Di Li

Bio: Di Li is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Microwave & Curing (chemistry). The author has an hindex of 3, co-authored 5 publications receiving 14 citations.

A Novel Method to Improve Temperature Uniformity in Polymer Composites Microwave Curing Process through Deep Learning with Historical Data

Abstract: Compared with processing methods using conductive heating, microwave processing technology has many advantages such as its extremely short processing time and low energy consumption. However, the uneven temperature on the composite surface resulted from the uneven electromagnetic field distribution have become a big problem. Because the traditional model-based approach was difficult to establish the relationship between the composite temperature behaviors and microwave control strategies, existing methods mainly alleviated this problem by generating a relative movement between the microwave field and the object being heated, which cannot essentially achieve a uniform temperature distribution due to the uncertainty of the random compensation principle. In this paper, a data-driven method was proposed to solve this problem using an optimized convolutional neural network with extensive historical data. On this basis, the monitored uneven temperature distribution on the composite surface was accurately compensated in real time. Experimental results indicated that a reduction of ~53% in temperature difference was achieved compared with existing methods.

Microwave heating temperature field intelligent monitoring method based on historical data

Abstract: 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.

A new concept to improve microwave heating uniformity through data-driven process modelling

Abstract: For a long time, the heating pattern of the workpiece within a multimode microwave oven was considered to be highly sophisticated. As a consequence, the uneven microwave heating problem can only be partly alleviated by a random movement between the electromagnetic field and the workpiece. In this paper, we reported that the heating pattern has a specific correspondence with microwave system settings. The influence factor of the heating pattern and the corresponding mechanism were systematically studied by both theoretical analysis and experimental investigations. On this basis, a data-driven process model was established to learn the material’s dynamic temperature behaviors under different microwave system settings, and a new concept to improve the microwave heating uniformity by temperature monitoring and active compensation was proposed. The effectiveness of the method was demonstrated by a polymer composite microwave processing case study.

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

Abstract: 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.

Composites Part A: Applied Science and Manufacturing

Abstract: Reference EPFL-ARTICLE-184271doi:10.1016/j.compositesa.2012.08.001View record in Web of Science Record created on 2013-02-27, modified on 2017-05-10

Continuous fibre-reinforced thermoplastic acrylic-matrix composites prepared by liquid resin infusion – a review

Abstract: Increasing demand for lightweight materials is a major driving force for the steady growth of the continuous fibre-reinforced polymer composite industry. In recent years, strict global targets demanding greater environmental responsibility have led to a shift in research focus to address the end-of-life challenges posed by the use of thermoset matrices. Thermosets offer lower-cost processibility than thermoplastics, which historically required cost- and energy-intensive production methodologies. Consequently, despite their well-demonstrated recyclability, thermoformability and weldability, thermoplastics are yet to attain the same technological maturity as thermosets. In situ polymerisable thermoplastic resins have been identified as attractive emerging solutions for improving the processibility of thermoplastics. Thus, are essential materials in meeting the demand for fibre-reinforced thermoplastic composites. This review presents a comprehensive summary of recent works on room-temperature-processible liquid thermoplastic acrylic resins and their composites. Moreover, open problems and research opportunities are identified and discussed.

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

Abstract: 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.

Processing of polymer matrix composites using microwave energy: A review

Abstract: 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.