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A Novel Method to Improve Temperature Uniformity in Polymer Composites Microwave Curing Process through Deep Learning with Historical Data

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Abstract

Compared with processing methods using conductive heating, microwave processing technology has many advantages such as its extremely short processing time and low energy consumption. However, the uneven temperature on the composite surface resulted from the uneven electromagnetic field distribution have become a big problem. Because the traditional model-based approach was difficult to establish the relationship between the composite temperature behaviors and microwave control strategies, existing methods mainly alleviated this problem by generating a relative movement between the microwave field and the object being heated, which cannot essentially achieve a uniform temperature distribution due to the uncertainty of the random compensation principle. In this paper, a data-driven method was proposed to solve this problem using an optimized convolutional neural network with extensive historical data. On this basis, the monitored uneven temperature distribution on the composite surface was accurately compensated in real time. Experimental results indicated that a reduction of ~53% in temperature difference was achieved compared with existing methods.

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Abbreviations

MCS :

microwave control strategy

HP :

heating pattern

X :

heating rate/temperature

X ′ :

relative heating rate/temperature

Ave :

average value of the component

Max :

maximum value of the component

δ :

standardization coefficient

E :

loss

p :

batch size

δ ij :

component of the label MCS

\( {\hat{\delta}}_{ij} \) :

component of the MCS predicted by the CNN model

w new :

weight after update

w old :

weight before update

Δw(q):

update value

η :

learning rate

α :

tuning factor

Average error rate:

prediction error rate of the CNN model on the test set

Accuracy :

prediction accuracy of the CNN model on the test set

N :

sample size of the test set

l :

number of magnetrons

ΔTmax :

preset temperature threshold

r1, r2 :

acceleration coefficient

NC1, NC2, NC3 :

number of filters in the 3 convolutional layers

Nh1, Nh2 :

number of filters in the 2 hidden layers

\( {\mathbf{P}}_i^k \) :

position of the i-th particle on the k-th step

\( {\mathbf{V}}_i^k \) :

velocity of the i-th particle on the k-th step.

\( {\mathbf{P}}_{i, Best}^k \) :

best position recorded by the i-th particle on the k-th step

\( {\mathbf{P}}_{Best}^k \) :

best position of the particle swarm on the k-th step

References

  1. Menéndez, J.A., Arenillas, A., Fidalgo, B., Fernández, Y., Zubizarreta, L., Calvo, E.G., Bermúdez, J.M.: Microwave heating processes involving carbon materials. Fuel Process. Technol. 91(1), 1–8 (2010)

    Google Scholar 

  2. Green, J.E., Nuhiji, B., Zivtins, K., Bower, M.P., Grainger, R.V., Day, R.J., Scaife, R.J.: Internal model control of a domestic microwave for carbon composite curing. IEEE T. Microw. Theory. 65(11), 4335–4346 (2017)

    Google Scholar 

  3. Beaumont, P.W.R., Soutis, C., Johnson, A.: Fitness considerations for contemporary composite materials: (Who’s afraid of the composite micro-crack?). Appl. Compos. Mater. 24(6), 1265–1285 (2017)

    Google Scholar 

  4. Lachaud, F., Espinosa, C., Michel, L., Rahme, P., Piquet, R.: Modelling strategies for predicting the residual strength of impacted composite aircraft fuselages. Appl. Compos. Mater. 22(6), 599–621 (2015)

    Google Scholar 

  5. Nuhiji, B., Swait, T., Bower, M., Green, J.E., Day, R.J., Scaife, R.J.: Tooling materials compatible with carbon fibre composites in a microwave environment. Compos. Part B-Eng. 163, 769–778 (2019)

    CAS  Google Scholar 

  6. Li, Y., Cheng, L., Zhou, J.: Curing multidirectional carbon fiber reinforced polymer composites with indirect microwave heating. Int. J. Adv. Manuf. Tech. 97(1–4), 1137–1147 (2018)

    Google Scholar 

  7. Zhang, L., Li, Y., Zhou, J.: Anisotropic dielectric properties of carbon fiber reinforced polymer composites during microwave curing. Appl. Compos. Mater. 25(6), 1339–1356 (2018)

    CAS  Google Scholar 

  8. Zhou, J., Li, Y., Li, N., Liu, S., Cheng, L., Sui, S., Gao, J.: A multi-pattern compensation method to ensure even temperature in composite materials during microwave curing process. Compos. Part A-Appl. S. 107, 10–20 (2018)

    CAS  Google Scholar 

  9. Chinedum, O.M., Danning, L., Meng-Fang, L., Paul, D.L., Kali, B.K., Vijay, T.K., Hamed, Y.N.: Accelerated microwave curing of fibre-reinforced thermoset polymer composites for structural applications: a review of scientific challenges. Compos. Part A-Appl. S. 115, 88–103 (2018)

    Google Scholar 

  10. Kwak, M., Robinson, P., Bismarck, A., Wise, R.: Microwave curing of carbon–epoxy composites: penetration depth and material characterization. Compos. Part A-Appl. S. 75, 18–27 (2015)

    CAS  Google Scholar 

  11. Mishra, R.R., Sharma, A.K.: Microwave-material interaction phenomena: heating mechanisms, challenges and opportunities in material processing. Compos. Part A-Appl. S. 81, 78–97 (2016)

    CAS  Google Scholar 

  12. Feher, L.E., Thumm, M.K.: Microwave innovation for industrial composite fabrication-the HEPHAISTOS technology. IEEE T. Plasma. Sci. 32(1), 73–79 (2004)

    CAS  Google Scholar 

  13. Sturm, G.S.J., Stefanidis, G.D., Verweij, M.D., Van Gerven, T.D.T., Stankiewicz, A.I.: Design principles of microwave applicators for small-scale process equipment. Chem. Eng. Process. 49(9), 912–922 (2010)

    CAS  Google Scholar 

  14. Chen, X., Zhan, L., Huang, M., Chang, T., Li, S., Peng, W.: A novel method for curing carbon fiber reinforced plastics by high-pressure microwave. Fiber. Polym. 17(12), 2143–2152 (2016)

    CAS  Google Scholar 

  15. Veronesi, P., Leonelli, C., Grisoni, F.: Numerical simulation of an industrial microwave assisted filter dryer: criticality assessment and optimization. J. Microwave Power EE. 41, 4–12 (2007)

    Google Scholar 

  16. Chandrasekaran, S., Ramanathan, S., Basak, T.: Microwave material processing—a review. AICHE J. 58(2), 330–363 (2012)

    CAS  Google Scholar 

  17. Plaza-González, P., Monzó-Cabrera, J., Catalá-Civera, J.M., Sánchez-hernández, D.: Effect of mode-stirrer configurations on dielectric heating performance in multimode microwave applicators. IEEE T. Microw. Theory. 53(5), 1699–1706 (2005)

    Google Scholar 

  18. Ku, H.S.L., Yusaf, T.: Processing of composites using variable and fixed frequency microwave facilities. Prog. Electromagn. Res. 5, 185–205 (2008)

    Google Scholar 

  19. Cuccurullo, G., Giordano, L., Albanese, D., Cinquanta, L., Di Matteo, M.: Infrared thermography assisted control for apples microwave drying. J. Food Eng. 112(4), 319–325 (2012)

    Google Scholar 

  20. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines [C]//Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807–814 (2010)

    Google Scholar 

  21. Weimer, D., Scholz-Reiter, B., Shpitalni, M.: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann-Manuf. Techn. 65(1), 417–420 (2016)

    Google Scholar 

  22. Wang, P., Gao, R.X., Yan, R.: A deep learning-based approach to material removal rate prediction in polishing. CIRP Ann-Manuf. Techn. 66(1), 429–432 (2017)

    Google Scholar 

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Acknowledgements

This project was supported by National Natural Science Foundation of China (Grant no. 51875288 and no. 51575275), jointly supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0282). The authors sincerely appreciate the continuous support provided by our industrial collaborators.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Di Li, Yingguang Li, Jing Zhou & Zhiwei Zhao

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  1. Di Li
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  2. Yingguang Li
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  3. Jing Zhou
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  4. Zhiwei Zhao
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Correspondence to Yingguang Li.

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Li, D., Li, Y., Zhou, J. et al. A Novel Method to Improve Temperature Uniformity in Polymer Composites Microwave Curing Process through Deep Learning with Historical Data. Appl Compos Mater 27, 1–17 (2020). https://doi.org/10.1007/s10443-019-09791-5

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  • DOI: https://doi.org/10.1007/s10443-019-09791-5

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