scholarly journals Experimental Investigation and Optimization of Delamination Factors in the Drilling of Jute Fiber Reinforced Polymer Biocomposites with Multiple Estimators

Author(s):  
Bachir Adda ◽  
Ahmed Belaadi ◽  
Messaouda Boumaaza ◽  
Mostefa Bourchak

Abstract Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used are limited to conventional machining, called chip removal machining, such as drilling. Delamination factors are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three Fd evaluation methods that have been most frequently used in previous investigations. To this end, three rotational and feed speeds and three BSD tool diameters were selected (L27) for drilling 155 g/m2 density jute fabric reinforced polyester biocomposites. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions.

2021 ◽  
Vol 11 (8) ◽  
pp. 3429
Author(s):  
Željka Beljkaš ◽  
Nikola Baša

Deflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, particularly when it comes to continuous beams. Machine learning methods can be applied for predicting a deflection level on continuous beams with GFRP (glass fiber-reinforced polymer) reinforcement and loaded with a concentrated load. This paper presents research on using artificial neural networks for deflection estimation and an optimal prediction model choice. It was necessary to first develop a database, in order to train the neural network. The database was formed based on the results of the experimental research on continuous beams with GFRP reinforcement. Using the best trained neural network model, high accuracy was obtained in estimating deflection, expressed over the mean absolute percentage error, 9.0%. This result indicates a high level of reliability in the prediction of deflection with the help of artificial neural networks.


2017 ◽  
Vol 867 ◽  
pp. 41-47 ◽  
Author(s):  
Chitra Umachitra ◽  
N.K. Palaniswamy ◽  
O.L. Shanmugasundaram ◽  
P.S. Sampath

Natural fibers have been used to reinforce materials in many composite structures. Many types of natural fibers have been investigated including flax, hemp, ramie, sisal, abaca, banana etc., due to the advantage that they are light weight, renewable resources and have marketing appeal. These agricultural wastes can also be used to prepare fiber reinforced polymer hybrid composites in various combinations for commercial use. Application of composite materials in structural applications has presented the need for the engineering analysis. The present work focuses on the fabrication of polymer matrix composites by using natural fibers like banana and cotton which are abundant in nature and analysing the effect of mechanical properties of the composites on different surface treatments on the fabric. The effect of various surface treatments (NaOH, SLS, KMnO4) on the mechanical properties namely tensile, flexural and impact was analyzed and are discussed in this project. Analysing the material characteristics of the compression moulded composites; their results were measured on sections of the material to make use of the natural fiber reinforced polymer composite material for automotive seat shell manufacturing.


2015 ◽  
Vol 19 (3) ◽  
pp. 04014062 ◽  
Author(s):  
Cheng-Cheng Zhang ◽  
Hong-Hu Zhu ◽  
Bin Shi ◽  
Fang-Dong Wu ◽  
Jian-Hua Yin

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