Optimization and prediction of CBN tool life sustainability during AA1100 CNC turning by response surface methodology

Parametric optimization to establish eco-friendly nanofluid minimum quantity lubrication (NMQL) practice for turning superalloy Inconel 718

“…RSM uses empirical modeling of polynomials and linear equations to build a relationship among selected input variables and measured response factors in the experiments (Lakshmikanthan et al , 2023). RSM is a preferred technique in industrial cases where many input parameters controlled by the engineer can potentially influence the response parameters of the machining process (Faisal et al , 2023). RSM uses a sequential approach in which a first-order model [represented by equation (1)] is followed by a second-order model [equation (2)] to explore the input factor space: where Xi and Xj are the independent variables, β0 is constant and βi , βii and βij are coefficients of linear, quadratic and cross-product terms, respectively.…”

“…The desirability technique under RSM has been demonstrated to be a successful method for low-error parametric optimization of the input machine data (Thirumalaikkannan et al , 2023). Faisal et al (2023) optimized turning input parameters for aluminum alloy (AA1100) using the RSM approach and recommended the best turning parameter combinations as: depth of cut (DOC) = 0.1 mm, feed rate (f) = 0.2 to 0.25 mm/rev and spindle speed (SS) = 1,300 to 1,500 rpm, that resulting in a higher tool life > 20 min. Similarly, Lakshmikanthan et al (2023) also used RSM and observed cutting speed as the most crucial factor for surface roughness during the turning of aluminum alloy (LM13) metal matrix composite.…”