Journal of Polymer & Composites Original Research
Machine Learning-Based Optimization of Additive Manufacturing with Improved Mechanical Performance: Development of Sustainable Thermoplastic Composites
Abstract
Additive manufacturing has successfully manufactured lightweight, programmable and geometrically complicated components. However, reliable mechanical performance and efficient use of resources remain hard. This paper presents a machine learning (ML) based approach to optimize additive fabrication of sustainable thermoplastic composites with enhanced mechanical characteristics. The suggested approach combines material composition, printing settings and process circumstances to forecast and optimize tensile strength, flexural strength, impact resistance and dimensional stability. The aim is to reduce the consumption of materials and increase the structural performance by employing eco-friendly fillers embedded in sustainable thermoplastic matrices. The standard ML models are trained with the experimental data of the major manufacturing parameters such as the extrusion temperature, layer height, printing speed, infill density, raster direction, and reinforcement concentration. Regression and artificial intelligence techniques are applied to identify the intricate links between processing circumstances, composite properties and ultimate mechanical performance. The predictions of the model are validated by statistical performance criteria and experimental validation. The optimization framework aims at finding the best combination of material and printing parameters that would improve the mechanical performance, while keeping the processing efficiency and lowering the material usage. The results show the machine learning can anticipate the mechanical behavior effectively and help the optimization of parameters compared to the conventional trial and error approaches. The solution we developed gives a data driven path to increase reliability, sustainability and performance of thermoplastic composite parts produced by additive manufacturing processes. The study shows the possibility of intelligent optimization for sustained high performance applications in additive manufacturing.
Keywords
References (29)
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