Search

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large. [ABSTRACT FROM AUTHOR]

In industrial composites applications, drilling is one of the most common operations and complex processes during the final assembly, which can generate undesirable damage to the manufactured part. Data collection from a given composite's fatigue life is often costly and time-consuming. To address this challenge, the current case study aims to adapt a hybrid machine learning framework to predict the fatigue life of the drilled Glass Fiber Reinforced Polymer composite laminates (with both unidirectional and woven lay-ups) under a limited and noisy data assumption. Composite specimens were drilled at various cutting speeds and feed rates. The size of the delamination around the hole was scanned by a microscopic camera. Cyclic three-point bending tests were conducted, and results indicated that the drilling-induced delamination size and the composite lay-up affect the specimens' fatigue lives. The latter were then modeled in two steps. In the first step, an offline deterministic model was established using the group method of data handling along with a singular value decomposition. Pareto multi-objective optimization was applied to prevent overfitting. In the second step, the Kalman filter was employed to update the polynomial of the deterministic model based on minimizing the mean and variance of error between the actual and modeled data. Results showed an excellent learning reliability, with a correlation coefficient of 97.6% and 96.5% in predicting the fatigue life of unidirectional and woven composite laminates, respectively. A sensitivity analysis was performed and indicated that the fatigue life of the samples has been more affected by the drilling feed rate, compared to the cutting speed. [ABSTRACT FROM AUTHOR]

Polymer-based materials are extensively used in various applications such as aircrafts, civilian structures, oil and gas platforms and electronics. They are, however, inherently damage prone and over time, the formation of cracks and microscopic damages influences the thermo-mechanical and electrical properties, which eventually results in the total failure of the materials. This paper provides an overview of the principal causes of cracking in polymer and composites and summarizes the recent progress in the development of non-destructive techniques in crack detection. Furthermore, recent progress in the development of bio-inspired self-healing methods in autonomic repair is discussed. [ABSTRACT FROM PUBLISHER]

Electrospun P(VDF-TrFE) nanogenrators with a wide range of piezoelectricity performance and physiochemical properties is fabricated through modification of the processing parameters such as polymer concentration, applied voltage, feed rate and electrospinning time/fibres mat thickness. In order to estimate and predict the relationships of the process parameters with the piezoelectricity performance and fibres morphology, an Artificial Neural Networks (ANN) model is developed. Results of the developed ANN model is found to be in a good agreement with experimental results with less than 5% error and shows the good potential to model physiochemical properties of the nanogenrators to predict untested conditions. [ABSTRACT FROM AUTHOR]