Akademik Veri Yönetim Sistemi
JOURNAL OF SUPERCOMPUTING, sa.1, 2025 (SCI-Expanded)
In recent years, the high cost of searching and processing raw materials and the fact that raw material resources have come to the point of depletion force industry and science to seek different solutions. In addition, due to the necessity of increasing the quality, the tendency to composite production has increased. However, it is necessary to make improvements in parameters such as quality, time, and cost in composite production. The use of artificial intelligence technology in the production of composites is becoming more and more common. On the other hand, studies on the determination of raw material mixture ratios are not common. Such studies are usually carried out with experimental productions, which increases the production cost and prolongs the production process. In this study, it was studied on the proportional optimization of the mixture components of thermoplastic hybrid composite materials by using the particle swarm optimization algorithm. First, a dataset was created with the real values obtained from the experimental productions, and the raw material mixture ratios to be included in the mixture were determined by making simulation studies on this dataset. With these obtained ratios, new productions were made, and tests were applied on these productions. Test results have shown that the resulting products are more successful by over 95%. Thus, it has been proven that especially the use of artificial intelligence technology in the field of composites in pre-production stages such as raw material and mixture processes can both reduce the cost and increase the quality.