Recent development of microwave applications for concrete treatment

The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites. Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research. 

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Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Search for novel energy-efficient, eco-friendly, and time-saving manufacturing techniques has gained momentum in the recent times. Polymer composites have been increasingly adapted for variety of industrial applications, however, their efficient processing remains challenging. Microwave curing technique has been employed to process thermoset-based composites since 1980, however, processing of thermoplastic-based composites with natural-fibers as reinforcement, calls for additional data and hence investigations. The current work presents an overview of the basics of microwave heating and the physics behind the microwave processing of polymer composites. A state-of-the-art on microwave processing of thermoset and thermoplastic-based composites and developments in microwave joining has been reviewed. The major constraints in adopting microwave technology for curing composites at the industry scale are highlighted. The article also highlights the challenges during microwave processing of sustainable thermoplastic-based polymer composites. Future scope of work to 3D print the polymer matrix composite parts, through microwave scanning route has been indicated. 

Processing of polymer matrix composites using microwave energy: A review

Microwaves are a form of electromagnetic radiation commonly used for telecommunications, navigation and food processing. More recently microwave technologies have found applications in fibre-reinforced polymer composites, which are increasingly used in aircraft structures. Microwave energy can be applied with low power (up to milliwatts) for non-destructive testing and high power (up to kilowatts) for heating/curing purposes. The state-of-the-art applications at high power include curing, three-dimensional (3D) printing, joining and recycling, whereas low-power microwave techniques can provide quality checks, strain sensing and damage inspection. Low-power microwave testing has the advantage of being non-contact, there is no need for surface transducers or couplants, it is operator friendly and relatively inexpensive; high-power microwave energy can offer volumetric heating, reduced processing time and energy saving with no ionising hazards. In this paper the recent research progress is reviewed, identifying achievements and challenges. First, the critical electromagnetic properties of composites that are closely related to the heating and sensing performance are discussed. Then, representative case studies are presented. Finally, the trends are outlined, including intelligent/automated inspection and solid-state heating.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0001924020000913

Review of microwave techniques used in the manufacture and fault detection of aircraft composites

Multidirectional carbon fiber reinforced polymer (CFRP) composites, stacked by multiple layers of unidirectional prepregs, are generally considered to be microwave reflective, but we have found that it is not always the case. In this paper, the microwave response of many CFRP laminates has been investigated, considering the effect of the fiber orientation and the laminate thickness. An analytical model was developed to establish the relationship between the lay-up configuration and the effective impedance of composite laminates. The result indicated that if carbon fibers inside the headmost layer(s) are parallel to the incident electric field, the material will reflect the microwave power regardless of the subsequent fiber orientation and laminate thickness, due to the high conductivity of carbon fibers in the axial direction. Otherwise, the response of the CFRP laminates will strongly associate with the position of its internal cross-ply and the laminate thickness, as they have a significant influence on the impedance of the laminate. When the position of the cross-ply is far from the surface radiated by microwave, the multidirectional CFRP laminate can be changed to microwave absorbers, because it has a relatively matched impedance with air. The findings in this paper may be applied to design lay-up configurations of CFRP laminates for a specific microwave performance, besides mechanical properties.

Effect of lay-up configuration on the microwave absorption properties of carbon fiber reinforced polymer composite materials

Online learning based intelligent temperature control during polymer composites microwave curing process

The invention provides a microwave heating temperature field intelligent monitoring method based on historical data. The method is characterized in that a correlation between an arbitrary heating modeof an arbitrary component and a microwave control strategy is established based on a large amount of historical data and a deep learning algorithm, the temperature distribution of a material of the same layer of the component is monitored in real time in a microwave heating process, when a maximum temperature difference exceeds a set value, a heating mode for compensating current temperature distribution is quickly calculated based on a heating mode complementary idea, the parameters of a microwave system are adjusted in real time according to a corresponding control strategy, and uneven temperature distribution is accurately and intelligently compensated. The problem of microwave uneven heating is solved in principle, and the temperature uniformity of a heated object in the microwave heating process is significantly improved.

Microwave heating temperature field intelligent monitoring method based on historical data

The invention relates to an intelligent monitoring method for the microwave heating temperature field based on on-line learning, which comprises the steps of learning a dynamic association relation between a heating mode and a control strategy in the microwave heating process of a component in real time by adopting a neural network model, predicating a control strategy for compensating the currenttemperature distribution in real time according to a heating mode complementation idea based on the model so as to carry out accurate and intelligent compensation on the non-uniform temperature distribution, thereby realizing accurate control for the temperature uniformity of the component in the heating process.

Li Yingguang

Papers

Online learning based intelligent temperature control during polymer composites microwave curing process

Microwave heating temperature field intelligent monitoring method based on historical data

Intelligent monitoring method for microwave heating temperature field based on on-line learning