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Fiber Reinforced Composite Manufacturing With the Aid of Artificial Intelligence – A State-of-the-Art Review

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Abstract

Manufacturing of fiber reinforced polymer matrix composite materials is being done with various methods in recent days. But controlling the accuracy of manufacturing and begetting quality products is very challenging. Adopting single manufacturing techniques may not cater this purpose, but blended or unified technologies do. Artificial intelligence (AI) is an emerging technique that is highly reliable to be blended with current methods and caters to the needs of various composite manufacturers to the maximum possible extent. Accordingly, in this article, the primary focus is provided on the manufacturing sector and more particularly emphasis only on composite material manufacturing with the help of AI. The way the manufacturing is guided by digital technologies including AI is effective in terms of the quality of manufacturing composite materials. In most of the recent studies, AI techniques are used in autoclave manufacturing of composites to control temperature and pressure, curing of composites establishing control over the curing environment and also in flaw detection of composites. This article also focuses on using digital technologies for manufacturing simulation, hybridizing digital technologies and a few of its implications over Industry 4.0.

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References

  1. Müller KR, Understanding ML (2019) models. Technical University of Berlin

  2. Samek W, Montavon G, Vedaldi A, Hansen LK, Müller KR (eds) (2019) Explainable AI: interpreting, explaining and visualizing deep learning, vol 11700. Springer Nature

  3. Tjoa E, Guan C (2020) A survey on explainable artificial intelligence (XAI): Towards medical XAI. IEEE Trans Neural Netw Learn Syst., pp. 1–21,10.1109/ TNNLS.2020.3027314. URL ar**v:1907.07374

  4. Shrikumar A, Greenside P, Kundaje A (2017) July. Learning important features through propagating activation differences. In International Conference on Machine Learning (pp. 3145–3153). PMLR

  5. Sundararajan M, Taly A, Yan Q (2017) July. Axiomatic attribution for deep networks. In International Conference on Machine Learning (pp. 3319–3328). PMLR

  6. Smilkov D, Thorat N, Kim B, Viégas F, Wattenberg M (2017) Smoothgrad: removing noise by adding noise. ar**v preprint ar**v:1706.03825

  7. Lundberg SM, Lee SI (2017) December. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems (pp. 4768–4777)

  8. Zeiler MD, Fergus R (2014) September. Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818–833). Springer, Cham

  9. Meister S, Wermes M, Stüve J, Groves RM (2021) Cross-evaluation of a parallel operating SVM–CNN classifier for reliable internal decision-making processes in composite inspection. J Manuf Syst 60:620–639

    Article  Google Scholar 

  10. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618–626)

  11. Zhang J, Bargal SA, Lin Z, Brandt J, Shen X, Sclaroff S (2018) Top-down neural attention by excitation backprop. Int J Comput Vision 126(10):1084–1102

    Article  Google Scholar 

  12. Srinivas S, Fleuret F (2019) Full-gradient representation for neural network visualization. ar**v preprint ar**v:1905.00780

  13. Montavon G, Binder A, Lapuschkin S, Samek W, Müller KR (2019) Layer-wise relevance propagation: an overview. Explainable AI: interpreting, explaining and visualizing deep learning, pp.193–209

  14. Grezmak J, Zhang J, Wang P, Loparo KA, Gao RX (2019) Interpretable convolutional neural network through layer-wise relevance propagation for machine fault diagnosis. IEEE Sens J 20(6):3172–3181

    Article  Google Scholar 

  15. Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: Visualising image classification models and saliency maps. ar**v preprint ar**v:1312.6034

  16. Zhang Q, Wu YN, Zhu SC (2018) Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8827–8836)

  17. Kanehira A, Harada T (2019) Learning to explain with complemental examples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8603–8611)

  18. Ribeiro MT, Singh S, Guestrin C (2016) Model-agnostic interpretability of machine learning. ar**v preprint ar**v:1606.05386

  19. Fong RC, Vedaldi A (2017) Interpretable explanations of black boxes by meaningful perturbation. In Proceedings of the IEEE international conference on computer vision (pp. 3429–3437)

  20. Fong R, Patrick M, Vedaldi A (2019) Understanding deep networks via extremal perturbations and smooth masks. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2950–2958)

  21. Kindermans PJ, Schütt KT, Alber M, Müller KR, Erhan D, Kim B, Dähne S (2017) Learning how to explain neural networks: Patternnet and patternattribution. ar**v preprint ar**v:1705.05598

  22. Gu GX, Chen CT, Buehler MJ (2018) De novo composite design based on machine learning algorithm. Extreme Mech Lett 18:19–28

    Article  Google Scholar 

  23. Ramesh M, Deepa C, Kumar LR, Sanjay MR, Siengchin S(2020) Life-cycle and environmental impact assessments on processing of plant fibres and its bio-composites: A critical review.Journal of Industrial Textiles, p.1528083720924730

  24. Balaji D, Ramesh M, Kannan T, Deepan S, Bhuvaneswari V, Rajeshkumar L (2021) Experimental investigation on mechanical properties of banana/snake grass fiber reinforced hybrid composites. Materials Today: Proceedings, 42, pp.350–355

  25. Karnik SR, Gaitonde VN, Rubio JC, Correia AE, Abrão AM, Davim JP (2008) Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model. Mater Design 29(9):1768–1776

    Article  Google Scholar 

  26. Yuan S, Li S, Zhu J, Tang Y (2021) Additive manufacturing of polymeric composites from material processing to structural design. Composites Part B: Engineering, 219, p.108903

  27. Patel P, Sheth S, Patel T (2016) Experimental analysis and ANN modelling of HAZ in laser cutting of glass fibre reinforced plastic composites. Procedia Technol 23:406–413

    Article  Google Scholar 

  28. Stamopoulos AG, Tserpes KI, Dentsoras AJ (2018) Quality assessment of porous CFRP specimens using X-ray Computed Tomography data and Artificial Neural Networks. Compos Struct 192:327–335

    Article  Google Scholar 

  29. Seyhan AT, Tayfur G, Karakurt M, Tanogˇlu M (2005) Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites. Comput Mater Sci 34(1):99–105

    Article  Google Scholar 

  30. Caggiano A, Rimpault X, Teti R, Balazinski M, Chatelain JF, Nele L (2018) Machine learning approach based on fractal analysis for optimal tool life exploitation in CFRP composite drilling for aeronautical assembly. CIRP Ann 67(1):483–486

    Article  Google Scholar 

  31. Spoerre J, Zhang C, Wang B, Parnas R (1998) Integrated product and process design for resin transfer molded parts. J Compos Mater 32(13):1244–1272

    Article  Google Scholar 

  32. Golkarnarenji G, Naebe M, Badii K, Milani AS, Jazar RN, Khayyam H (2018) Production of low cost carbon-fiber through energy optimization of stabilization process. Materials, 11(3), p.385

  33. Wilcox JAD, Wright DT (1998) Towards pultrusion process optimisation using artificial neural networks. J Mater Process Technol 83(1–3):131–141

    Article  Google Scholar 

  34. Pfrommer J, Zimmerling C, Liu J, Kärger L, Henning F, Beyerer J (2018) Optimisation of manufacturing process parameters using deep neural networks as surrogate models. Procedia CiRP 72:426–431

    Article  Google Scholar 

  35. Liu Y, Farnsworth M, Tiwari A (2017) A review of optimisation techniques used in the composite recycling area: State-of-the-art and steps towards a research agenda. J Clean Prod 140:1775–1781

    Article  Google Scholar 

  36. Hinkle D, Toomey C (1995) Applying case-based reasoning to manufacturing. AI magazine 16(1):65–65

    Google Scholar 

  37. Li Z (2015) Tension control system design of a filament winding structure based on fuzzy neural network. Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva, 35(1), pp.9–17

  38. Heider D, Piovoso MJ, Gillespie Jr JW (2002) Application of a neural network to improve an automated thermoplastic tow-placement process. J Process Control 12(1):101–111

    Article  Google Scholar 

  39. Sharp M, Ak R, Hedberg Jr T (2018) A survey of the advancing use and development of machine learning in smart manufacturing. Journal of manufacturing systems, 48, pp.170–179

  40. Wu JY, Sfarra S, Yao Y (2018) Sparse principal component thermography for subsurface defect detection in composite products. IEEE Trans Industr Inf 14(12):5594–5600

    Article  Google Scholar 

  41. Maass D (2015) Progress in automated ply inspection of AFP layups. Reinf Plast 59(5):242–245

    Article  Google Scholar 

  42. Gregory ED, Juarez PD (2018) April. In-situ thermography of automated fiber placement parts. In AIP Conference Proceedings (Vol. 1949, No. 1, p. 060005). AIP Publishing LLC

  43. Ramesh M, Deepa C, Selvan MT, Rajeshkumar L, Balaji D, Bhuvaneswari V (2021) Mechanical and water absorption properties of Calotropis gigantea plant fibers reinforced polymer composites. Materials Today: Proceedings, 46, pp.3367–3372

  44. Denkena B, Schmidt C, Völtzer K, Hocke T (2016) Thermographic online monitoring system for Automated Fiber Placement processes. Compos Part B: Eng 97:239–243

    Article  Google Scholar 

  45. Bhuvaneswari V, Priyadharshini M, Deepa C, Balaji D, Rajeshkumar L, Ramesh M (2021) Deep learning for material synthesis and manufacturing systems: a review. Materials Today: Proceedings, 46(9), pp. 3263–3269

  46. Ramesh M, Rajeshkumar L, Deepa C, Tamil Selvan M, Kushvaha V, Asrofi M (2021) Impact of Silane Treatment on Characterization of Ipomoea Staphylina Plant Fiber Reinforced Epoxy Composites. J Nat Fibers 1–12. https://doi.org/10.1080/15440478.2021.1902896

  47. Kuhl M, Wiener T, Krauß M (2013) Multisensorial self-learning systems for quality monitoring of carbon fiber composites in aircraft production. Procedia CIRP 12:103–108

    Article  Google Scholar 

  48. Brüning J, Denkena B, Dittrich MA, Hocke T (2017) Machine learning approach for optimization of automated fiber placement processes. Procedia CIRP 66:74–78

    Article  Google Scholar 

  49. Ramesh M, Rajeshkumar L, Balaji D (2021) Influence of Process Parameters on the Properties of Additively Manufactured Fiber-Reinforced Polymer Composite Materials: A Review. J Mater Eng Perform 30(7):4792–4807

    Article  Google Scholar 

  50. Ramesh M, Deepa C, Niranjana K, Rajeshkumar L, Bhoopathi R, Balaji D (2021) Influence of Haritaki (Terminalia chebula) nano-powder on thermo-mechanical, water absorption and morphological properties of Tindora (Coccinia grandis) tendrils fiber reinforced epoxy composites. J Nat Fibers 1–17. https://doi.org/10.1080/15440478.2021.1921660

  51. Sacco C, Radwan AB, Anderson A, Harik R, Gregory E (2020) Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection. Composite Structures, 250, p.112514

  52. Hunter D, Yu H, Pukish III, Kolbusz MS, Wilamowski BM (2012) Selection of proper neural network sizes and architectures—A comparative study. IEEE Trans Industr Inf 8(2):228–240

    Article  Google Scholar 

  53. Schmidt C, Hocke T, Denkena B (2019) Artificial intelligence for non-destructive testing of CFRP prepreg materials. Prod Eng Res Devel 13(5):617–626

    Article  Google Scholar 

  54. Carlone P, Aleksendrić D, Rubino F, Ćirović V (2018) June. Artificial Neural Networks in Advanced Thermoset Matrix Composite Manufacturing. In International Conference on the Industry 4.0 model for Advanced Manufacturing (pp. 78–88). Springer, Cham

  55. Vijay R, Vinod A, Singaravelu DL, Sanjay MR, Siengchin S (2021) Characterization of chemical treated and untreated natural fibers from Pennisetum orientale grass-A potential reinforcement for lightweight polymeric applications. Int J Lightweight Mater Manuf 4(1):43–49

    Google Scholar 

  56. Shivegowda MD, Boonyasopon P, Rangappa SM, Siengchin S (2022) A Review on Computer-Aided Design and Manufacturing Processes in Design and Architecture. Arch Comput Methods Eng 1–8. https://doi.org/10.1007/s11831-022-09723-w

  57. Correa JL, Todeschini M, Pérez DS, Karouta J, Bromberg F, Ribeiro A, Andújar D (2021) Multi species weed detection with Retinanet one-step network in a maize field. Precision agriculture’21. Wageningen Academic Publishers, pp 2223–2228

  58. Ramesh M, Deepa C, Rajeshkumar L, Tamil Selvan M, Balaji D (2021) Influence of fiber surface treatment on the tribological properties of Calotropis gigantea plant fiber reinforced polymer composites. Polym Compos. https://doi.org/10.1002/pc.26149

    Article  Google Scholar 

  59. Ramesh M, Rajeshkumar L, Bhoopathi R (2021) Carbon substrates: a review on fabrication, properties and applications. Carbon Lett 31:557–580

    Article  Google Scholar 

  60. Automation Technology GmbH E. C5 Series - User manual for high speed 3D sensors: techreport 1.2 (1st ed.), Automation Technology GmbH, Hermann-Bössow-Straße 6–8, 23843 Bad Oldesloe, Germany (2019) URL https://www.automationtechnology.de/cms/wp-content/uploads/2019/03/C5-Series_specifications_web.pdf. Rev 1.2

  61. Meister S, Wermes M, Stüve J, Groves RM(2021) Investigations on Explainable Artificial Intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing. Composites Part B: Engineering, 224, p.109160

  62. ams AGE. Datasheet DS000603 - CMV12000 - CMOS image sensor: techreport 3.0 ams AG(2020) Tobelbader Strasse 30, 8141 Premstaetten, Austria datasheet DS000603 v3-00. URL https://ams.com/documents/20143/36005/CMV12000_DS000603_3-00.pdf/d27f4643-e11b-86f9-4e09-ec055cb4c8e1

  63. Chen CT, Gu GX (2019) Machine learning for composite materials. MRS Commun 9(2):556–566

    Article  Google Scholar 

  64. Ramesh M, Rajeshkumar L, Bhuvaneswari V (2021) Leaf fibres as reinforcements in green composites: a review on processing, properties and applications. Emergent Mater 1–25. https://doi.org/10.1007/s42247-021-00310-6

  65. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R (2020) Array programming with NumPy. Nature 585(7825):357–362

    Article  Google Scholar 

  66. Bradski G (2000) The openCV library. Dr Dobb’s Journal: Software Tools for the Professional Programmer 25(11):120–123

    Google Scholar 

  67. Devarajan B, Saravanakumar R, Sivalingam S, Bhuvaneswari V, Karimi F, Rajeshkumar L (2021) Catalyst derived from wastes for biofuel production: a critical review and patent landscape analysis. Appl Nanosci 1–25. https://doi.org/10.1007/s13204-021-01948-8

  68. Meister S, Möller N, Stüve J, Groves RM(2021) Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set.Journal of Intelligent Manufacturing, pp.1–23

  69. Chollet F(2018) Keras: The python deep learning library. Astrophysics Source Code Library, pp.ascl-1806

  70. Sharma A, Mukhopadhyay T, Rangappa SM, Siengchin S, Kushvaha V (2022) Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design. Arch Comput Methods Eng 1–45. https://doi.org/10.1007/s11831-021-09700-9

  71. Van Rossum G, Drake Jr FL (1995) Python reference manual. Centrum voor Wiskunde en Informatica, Amsterdam

    Google Scholar 

  72. Rajeshkumar L(2021) Biodegradable polymer blends and composites from renewable Resources. In: M. R. Sanjay, J. Parameswaranpillai, Suchart Siengchin and M. Ramesh (eds.) Biodegradable polymer blends and composites. Woodhead publishing, Elsevier. pp. 527–549. https://doi.org/10.1016/B978-0-12-823791-5.00015-6

  73. Manimaran P, Senthamaraikannan P, Sanjay MR, Marichelvam MK, Jawaid M (2018) Study on characterization of Furcraea foetida new natural fiber as composite reinforcement for lightweight applications. Carbohydr Polym 181:650–658

    Article  Google Scholar 

  74. Yeh CK, Hsieh CY, Suggala A, Inouye DI, Ravikumar PK (2019) On the (in) fidelity and sensitivity of explanations. Adv Neural Inf Process Syst 32:10967–10978

    Google Scholar 

  75. Carlone P, Aleksendrić D, Ćirović V, Palazzo GS (2014) Modelling of thermoset matrix composite curing process. Key Engineering Materials, vol 611. Trans Tech Publications Ltd, pp 1667–1674

  76. Jothibasu S, Mohanamurugan S, Vijay R, Lenin Singaravelu D, Vinod A, Sanjay MR (2020) Investigation on the mechanical behavior of areca sheath fibers/jute fibers/glass fabrics reinforced hybrid composite for light weight applications. J Ind Text 49(8):1036–1060

    Article  Google Scholar 

  77. Ćirović V(2012) : Investigation of the possibilities for using artificial intelligence methods in predicting of the performance of the motor vehicles’ braking system. Ph.D. thesis (in Serbian)

  78. Vijay R, Vinod A, Singaravelu DL, Sanjay MR, Siengchin S (2021) Characterization of chemical treated and untreated natural fibers from Pennisetum orientale grass-A potential reinforcement for lightweight polymeric applications. Int J Lightweight Mater Manuf 4(1):43–49

    Google Scholar 

  79. MR S, Yogesha B (2016) Study on water absorption behaviour of jute and kenaf fabric reinforced epoxy composites: hybridization effect of e-glass fabric. Inter J Compos Mater 6:55–62

    Google Scholar 

  80. Crawford B, Sourki R, Khayyam H, Milani AS(2021) A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: An industry 4.0-based case study on composite autoclave manufacturing. Computers in Industry, 132, p.103510

  81. Shearer C (2000) The CRISP-DM model: the new blueprint for data mining. J data Warehous 5(4):13–22

    Google Scholar 

  82. Kushvaha V, Kumar SA, Madhushri P, Sharma A (2020) Artificial neural network technique to predict dynamic fracture of particulate composite. J Compos Mater 54(22):3099–3108

    Article  Google Scholar 

  83. Mariscal G, Marban O, Fernandez C (2010) A survey of data mining and knowledge discovery process models and methodologies. Knowl Eng Rev 25(2):137–166

    Article  Google Scholar 

  84. Sharma A, Kushvaha V(2020) Predictive modelling of fracture behaviour in silica-filled polymer composite subjected to impact with varying loading rates using artificial neural network. Engineering Fracture Mechanics, 239, p.107328

  85. Moustafa N, Hu J, Slay J (2019) A holistic review of network anomaly detection systems: A comprehensive survey. J Netw Comput Appl 128:33–55

    Article  Google Scholar 

  86. Parmar JD, Patel JT (2017) Anomaly detection in data mining: A review. Int J 7(4):32–40

    Google Scholar 

  87. Demir KA, Döven G, Sezen B (2019) Industry 5.0 and human-robot co-working. Procedia Comput Sci 158:688–695

    Article  Google Scholar 

  88. Siakeng R, Jawaid M, Asim M, Saba N, Sanjay MR, Siengchin S, Fouad H(2020) Alkali treated coir/pineapple leaf fibres reinforced PLA hybrid composites: Evaluation of mechanical, morphological, thermal and physical properties.eXPRESS Polymer Letters, 14(8)

  89. Dinesh S, Kumaran P, Mohanamurugan S, Vijay R, Singaravelu DL, Vinod A, Sanjay MR, Siengchin S, Bhat KS (2020) Influence of wood dust fillers on the mechanical, thermal, water absorption and biodegradation characteristics of jute fiber epoxy composites. J Polym Res 27(1):1–13

    Article  Google Scholar 

  90. Nahavandi S(2019) Industry 5.0—A human-centric solution. Sustainability, 11(16), p.4371

  91. Kumar TSM, Kumar KS, Ra**i N, Siengchin S, Ayrilmis N, Rajulu AV(2019) A comprehensive review of electrospun nanofibers: Food and packaging perspective. Composites Part B: Engineering, 175, p.107074

  92. Syafrudin M, Alfian G, Fitriyani NL, Rhee J(2018) Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors, 18(9), p.2946

  93. Luo J, Liang Z, Zhang C, Wang B (2001) Optimum tooling design for resin transfer molding with virtual manufacturing and artificial intelligence. Compos Part A: Appl Sci Manufac 32(6):877–888

    Article  Google Scholar 

  94. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press

  95. Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS (2019) Minimum sample size for develo** a multivariable prediction model: PART II-binary and time‐to‐event outcomes. Stat Med 38(7):1276–1296

    Article  MathSciNet  Google Scholar 

  96. Siengchin S, Karger-Kocsis J (2006) Creep Behavior of Polystyrene/Fluorohectorite Micro‐and Nanocomposites. Macromol Rapid Commun 27(24):2090–2094

    Article  Google Scholar 

  97. Psarras GC, Siengchin S, Karahaliou PK, Georga SN, Krontiras CA, Karger-Kocsis J (2011) Dielectric relaxation phenomena and dynamics in polyoxymethylene/polyurethane/alumina hybrid nanocomposites. Polym Int 60(12):1715–1721

    Article  Google Scholar 

  98. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  MATH  Google Scholar 

  99. Crawford B, Khayyam H, Milani AS, Jazar RN (2020) Big data modeling approaches for engineering applications. Nonlinear Approaches in Engineering Applications. Springer, Cham, pp 307–365

    Chapter  Google Scholar 

  100. Siengchin S, Karger-Kocsis J (2013) Binary and ternary composites of polystyrene, styrene–butadiene rubber and boehmite produced by water-mediated melt compounding: Morphology and mechanical properties. Compos Part B: Eng 45(1):1458–1463

    Article  Google Scholar 

  101. Luo L, Zhang B, Zhang G, Li X, Fang X, Li W, Zhang Z (2021) Rapid prediction and inverse design of distortion behaviors of composite materials using artificial neural networks. Polym Adv Technol 32(3):1049–1060

    Article  Google Scholar 

  102. Zobeiry N, Humfeld KD(2021) A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Engineering Applications of Artificial Intelligence, 101, p.104232

  103. Chen YX, Wang LC, Chu PC (2020) A recipe parameter recommendation system for an autoclave process and an empirical study. Procedia Manuf 51:1046–1053

    Article  Google Scholar 

  104. Golkarnarenji G, Naebe M, Badii K, Milani AS, Jazar RN, Khayyam H (2019) A machine learning case study with limited data for prediction of carbon fiber mechanical properties. Comput Ind 105:123–132

    Article  Google Scholar 

  105. Davim JP (ed) (2012) Computational Methods for Optimizing Manufacturing Technology: Models and Techniques: Models and Techniques. IGI Global

  106. Mesogitis TS, Skordos AA, Long AC (2014) Uncertainty in the manufacturing of fibrous thermosetting composites: A review. Compos Part A: Appl Sci Manufac 57:67–75

    Article  Google Scholar 

  107. Marmolejo-Saucedo JA, Rodriguez-Aguilar R, Perea UAR, Vaqueiro MG, Hernandez RR, Ramirez FS, Martinez AP(2021) September. Improving a Manufacturing Process using Recursive Artificial Intelligence. In IFIP International Conference on Advances in Production Management Systems (pp. 266–275). Springer, Cham

  108. Savu T, Abaza BF, Spanu P (2014) Artificial Intelligence based System for the Real-time Control of Polymerization Processes. MATERIALE PLASTICE 51(3):343–346

    Google Scholar 

  109. Veluri S, Kumar R, Vasudevan R, Gorur RP, Nampuraja E, Shankaraiah M, Tanjore S, Rao S(2018) Improving Manufacturing Efficiencies through Industry 4.0 Technologies in Aerospace (No. 2018-01-1929). SAE Technical Paper

  110. Meister S, Wermes MA, Stüve J, Groves RM (2021) June. Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the automated fiber placement process. Automated Visual Inspection and Machine Vision IV, vol 11787. International Society for Optics and Photonics, p 1178705

  111. He L, Aouf N, Song B (2021) Explainable Deep Reinforcement Learning for UAV autonomous path planning. Aerosp Sci Technol 118:107052

    Article  Google Scholar 

  112. Ahmed I, Jeon G, Piccialli F (2022) From Artificial Intelligence to eXplainable Artificial Intelligence in Industry 4.0: A survey on What, How, and Where. IEEE Transactions on Industrial Informatics

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Acknowledgements

This research was supported by King Mongkut’s University of Technology North Bangkok through funding support from the National, Science and Innovation fund (NSRF) with Grant no. of KMUTNB-FF-68-19.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India

    M. Priyadharshini

  2. Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    V. Bhuvaneswari & L. Rajeshkumar

  3. Natural Composites Research Group Lab, Department of Materials and Production Engineering, The Sirindhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok, Thailand

    M. R. Sanjay & Suchart Siengchin

  4. Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    D. Balaji

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Priyadharshini, M., Balaji, D., Bhuvaneswari, V. et al. Fiber Reinforced Composite Manufacturing With the Aid of Artificial Intelligence – A State-of-the-Art Review. Arch Computat Methods Eng 29, 5511–5524 (2022). https://doi.org/10.1007/s11831-022-09775-y

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