Parametric analysis through ANFIS modelling and optimization of micro-hole machining in super duplex stainless steel by die-sinking EDM

ABSTRACT

Super duplex stainless steel (SDSS) contains ferrite and austenite in equal proportions. The high chromium imparts good corrosion resistance and high-temperature workability but the low thermal conductivity and high work hardening reduces its machinability by traditional methods. In this study, the feasibility of using electric discharge machining (EDM) to create geometrically accurate microholes with high productivity has been explored. Individual effect of input process parameters like pulse-on-Time (T_on), pulse-off time (T_off) and current (I) on material removal rate (MRR), overcut (OC) and taper angle (TA) has been investigated through adaptive neuro fuzzy inference system (ANFIS). Furthermore, hybrid multiobjective optimisation technique of grey relational analysis (GRA) combined with principal component analysis (PCA) was used to determine the optimal process input parameters. During hole sinking µ-EDM, increase of pulse on time increases the MRR, whereas the OC and taper decrease. GRA-PCA optimisation technique yields optimal input parameters which enhances MRR by 5.92% and rescues the OC and TA by 27.1% and 75%, respectively.

KEYWORDS:

• Super duplex stainless steel
• EDM
• hole-sinking
• microhole
• ANFIS
• multiobjective optimisation
• grey relational coefficient
• grey relational grade
• principal component analysis
• central composite design

Disclosure statement

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