scholarly journals Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 825
Author(s):  
Kaixin Liu ◽  
Zhengyang Ma ◽  
Yi Liu ◽  
Jianguo Yang ◽  
Yuan Yao
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Carbon Fiber ◽  
Defect Detection ◽  
Principal Component ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Reinforced Polymer

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.

scholarly journals Recent Advances on the Design Automation for Performance-Optimized Fiber Reinforced Polymer Composite Components

2020 ◽  
Vol 4 (2) ◽  
pp. 61
Author(s):  
Yi Di Boon ◽  
Sunil Chandrakant Joshi ◽  
Somen Kumar Bhudolia ◽  
Goram Gohel
Keyword(s):  
Machine Learning ◽  
Polymer Composite ◽  
Design Automation ◽  
Material Design ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Reinforced Polymer ◽  
Applications Of Machine Learning

Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantage of this customizability, the design process for fiber-reinforced polymer composite components needs to be improved. Machine learning methods have been identified as potential techniques capable of handling the complexity of the design problem. In this review, the applications of machine learning methods in various aspects of structural component design are discussed. They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. A design automation framework for performance-optimized fiber-reinforced polymer composite components is also proposed. The proposed framework aims to provide a comprehensive and efficient approach for the design and optimization of fiber-reinforced polymer composite components. The challenges in building the models required for the proposed framework are also discussed briefly.

Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning

2020 ◽  
Vol 20 (6) ◽  
Author(s):  
Ziyang Zhang ◽  
Junchuan Shi ◽  
Tianyu Yu ◽  
Aaron Santomauro ◽  
Ali Gordon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Fiber ◽  
Flexural Strength ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Design Parameters ◽  
Fiber Reinforced ◽  
Reinforced Polymer ◽  
Continuous Carbon Fiber ◽  
Carbon Fiber Reinforced

Abstract Carbon fiber-reinforced polymer (CFRP) composites have been used extensively in the aerospace and automotive industries due to their high strength-to-weight and stiffness-to-weight ratios. Compared with conventional manufacturing processes for CFRP, additive manufacturing (AM) can facilitate the fabrication of CFRP components with complex structures. While AM offers significant advantages over conventional processes, establishing the structure–property relationships in additively manufactured CFRP remains a challenge because the mechanical properties of additively manufactured CFRP depend on many design parameters. To address this issue, we introduce a data-driven modeling approach that predicts the flexural strength of continuous carbon fiber-reinforced polymers (CCFRP) fabricated by fused deposition modeling (FDM). The predictive model of flexural strength is trained using machine learning and validated on experimental data. The relationship between three structural design factors, including the number of fiber layers, the number of fiber rings as well as polymer infill patterns, and the flexural strength of the CCFRP specimens is quantified.

Restoring Initially Cracked Reinforced Concrete Beams utilizing Carbon Fiber Reinforced Polymer Strips

2019 ◽  
Vol 7 (1) ◽  
pp. 30-34
Author(s):  
A. Ajwad ◽  
U. Ilyas ◽  
N. Khadim ◽  
Abdullah ◽  
M.U. Rashid ◽  
...  
Keyword(s):  
Carbon Fiber ◽  
Concrete Beams ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Loading Test ◽  
Control Beam ◽  
Reinforced Polymer ◽  
Cfrp Sheet ◽  
Carbon Fiber Reinforced

Carbon fiber reinforced polymer (CFRP) strips are widely used all over the globe as a repair and strengthening material for concrete elements. This paper looks at comparison of numerous methods to rehabilitate concrete beams with the use of CFRP sheet strips. This research work consists of 4 under-reinforced, properly cured RCC beams under two point loading test. One beam was loaded till failure, which was considered the control beam for comparison. Other 3 beams were load till the appearance of initial crack, which normally occurred at third-quarters of failure load and then repaired with different ratios and design of CFRP sheet strips. Afterwards, the repaired beams were loaded again till failure and the results were compared with control beam. Deflections and ultimate load were noted for all concrete beams. It was found out the use of CFRP sheet strips did increase the maximum load bearing capacity of cracked beams, although their behavior was more brittle as compared with control beam.

Sign in / Sign up

Export Citation Format

Share Document