کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7122216 | 1461471 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures](/preview/png/7122216.png)
چکیده انگلیسی
In recent years, 3-D printing experts have laid emphasis on designing and printing the cellular structures, since the key advantages (high strength to weight ratio, thermal and acoustical insulation properties) offered by these structures makes them highly versatile to be used in aerospace and automotive industries. In the present work, an experimental study is firstly conducted to study the effects of the design parameters (wall thickness and cell size) on the mechanical properties i.e yield strength and modulus of elasticity (stiffness) of honeycomb cellular structures printed by fused deposition modelling (FDM) process. Further, three promising numerical modelling methods based on computational intelligence (CI) such as genetic programming (GP), automated neural network search (ANS) and response surface regression (RSR) were applied and their performances were compared while formulating models for the two mechanical properties. Statistical analysis concluded that the ANS model performed the best followed by GP and RSR models. The experimental findings were validated by performing the 2-D, 3-D surface analysis on formulated models based on ANS.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 116, February 2018, Pages 495-506
Journal: Measurement - Volume 116, February 2018, Pages 495-506
نویسندگان
Biranchi Panda, Marco Leite, Bibhuti Bhusan Biswal, Xiaodong Niu, Akhil Garg,