Simple Linear Regression (SLR) Model for Re-Bending Behavior of a Non-Crimp Dry Thick Carbon Fiber Fabrics

Authors

  • Hafeth Bu Jldain Omar Al-Mukhtar University
  • Mohamed Eltarkawe Omar Al-Mukhtar University
  • Gebreel Abdalrahman Omar Al-Mukhtar University

DOI:

https://doi.org/10.36602/ijeit.v10i2.35

Keywords:

Bending behavior, dry thick carbon fibers, non-crimp fabrics, Simple Linear Regression model

Abstract

During manufacturing process, fabric such as the non-crimp dry thick fabric (NCF) is bent and re-bent many times until the fabric takes the desirable shape in the mold. Understanding the behavior of dry NCFs requires conducting experimental bending tests which is expensive due to the cost of the carbon fabrics. This paper aimed to model the bending behavior of NCF using simple Linear Regression Model in which the bending moment force is used as a response to displacement. The data modeled were obtained from testing three samples each sample was tested three consecutive times. A total of nine simple linear regression models were created. These nine models showed strong correlation between bending force and extension. The results also showed that the fabric becomes more flexible when it is subjected to re-bending process. Good performance of these models was confirmed using cross-validation method indicating that all presented models in this study were able to predict the bending behavior of non-crimp dry thick fabric.

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References

[1] F. Edgren, D. Mattsson, L.E. Asp and J. Varna. “Formation of damage and its effect on non-crimp fabric reinforced composites loaded in tension”. Composites Science and Technology, 64 (5): 675–692, 2004.

[2] S. V. Lomov. “Non-crimp fabric composites: manufacturing, properties and applications”. Elsevier, 2011.

[3] H. Bu Jldain. “Single Curvature Bending of Structural Stitched Textile Reinforcements Part I: Experimental Work”. The International Journal of Engineering and Information Technology, 7(2): 84–93, 2021.

[4] D.J. Wheeler. “Understanding industrial experimentation”. SPC Press, 1990.

[5] H. Bu Jldain. “Single Curvature Bending of Structural Stitched Textile Reinforcements Part II: Quantitative Analysis”. The International Journal of Engineering and Information Technology, 8(1): 44–49, 2021.

[6] Sahoo, S. and Jha, M. K. “Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment”. Hydrogeology Journal, 21: 1865-1887, 2013.

[7] Chetchotisak, P., Teerawong, J. and Yindeesuk, S. “Multiple linear regression models for shear strength prediction and design of simply-supported deep beams subjected to symmetrical point loads”. KKU Engineering Journal. 42(3): 219-225, 2015.

[8] Osborne, Jason W., and Elaine Waters. "Four assumptions of multiple regression that researchers should always test." Practical assessment, research, and evaluation 8(1): 2, 2002.

[9] Ismail, Z. “Forecasting gold prices using multiple linear regression method”. American Journal of Applied Sciences, 6 (8): 1509-1514, 2009

[10] Gastwirth, Joseph L., Yulia R. Gel, and Weiwen Miao. "The impact of Levene’s test of equality of variances on statistical theory and practice." Statistical Science 24(3): 343-360, 2009..

[11] Zyprych-Walczak, Joanna, Alicja Szabelska, and Idzi Siatkowski. "Application of statistical tests in gene selection problems." Biometrical Letters 48(2) : 113-121, 2011.

[12] Pornpakdee, Yada, Kamon Budsaba, and Wararit Panichkitkosolkul. "An empirical comparison of homogeneity of variance tests." Supported by (263) 2014.

[13] Trivedi, D., Lotfi, A., and Rahn, C. D., “Geometrically exact models for soft robotic manipulators”. IEEE Transactions on Robotics, 24(4): 773-780, 2008.

[14] Walker, I. D., Mattfeld, R., Mutlu, A., Bartow, A., and Giri, N. “A novel approach to robotic climbing using continuum appendages in in-situ exploration”. IEEE Aerospace Conference, 1–9, 2012 .

[15] Satheeshbabu, S., and Krishnan, G. “Modeling the bending behavior of fiber reinforced pneumatic actuators using a pseudo rigid body model”. Journal of Mechanisms and Robotics. JMR(18-1179), 2019

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Published

2023-02-10

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Article

How to Cite

Simple Linear Regression (SLR) Model for Re-Bending Behavior of a Non-Crimp Dry Thick Carbon Fiber Fabrics. (2023). The International Journal of Engineering & Information Technology (IJEIT), 10(2), 158-163. https://doi.org/10.36602/ijeit.v10i2.35

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