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Tensile, Flexural and Impact strength analysis of a 3D printed Carbon Fiber Reinforced Nylon Filament | ||
| Mechanics of Advanced Composite Structures | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 دی 1403 اصل مقاله (845.46 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/macs.2024.35556.1742 | ||
| نویسندگان | ||
| Javed Dhalait1؛ Vijay Kumar Jatti2؛ Shahid Tamboli* 3؛ Rakesh Motgi4 | ||
| 1A. G. Patil Polytechnic Institute, Solapur, Maharashtra. India. | ||
| 2Department of Mechanical Engineering, School of Engineering and Applied Sciences, Bennett University, India, | ||
| 3Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, Maharashtra, India | ||
| 4Department of Mechanical Engineering, A. G. Patil Polytechnic Institute, Solapur, Maharashtra. India. | ||
| تاریخ دریافت: 18 مهر 1403، تاریخ بازنگری: 30 آذر 1403، تاریخ پذیرش: 11 دی 1403 | ||
| چکیده | ||
| 3D printing is one of the most popular methods for prototyping and manufacturing lightweight and complex parts in recent years. The fused filament fabrication (FFF) method is the preferred due to its ease of operation. Different plastics can be used as additive materials, such as filaments. To enhance the mechanical properties of 3D printed products researchers are developing new composite materials. By varying the parameters associated with the manufacturing of these materials, mechanical properties can be altered. This study aimed to find out the effect of printing parameters in Carbon fiber reinforced Nylon to get better mechanical properties. In this study chopped carbon fibers are reinforced in Nylon base material to get the ‘FFF 3D printing’ filament material. Infill density and shell perimeter were varied to get different specimen types. The specimens were prepared as per the ASTM standards for the tensile, flexural, and impact testing. Machine learning is used to predict the parameters for tensile, flexural, and impact strength. The study shows the effect of printing parameters on mechanical properties like flexural strength and tensile strength. Infill percentage shows a significant effect on mechanical strength. The ML regression model shows higher accuracy for tensile strength than the flexural and impact strength. | ||
| کلیدواژه‌ها | ||
| Fused filament fabrication (FFF)؛ shell count؛ infill density؛ optimization؛ machine learning | ||
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آمار تعداد مشاهده مقاله: 88 تعداد دریافت فایل اصل مقاله: 73 |
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