A review on computational intelligence methods for modeling of light weight composite materials.

Light weight composite materials (LWCM) have gained tremendous attention, thanks to their low cost, eco-friendly nature, biodegradability, life-cycle superiority, noble mechanical properties for polymer composites and excellent electrical properties for carbon composites. LWCM are widely used in various engineering fields including aerospace, automobile, civil and architecture. The diversified applications of LWCM demand an extensive investigation of their mechanical and structural behavior under elevated conditions. Therefore, modeling of LWCM for mechanical and structural properties is a challenging task. Nowadays, computational intelligence (CI) methods are ones of the immensely growing topics in materials data science including materials imaging, feature identification, process and quality control, prediction, uncertainty quantification and design optimization. CI has not only been emerged as a revolutionary way to dig out material-related information from various data sources to serve next generation composite materials with unprecedented properties but also as an active surrogate model for conventional experimental methods. CI techniques are able to increase the quality of LWCM by optimizing the characteristics of individual constituent, their orientation, volume fractions, laminas and thickness using an adequate model. The process of designing, controlling and optimizing LWCM with distinguished properties has been redefined by various CI methods. In this paper, a high-level overview of CI methods has been introduced from the beginning of raw material selection to the final output performance of LWCM, discusses the strength, challenges and limitations of CI methods for LWCM, and proposes solutions and future directions. • A new approach is discussed regarding artificial intelligence (AI). • Composite materials and their modeling with AI are discussed. • New algorithms are introduced for fiber reinforced composites. • Golden eagle, ANN, crow search algorithms are elaborated. [ABSTRACT FROM AUTHOR]