A novel approach for optimization of Machining characteristics of polymer nanocomposites

EasyChair Preprint 4821

Abstract

Glass fiber polymer composites possess a broad spectrum of applications in spacecraft, automotive, marine, and sports components. Due to enhanced features, this material replacing the conventional engineering materials and their alloys. The machining behavior of their materials requires more attention for proper utilization and makes them cost-effective. Some critical issues such as fiber pull out, matrix debonding, resin pulls out, etc. due to anisotropic and abrasive nature. It can be overcome by machining performance optimization using hybrid modules. This paper present an experimental investigation on machining (Milling) of multiwall carbon nanotube (MWCNT) doped epoxy /GFRP composites and effect of process parameters viz. spindle speed (S), feed rate (F), depth of cut (D), MWCNT weight % (R%) on machining performances such as MRR, cutting force (Fc), and Surface roughness (Ra) has been examined. Taguchi based L9 orthogonal array was employed to execute the machining. A relatively advanced combined approach of Data Envelopment Analysis based Ranking (DEAR)and Taguchi was used to tackling critical issues of multiple conflicting responses. The optimal condition of the DEAR-Taguchi approach are found at S2450F85D0.6R2%. It has been validated through a confirmatory test, which shows satisfactory improvement in machining performance. This improvement is highly desired for an efficient machining environment.

Keyphrases: DEAR, GFRP, MWCNT, Milling, Optimization, Taguchi

@booklet{EasyChair:4821,
  author    = {Kuldeep Kumar and Jogendra Kumar and Vijay Kumar Singh and Rajesh Kumar Verma and Abhishek Singh},
  title     = {A novel approach for optimization of Machining characteristics of polymer nanocomposites},
  howpublished = {EasyChair Preprint 4821},
  year      = {EasyChair, 2020}}