Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718

Abstract

Nickel based super alloys are excellent for several applications and mainly in structural components submitted to high temperatures owing to their high strength to weight ratio, good corrosion resistance and metallurgical stability such as in cases of jet engine and gas turbine components. The current work presents the experimental investigations of the cutting parameters effects (cutting speed, depth of cut and feed rate) on the surface roughness, cutting force components, productivity and power consumption during dry conditions in straight turning using coated carbide tool. The mathematical models for output parameters have been developed using Box-Behnken design with 15 runs and Box-Cox transformation was used for improving normality. The results of the analysis have shown that the surface finish was statistically sensitive to the feed rate and cutting speed with the contribution of 43.58% and 23.85% respectively, while depth of cut had the greatest effect on the evolution of cutting force components with the contribution of 79.87% for feed force, 66.92% for radial force and 66.26% for tangential force. Multi-objective optimization procedure allowed minimizing roughness Ra, cutting forces and power consumption and maximizing material removal rate using desirability approach.

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

“…RSM can represent the direct and interactive effects of process parameters through the analysis of variance (ANOVA). Moreover, this approach applied in the present work is considered as a procedure to identify a relationship between independent input process parameters and output data (process response), which includes commonly six steps as it is indicated by Gaitonde et al [21] and Tebassi et al [20]: (1) define the independent input variables and the desired output responses, (2) adopt an experimental design plan, (3) perform regression analysis with the required model of RSM as found by Hessainia et al [22] and Zahia et al [23] as shown in Eq. (1).…”

“…Response surface methodology is an empirical and widely accepted statistical modeling technique employed for multiple regression analysis using quantitative data obtained from properly designed experiments to solve multivariate equations simultaneously as found by Maran et al [19] and Tebassi et al [20]. RSM approach proceeds with carrying out statistically designed experiments, followed by evaluating the coefficients in a mathematical relationship, the prediction of response and examining the sufficiency of the model.…”

Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach

“…Considering mechanical properties of workpiece, specific shearing energy which is a strong function of feed rate; was the largest at the lowest cutting speed (125 m/min) and reduces subsequently when the cutting speeds increases up to 300 m/min (Pawade et al, 2009). Regarding surface quality and productivity, Yadav et al (2015) and Tebassi et al (2016b) obtained that the most influencing factor on MRR is depth of cut, whereas spindle speed and depth of cut are the most influencing factors on flank wear. In the same way, at low feed rate; the tendency for built-up edge formation, is also higher than at a higher feed, due to an increase in the size of the plastic deformation area at the interface of the tool and workpiece (Zhou et al, 2012).…”

“…The predicted responses are Ra = 0.30 µm and 8142.14 mm 3 /min for MRR with desirability value of 1.00 as shown in Fig. 13; which presents solution ramps of multi-objective optimization (Tebassi et al, 2016b). …”

“…The RSM applied in this work is considered as a procedure to identify a relationship between independent input process parameters and output data (process response).This procedure includes commonly six steps (Tebassi et al, 2016b;Gaitonde et al, 2009;Davim et al, 2008):…”