• 文献标题:   A novel methodology of Combined Compromise Solution and Principal Component Analysis (CoCoSo-PCA) for machinability investigation of graphene nanocomposites
  • 文献类型:   Article
  • 作  者:   KUMAR J, VERMA RK
  • 作者关键词:   graphene, drilling, carbon, circularity error, cocoso
  • 出版物名称:   CIRP JOURNAL OF MANUFACTURING SCIENCE TECHNOLOGY
  • ISSN:   1755-5817
  • 通讯作者地址:  
  • 被引频次:   10
  • DOI:   10.1016/j.cirpj.2021.03.007 EA MAR 2021
  • 出版年:   2021

▎ 摘  要

The interest in the application of Graphene oxide/polymer nanocomposites is recently rising in various industries such as drug delivery, bioimaging, aircraft structures, sensors, battery application, etc., due to the improved features of Graphene oxide. The machining behavior of polymers is remarkably different from metallic alloys due to anisotropic and high synergistic effects. This article investigates the machinability aspect and machining response optimization of the developed hybrid nanocomposites. The drilling responses, namely, average surface roughness (Ra), Mean roughness depth (Rz), and Circularity error (Ce), were optimized by using the integrated approach of Combined Compromise Solution and Principal Component Analysis (CoCoSo-PCA). Response Surface Methodology (RSM) array is utilized to execute the drilling tests. The optimal setting is obtained as drill speed 2400 rpm, feed 80 mm/min, 1 weight% of Graphene oxide (G%). The feed rate shows a primary role in controlling both the surface roughness indices and Circularity error. The high feed value affects the development of the drilling-induced defect and cracks. The SEM analysis of the machined samples was performed to check the machined hole quality and surface finishing. ANOVA estimates the model adequacy of the proposed hybrid model. The obtained results have been validated by a confirmatory test, which proves the proposed hybrid module practicality in the manufacturing environment. Besides, the proposed approach is compared with the recently developed optimization modules to evaluate the application potential. (C) 2021 CIRP.