. Optimization of mechanical properties of recycled plastic products via optimal processing parameters using the Taguchi method. Journal of Materials Processing Technology. 2011, 211(12): 1989-1994. doi: 10.1016/j.jmatprotec.2011.06.014

30. Antony J. Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Quality and Reliability Engineering International. 2000, 16(1): 3-8. doi: 10.1002/(sici)1099-1638(200001/02)16: 1<3: : aid-qre276>3.0.co, 2-w

31. Al-Refaie A, Al-Alaween W, Diabat A, et al. Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis. Journal of Intelligent Manufacturing. 2014, 28(2): 387-403. doi: 10.1007/s10845-014-0986-4

32. Costa NR, Lourenço J, Pereira ZL. Desirability function approach: A review and performance evaluation in adverse conditions. Chemometrics and Intelligent Laboratory Systems. 2011, 107(2): 234-244. doi: 10.1016/j.chemolab.2011.04.004

33. Lee D, Jeong I, Kim K. A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach. Quality and Reliability Engineering International. 2018, 34(3): 360-376. doi: 10.1002/qre.2258

34. Diakoulaki D, Mavrotas G, Papayannakis L. Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research. 1995, 22(7): 763-770. doi: 10.1016/0305-0548(94)00059-h

35. Doukas H, Papadopoulou A, Savvakis N, et al. Assessing energy sustainability of rural communities using Principal Component Analysis. Renewable and Sustainable Energy Reviews. 2012, 16(4): 1949-1957. doi: 10.1016/j.rser.2012.01.018

36. Su CT, Tong LI. Multi-response robust design by principal component analysis. Total Quality Management. 1997, 8(6): 409-416. doi: 10.1080/0954412979415

37. Fung CP, Kang PC. Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis. Journal of Materials Processing Technology. 2005, 170(3): 602-610. doi: 10.1016/j.jmatprotec.2005.06.040

38. Wu FC. Optimising robust design for correlated quality characteristics. The International Journal of Advanced Manufacturing Technology. 2003, 1(1): 1-1. doi: 10.1007/s00170-002-1501-1

39. Jenarthanan MP, Jeyapaul R. Optimisation of machining parameters on milling of GFRP composites by desirability function analysis using Taguchi method. International Journal of Engineering, Science and Technology. 2018, 5(4): 22-36. doi: 10.4314/ijest.v5i4.3

40. Odu GO. Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management. 2019, 23(8): 1449. doi: 10.4314/jasem.v23i8.7

41. Sengottuvel P, Satishkumar S, Dinakaran D. Optimization of Multiple Characteristics of EDM Parameters Based on Desirability Approach and Fuzzy Modeling. Procedia Engineering. 2013, 64: 1069-1078. doi: 10.1016/j.proeng.2013.09.185

42. Ramanujam R, Maiyar LM, Venkatesan K, Vasan M. Multi response optimization using ANOVA and desirability function analysis: a case study in end milling of Inconel alloy. ARPN Journal of Engineering and Applied Sciences. 2014, 9(4): 457-63.

43. Barreno-Avila E, Moya-Moya E, Pérez-Salinas C. Rice-husk fiber reinforced composite (RFRC) drilling parameters optimization using RSM based desirability function approach. Materials Today: Proceedings. 2022, 49: 167-174. doi: 10.1016/j.matpr.2021.07.498

44. Devarajaiah D, Muthumari C. Evaluation of power consumption and MRR in WEDM of Ti–6Al–4V alloy and its simultaneous optimization for sustainable production. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2018, 40(8). doi: 10.1007/s40430-018-1318-y

45. Adda B, Belaadi A, Boumaaza M, et al. Experimental investigation and optimization of delamination factors in the drilling of jute fiber–reinforced polymer biocomposites with multiple estimators. The International Journal of Advanced Manufacturing Technology. 2021, 116(9-10): 2885-2907. doi: 10.1007/s00170-021-07628-9

46. Dembri I, Belaadi A, Boumaaza M, et al. Drilling performance of short Washingtonia filifera fiber–reinforced epoxy biocomposites: RSM modeling. The International Journal of Advanced Manufacturing Technology. 2022, 121(11-12): 7833-7850. doi: 10.1007/s00170-022-09849-y

47. Tong LI, Hsieh KL. A novel means of applying neural networks to optimize the multiresponse problem. Quality Engineering. 2001, 13(1): 11-18. doi: 10.1080/08982110108918619

48. Noorossana R, Davanloo Tajbakhsh S, Saghaei A. An artificial neural network approach to multiple-response optimization. The International Journal of Advanced Manufacturing Technology. 2008, 40(11-12): 1227-1238. doi: 10.1007/s00170-008-1423-7

49. Xu K, Lin DKJ, Tang LC, et al. Multiresponse systems optimization using a goal attainment approach. IIE Transactions. 2004, 36(5): 433-445. doi: 10.1080/07408170490426143

50. Messac A. From Dubious Construction of Objective Functions to the Application of Physical Programming. AIAA Journal. 2000, 38(1): 155-163. doi: 10.2514/2.936

51. Kovach J, Cho BR. Development of a multidisciplinary–multiresponse robust design optimization model. Engineering Optimization. 2008, 40(9): 805-819. doi: 10.1080/03052150802046304

52. Aydoğmuş E, Arslanoğlu H, Dağ M. Production of waste polyethylene terephthalate reinforced biocomposite with RSM design and evaluation of thermophysical properties by ANN. Journal of Building Engineering. 2021, 44: 103337. doi: 10.1016/j.jobe.2021.103337

53. Parikh HH, Gohil PP. Experimental investigation and prediction of wear behavior of cotton fiber polyester composites. Friction. 2017, 5(2): 183-193. doi: 10.1007/s40544-017-0145-y

54. Pradhan P, Satapathy A. Analysis of Dry Sliding Wear Behavior of Polyester Filled with Walnut Shell Powder Using Response Surface Method and Neural Networks. Journal of Materials Engineering and Performance. 2021, 30(6): 4012-4029. doi: 10.1007/s11665-021-05802-4

55. Singh T. Optimum design based on fabricated natural fiber reinforced automotive brake friction composites using hybrid CRITIC-MEW approach. Journal of Materials Research and Technology. 2021, 14: 81-92. doi: 10.1016/j.jmrt.2021.06.051

56. Singh T, Pattnaik P, Kumar SR, et al. Optimization on physicomechanical and wear properties of wood waste filled poly(lactic acid) biocomposites using integrated entropy-simple additive weighting approach. South African Journal of Chemical Engineering. 2022, 41: 193-202. doi: 10.1016/j.sajce.2022.06.008

57. Homkhiew C, Ratanawilai T, Thongruang W. The optimal formulation of recycled polypropylene/rubberwood flour composites from experiments with mixture design. Composites Part B: Engineering. 2014, 56: 350-357. doi: 10.1016/j.compositesb.2013.08.041

58. Patel GCM, Jagadish. Experimental modeling and optimization of surface quality and thrust forces in drilling of high-strength Al 7075 alloy: CRITIC and meta-heuristic algorithms. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2021, 43(5). doi: 10.1007/s40430-021-02928-3

59. Zhong Y, Poloso T, Hetzer M, et al. Enhancement of wood/polyethylene composites via compatibilization and incorporation of organoclay particles. Polymer Engineering & Science. 2007, 47(6): 797-803. doi: 10.1002/pen.20756

60. Mohanty AK, Wibowo A, Misra M, et al. Effect of process engineering on the performance of natural fiber reinforced cellulose acetate biocomposites. Composites Part A: Applied Science and Manufacturing. 2004, 35(3): 363-370. doi: 10.1016/j.compositesa.2003.09.015

61. Dunne R, Desai D, Sadiku R, et al. A review of natural fibres, their sustainability and automotive applications. Journal of Reinforced Plastics and Composites. 2016, 35(13): 1041-1050. doi: 10.1177/0731684416633898

62. Ticoalu A, Aravinthan T, Cardona F. A review of current development in natural fiber composites for structural and infrastructure applications. In: Proceedings of Southern Region Engineering Conference; 11–12 November 2010; Toowoomba, Australia.