Prediction of Responses in a Sustainable Dry Turning Operation: A Comparative Analysis

Table 1

Turning parameters, responses, and prediction models considered by the past researchers.

Sl. no.	Author(s)	Input parameters	Response(s)	Prediction model
1.	Koura et al. [7]	, f, d	Ra	ANN
2.	Hanief and Wani [8]	, f, d	Ra	Regression
3.	Mia et al. [9]	, f, tool configuration, environment	Ra	ANOVA
4.	Benlahmidi et al. [10]	, f, d, workpiece hardness	Ra, cutting pressure, cutting power	Regression
5.	Sharma and Krishnaiah [11]	, f, d	Ra, MRR, power consumption	ANN, regression
6.	Panda et al. [12]	, f, d	Flank wear, Ra, acceleration	Regression
7.	Pawan and Misra [13]	, f, approach angle	Ra	Regression
8.	Aouici et al. [14]	, f, cutting time	Ra, specific cutting force, flank wear	Regression
9.	Elbah et al. [15]	, f, d, cutting radius	Ra, cutting force components, tool wear	Regression
10.	Rajbongshi and Sarma [16]	, f, d	Ra, flank wear, Fc, feed force	ANN, regression
11.	Alajmi and Almeshal [17]	, f, d	Ra	ANFIS
12.	Cica et al. [18]	, f, d, environment	Machining force, cutting power, cutting pressure	Regression, support vector regression, Gaussian process regression, ANN
13.	Panda et al. [19]	, f, d	Acceleration, flank wear, Ra	Regression
14.	Setia and Chauhan [20]	, f, d	Cutting force components, cutting temperature	Regression
15.	This paper	, f, d	Ra, Fc, MRR	Regression, ANN, fuzzy logic, ANFIS