In the bustling world of advanced manufacturing, a groundbreaking study led by Mohammed Raffic Noor Mohamed, an aeronautical engineer from the Nehru Institute of Technology in Coimbatore, India, is set to revolutionize the way we think about 3D-printed composites, particularly in the energy sector. The research, published in the Journal of Engineered Fibers and Fabrics, delves into the intricate relationship between the process, structure, and properties of nylon aramid 3D printed composites, offering insights that could significantly impact the production of energy-related components.
Mohammed Raffic Noor Mohamed and his team embarked on a mission to identify the key parameters that influence the quality and performance of 3D-printed nylon aramid composites. Using a combination of experimental design and advanced machine learning techniques, they aimed to optimize the printing process for better outcomes. “Our goal was to understand how different printing parameters affect the final product and to find the optimal settings for achieving the best results,” Mohamed explained.
The study utilized Taguchi’s L 18 orthogonal array to consider six key FDM parameters: infill pattern, infill density, layer thickness, component orientation, print temperature, and raster angle. By printing rectangular samples with an Ultimaker FDM printer, the team assessed various properties, including face hardness, thickness regions, printing time, and component weight. The results were analyzed using ANOVA and the signal-to-noise ratio method to determine the most influential parameters.
One of the most striking findings was the significant impact of raster angle on hardness. “Raster angle contributed 50.09% to face hardness and 30.11% to hardness at the thickness area,” Mohamed noted. This discovery highlights the critical role of raster angle in determining the mechanical properties of the printed components. Additionally, layer thickness emerged as a crucial factor, contributing 81.95% to printing time and 42.09% to part weight.
The research also explored the use of supervised machine learning techniques to predict and optimize the printing process. The decision tree approach outperformed the k-nearest neighbor algorithm in predicting all four output responses, achieving classification accuracies ranging from 83.33% to 100%. This advancement in predictive modeling could lead to more efficient and cost-effective manufacturing processes.
The implications of these findings are vast, particularly for the energy sector. The ability to optimize the printing parameters of 3D-printed composites could lead to the development of stronger, lighter, and more durable components for energy infrastructure. This could result in more efficient energy production and distribution systems, reducing costs and environmental impact.
Mohammed Raffic Noor Mohamed’s research, published in the Journal of Engineered Fibers and Fabrics, marks a significant step forward in the field of additive manufacturing. As the industry continues to evolve, the insights gained from this study will undoubtedly shape future developments, paving the way for more innovative and sustainable solutions in the energy sector.
