article · Hybrid Advances
Reinforced composites are widely chosen for developing lightweight industrial structures. Traditional techniques for developing these materials face limitations, prompting increased adoption of machine learning algorithms to improve both time efficiency and predictive accuracy. Recent developments over the past five years demonstrate how machine learning can be incorporated across composite materials technology. A structured protocol guides the implementation of these computational models, highlighting the critical role of data hygiene throughout the process. Furthermore, machine learning assists in both material selection and manufacturing process selection, supported by specific data sourcing methods. Various emerging digital tools and platforms now facilitate the execution of these algorithms. While these computational approaches offer clear benefits, ongoing research gaps continue to define the direction of future investigations into machine learning-aided composite design.
Lightweight reinforced composites are crucial across modern manufacturing and structural engineering. Developing these materials through conventional experimentation is often slow and constrained. Applying machine learning speeds up the design pipeline and enhances predictive performance, enabling engineers to optimise materials and production processes more reliably while identifying existing technical limitations that require further development.
The integration of machine learning into composite design could benefit engineers and manufacturers developing industrial lightweight structures by streamlining material and process selection. Because this work is a review identifying research gaps and assessing emerging digital tools alongside methodological protocols, the overall field appears to be at an early research stage rather than delivering a near-market commercial tool.
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Reinforced composite is a preferred choice of material for the design of industrial lightweight structures. As of late, composite materials analysis and development utilizing machine learning algorithms have been getting expanding consideration and have accomplished extraordinary upgrades in both time productivity and expectation exactness. This review encapsulates recent advances in machine learning-based design of reinforced composite during the last half-decade. It summarizes the limitations of traditional methods of reinforced composite development and presents a detailed protocol of machine learning in composite materials technology; implementation of machine learning algorithms in reinforced composite material design was covered, with an emphasis on the importance of data hygiene. Machine learning integration in material and process selection, and data sourcing techniques for machine learning-based design were also examined. The evaluation also looked at emerging digital tools and platforms for implementing machine learning algorithms. In addition, an essential effort was made to identify research gaps and define areas for further research. This review is indeed designed to provide some direction for future research into the use of machine learning for composite material design.
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DOI: 10.1016/j.hybadv.2023.100026
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