An integrated module for predictive modelling and machinability appraisal during milling of modified graphene/epoxy nanocomposites

ABSTRACT

This article investigates the machining performance optimization of polymer nanocomposites modified by GO/Carbon fiber. The Milling responses, namely, surface roughness (Ra) and cutting force (Fc), are optimized by using the integrated approach of Grey theory and Principal component analysis based on Technique for Order of Preference by Similarity to Ideal Solution method (Grey-PCA-TOPSIS). The Taguchi orthogonal design was used for the Milling test experimentation of the proposed nanocomposite. The outcomes of Analysis of Variance (ANOVA) demonstrate the model adequacy of the proposed hybrid approach. The optimal setting was obtained as spindle speed 1600 rpm, feed rate 240 mm/min, depth of cut 0.5 mm and Graphene Oxide weight 1%. The analysis of means (ANOM) was used to appraise the significant factor and their effects on machining responses. The spindle speed works a primary role in surface finishing. The combined effect of spindle speed with a lower depth of cut, in turn, reduces the extent of vibration occurrence and the development of the defect and cracks. The outcomes have been validated by a confirmatory test that proves the proposed hybrid module practicality for manufacturing product/process improvement. Further, SEM analysis of machined samples was performed to check the quality and surface features required.

KEYWORDS:

• Milling
• Optimisation
• Taguchi
• TOPSIS, PCA

Disclosure statement

No potential conflict of interest was reported by the author(s).

Nomenclature

GO- Graphene Oxide

ANOM- Analysis of Means

ANOVA- Analysis of Variance

GRA- Grey Relation Analysis

GRC- Grey Relation Coeficient

OA- Orthogonal Array

S/N- Signal to Noise

R_a- Surface roughness

F_c- Cutting force

PCA- Principal Component Analysis

TOPSIS- Technique for Order of Preference by Similarity to Ideal Solution

S- Spindle speed

F- Feed rate

D- Depth of cut

G (wt.%)- Graphene Oxide weight %

SEM- Scanning Electron Microscopes

CFRP- Carbon Fibre Reinforced polymer

GFRP- Glass Fibre Reinforced polymer

SWCNT- Single Wall Carbon Nanotube

MWCNT- Multi-Wall Carbon Nanotube

CNO- Carbon Nano Onion

CNR- Carbon Nano Rod

AHP- Analytic Hierarchy Process

GO/CF- Graphene Oxide/Carbon Fibre

S/N- Singal to Noise ratio

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Jogendra Kumar

Mr. Jogendra Kumar is currently working as a Research cum teaching fellow in the Department of Mechanical Engineering at Madan Mohan Malaviya University of Technology, India. His research interests focus on nanomaterial synthesis and development, composites machining, numerical modeling, and surface texturing.

Rajesh Kumar Verma

Dr. Rajesh Kumar Verma is an Associate Professor and Doctoral Supervisor at the Department of Mechanical Engineering at the Madan Mohan Malaviya University of Technology, Gorakhpur, India. He received his Ph.D. (Engineering) from Jadavpur University, Kolkata, India. He is actively involved in teaching and research in nanomaterial, polymer composites, modeling, simulation, optimization, advanced machining, Machinability estimation of composites/nanocomposites materials. Dr. Verma currently supervised/ongoing more than 13 Masters and 08 Ph.D. thesis and published more than 78 research articles in peer-reviewed journals and conferences.