Evaluation of Surface Roughness in Turning with Precision Feed for Carbon Fibre-Reinforced Plastic Composites Using Response-Surface Methodology and Fuzzy Logic Modelling

2017 ◽  
pp. 189-210
Author(s):  
K. Palanikumar ◽  
T. Rajasekaran
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Fuzzy Logic ◽  
Carbon Fibre ◽  
Response Surface ◽  
Plastic Composites ◽  
Reinforced Plastic ◽  
Carbon Fibre Reinforced Plastic

scholarly journals The interlaminar toughness of carbon-fibre reinforced plastic composites using ‘hybrid-toughened’ matrices

2006 ◽  
Vol 41 (15) ◽  
pp. 5043-5046 ◽  
Author(s):  
A. J. Kinloch ◽  
R. D. Mohammed ◽  
A. C. Taylor ◽  
S. Sprenger ◽  
D. Egan
Keyword(s):  
Carbon Fibre ◽  
Plastic Composites ◽  
Reinforced Plastic ◽  
Carbon Fibre Reinforced Plastic

Predicting the delamination factor in carbon fibre reinforced plastic composites during drilling through the Gaussian process regression

2020 ◽  
pp. 002199832098424
Author(s):  
Yun Zhang ◽  
Xiaojie Xu
Keyword(s):  
Carbon Fibre ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Machining Process ◽  
Machining Parameters ◽  
Machining Processes ◽  
Plastic Composites ◽  
Reinforced Plastic ◽  
Structural Applications ◽  
Carbon Fibre Reinforced Plastic

The carbon fibre reinforced plastic (CFPR) has been widely used in aircraft structural applications due to the superior modulus, specific tensile strength, and fatigue strength. The inhomogeneous and anisotropic nature of these composites poses great challenges on the machining process. Particularly, the delamination is one of major defects associated with drilling, which has a significant impact on CFRP’s structure integrity and application. Machine learning approaches can help facilitate the optimization of machining processes. In this study, we develop the Gaussian process regression (GPR) model to predict delaminations in carbon fibre reinforced plastic composites during drilling from machining parameters. The model is simple and highly accurate and stable that contributes to fast delamination estimations. By combining the optimization results from the Taguchi method and GPR approach, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time.

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