Modelling and investigating the impact of EDM parameters on surface roughness in EDM of Al/Cu/Ni Alloy

ABSTRACT

The paper presents the prediction of surface quality during the electro-discharge machining (EDM) of aluminium-based alloy. The composition consists of Aluminium, copper, nickel (Al/Cu/Ni) alloy. The techniques like dimensional exponential model (DEM), response surface-based modelling (RSM), and adaptive neuro-fuzzy inference based system (ANFIS) were employed to analyse the EDM process. Response variable i.e. Surface characteristic measure in terms of roughness (Ra) is the dependent parameter. Experimental analysis was done using Taguchi’s L₁₈ mixed OA’s i.e. orthogonal-array by considering the process parameters namely variation in the material composition (CP), pulse-on-time(TON), pulse-off- time(TOFF) and input current(IP). Analysis of variances (ANOVA) has been employed out to identify the significant process parameters and their impact over surface characteristics. Models based on the DEM and RSM were developed to analyse the surface characteristics or roughness of the Al/Cu/Ni alloy. The experimental result proves the commitment of all three approaches to represent the process effectually. The correlation coefficient (R²) between the experimental and the DEM, RSM, and ANFIS predicted roughness was 0.830626, 0.995649, and 0.999958 respectively. This value shows the dominance of the ANFIS technique over the DEM and the RSM.

KEYWORDS:

• Al/Cu/Ni
• ANFIS
• Buckingham
• DEM
• fuzzy
• MMC
• Pi
• roughness
• RSM
• rule base

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Mangesh Phate

Mangesh Phate completed Bachelors degree in Mechanical Engineering from Amravati University, Amravati  in 2002 & Masters of Technology degree in Production Engineering with First university rank and Gold Medals in 2005 from R.T.M. Nagpur University, Nagpur. He has completed PhD in Mechanical Engineering from R.T.M. Nagpur University, Nagpur in 2015. Now he is working as Associate Professor in Mechanical Engineering Department at  All India Shri Shivaji Memorial Society's, College of Engineering, Pune, Maharashtra, India-411001, since 2016. His area of interest is advanced manufacturing processes, ergonomics, Multi response optimization and design engineering. He is permanent member of Indian society ISTE from 2003.He received Best teacher award (Rank 1) in 2019 from AISSM'S Pune.

Shraddha Toney

Shraddha Toney has obtained her Bachelor’s degree in Information Technology from Amravati university, Amravati in 2006 and Masters of Engineering in Computer Engineering from Savitribai Phule Pune University, Pune. She has about 13 years of teaching experience. Her area of interest includes soft computing techniques, cloud computing etc.She is currently working at Sinhgad Institute of Technology and Science , Pune, Maharashtra, India.

Vikas Phate

Vikas Phate has obtained his Bachelor’s in Electronics and Telecommunication Engineering from Amravati University, Amravati in 2004 and  Master’s in Electronics in 2009 from Dr. Babasaheb Ambedkar Marathwada University, Aurangabad and currently persuing PhD (QIP Scheme) from NIT, Tiruchirappalli, Tamilnadu, India. He is currently working at Government Polytechnic Murtizapur,Akola, Maharashtra, India. His research area is image processing, computer vision system, Statistic analysis etc.