Lightweight structures and materials have fascinated massive attention for their impelling benefits in engineering issues, strengthening provisions in competitiveness, environment, cost, and safety over recent decades. As an effective method, hybrid schemes target to grab the characteristics and benefits of different components to boost their functional aspects in lightweight schemes, thereby improving their various material performance. Developing hybrid structures and materials for other applications in those fiber-based polymers, cellular, inorganic, organic, and metal fillers and their hybrid composition are broadly applied. This material attains novel visions in developing new lightweight hybrid structures for railway, automotive, aerospace, and marine applications. In this context, artificial intelligence-based techniques examine applications in materials and manufacturing industries for optimizing and forecasting better material characteristics. AI models trained from vast data sets that correlate function, properties, and structure under multiple hierarchical ranges have provided novel regions for rapid exploration of the design portions. The achievement of the AI-based materials design method depends on the generation or collection of a massive set of data, which is appropriately preprocessed with the help of basic information of materials underlying physical and chemical principles and a good selection of the AI model. Recent progress in AI methods has generated many opportunities for developing materials design approaches and longstanding mechanics issues.

 

Modeling and Material Design

Homogenization and simulation of materials have attained the main engineering tools and computational technology in material design and modeling. Furthermore, the detailed simulations achieve extensive computational support in that the time of processing unit increments exponentially as the materialistic and spatial increments. In principle, with inevitable expulsion, circumstantial and hierarchical multiscale modeling methods can not be selected in the industries due to their higher computational cost. The fast advancements in artificial intelligence technology and rapid growth in computational data have reproduced a broad selection of machine learning-based technologies to improve the accuracy and efficiency of simulations and their respective applications. Still, there is an excellent prospect of a revolution pushed by artificial intelligence methodology in computational mechanics and materials. In contrast, machine learning simulation and modeling are still in the toddler phase. In the future, more artificial intelligence-based algorithms can provide a complete solution to material design, homogenization of materials, and defect mechanics modeling, which may take over traditional modeling techniques.

 

Smart Manufacturing Perspective

Industry 5.0 encloses a novel industrial transformation, combining advanced manufacturing schemes with information mechanics. Industry 5.0 targets human-centric fabrication by assuming human attention as the focus of manufacturing and technology that combines industrial workers by generating their capabilities and knowledge. These mature data-collecting schemes have tended to move toward intelligent manufacturing techniques, including utilizing smart and novel materials. The innovative materials’ characteristics make them highly alluring for many lightweight structure applications. The combination of AI facilitates them to be efficiently applied in developing novel platforms to conquer shortcomings in the present structural industries. The applications of AI in intelligent manufacturing could decrease quality management, production, and logistics costs by up to 30%.

 

Challenges and Future Perspective

These schemes can come up with different complexities and challenges faced by the current companies. Depending on other dependent parameters, these schemes are contemplated to be met from lack of system compilation, financial problems, lack of return from investment in novel techniques, and security problems during the adjustment of novel schemes and promotion of existing industries with AI, materials, and innovative manufacturing techniques. The primary hurdle to AI-based materials development is the precise models and lack of datasets that can forecast and recommend novel materials. Advanced quantum mechanics and computational chemistry can support addressing the limitations by producing synthetic information. Future research requires to be administered in the following sectors:

1. We are producing publicly large material data sets which can be applied as the primary source for model testing and training.
2. Generating a bridge language that controls communication between the researchers and machine
3. We are generating novel AI models that can select a wide range of inorganic and organic materials for developing hybrid composites.
4. Density function theory and molecular dynamics presently depend on forecasting the characteristics of materials under the molecular and atomic level, which may be sufficient to recommend starting and building blocks materials.
5. AI-assisted material development needs a well-known model output and input factors that acquire a scientific consensus among scientists to record their outcomes under a consistent and systematic framework.
6. To attain sustainability by offering a chance for the novel material to be interconnected with the life cycle assessment characteristics and better-quality governance and social information within the supply networks.

 

Dr. Mohit Hemanth Kumar
Assistant Professor
Department of Mechanical Engineering
Alliance College of Engineering and Design
Alliance University