Abstract: Fatigue life models are fundamental when assessing the integrity and reliability of engineering components made of metallic materials. Hence, a plethora of domain knowledge-driven models have been developed over the past centuries, pursuing the consistency with fatigue failure mechanisms and the rationality of mathematical expressions. They generally demonstrate physical significance and can describe the complex processes of fatigue damage evolution explicitly and comprehensively. However, with the increasing demand for the operational safety of critical components and high-performance structural materials emerging constantly, they are facing limitations in the aspects of predictive capability, application scope, and engineering practicality. As an alternative, data-driven models, under the impetus of Artificial Intelligence tide, have attained growing attention and found increasing applications in life-prediction issues under various loading patterns. Data-driven models feature their powerful ability to derive optimal explicit/implicit relationships between fatigue life with numerous influential factors, without suffering from human errors. Moreover, they can quickly discover the physical laws governing fatigue failure which are difficult to be clarified by domain knowledge-driven models. Nowadays, data-driven models are recognized as opening a new pathway for fatigue damage analysis and life prediction, being a hot spot in fatigue research. This paper reviews the progress of research in developing data-driven models for predicting the fatigue life of metallic materials. Different types of data-driven models, including pure data-driven models and knowledge informed data-driven models, are summarized, along with their distinct construction methodologies and application advantages. The future prospects and challenges in this field are also discussed.
Gan L, Wu H, Zhong Z. Advances in data-driven models for fatigue life prediction of metallic materials. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-025.
Abstract: Mechanical metamaterials are engineered materials with unconventional mechanical behavior that originates from artificially programmed microstructures along with intrinsic material properties. With tremendous advancement in computational and manufacturing capabilities to realize complex microstructures over the last decade, the field of mechanical metamaterials has been attracting wide attention due to immense possibilities of achieving unprecedented multi-physical properties which are not attainable in naturally-occurring materials. One of the rapidly emerging trends in this field is to couple the mechanics of material behavior and the unit cell architecture with different other multi-physical aspects such as electrical or magnetic fields, and stimuli like temperature, light or chemical reactions to expand the scope of actively programming on-demand mechanical responses. In this article, we aim to abridge outcomes of the relevant literature concerning mechanical and multi-physical property modulation of metamaterials focusing on the emerging trend of bi-level design, and subsequently highlight the broad-spectrum potential of mechanical metamaterials in their critical engineering applications. The evolving trends, challenges and future roadmaps have been critically analyzed here involving the notions of real-time reconfigurability and functionality programming, 4D printing, nano-scale metamaterials, artificial intelligence and machine learning, multi-physical origami/kirigami, living matter, soft and conformal metamaterials, manufacturing complex microstructures, service-life effects and scalability.
Sinha P, Mukhopadhyay T. Programmable multi-physical mechanics of mechanical metamaterials. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-012.
Abstract: For lighter, stronger and more flexible aerospace structures, the nonlinear phenomena observed during ground vibration tests and in-service operations are first sorted out. Two types of typical nonlinear structures-localised and distributed nonlinear structures—are then highlighted, the basic concepts of which are explained. Secondly, the vibration testing techniques developed for these nonlinear structures are compared, and the research progress is summarised from the perspective of frequency response test, pure modal test, free decay test and others. Finally, model updating procedures of the two types of nonlinear structures are analysed, with identification methods discussed. Future perspectives are pointed out and research suggestions are also highlighted. It is expected to provide a useful reference for the future development of vibration testing techniques and accurate modelling methods of nonlinear aerospace structures.
Wang X. Advances in vibration testing and model updating for nonlinear aerospace structures. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-011.
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