DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

Abstract

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

“…(2019c) CNC Milling Empirical Tlhabadira et al. (2019) CNC Milling Empirical Daniyan et al. (2020a) CNC Milling Empirical …”

“…However, the process sustainability reduces when the temperature exceeds the optimum. One way to keep the temperature within the optimum range is through temperature monitoring in real time via the use of infrared video thermometer or other suitable temperature measuring and monitoring devices ( Daniyan et al., 2020a ). In addition, the use of cutting fluids at the shear zone will reduce the coefficient of friction among the chips, tools and work piece interface ( Okafor and Aramalla, 2006 .…”

“…The quest to develop smart products which will meet functional requirements using emerging materials within a short manufacturing cycle time has led to increase in energy requirement of the manufacturing processes with environmental impact and cost consequences ( Priarone, 2016 ). Typical examples of such emerging materials are the nickel-based super alloys, for instance, the Inconel 718, Grade EA1N steel, Gamma and Beta titanium based alloys amongst others ( Koohestani et al., 2014 ; Fratila, 2014 ; Daniyan et al., 2020a ).…”

Process Design, Development and Mechanical Analysis of Cu-Zn Alloy Produced by Sand Casting Process

“…The ANOVA evaluates the statistical significance of the output of the model. A good model which validates the numerical experimentation is signalled by a "p-value Prob > F" which should be less than 0.050, a F-value greater than unity, a "Lack of Fit" which is statistically insignificant relative to the pure error, the predicted R square, R squared and the adjusted R Squared which should be within the same range and close to 1 as well as the adequate precision which measures the signal to noise ratio which should be greater than 4 [16].…”

“…Previous studies have shown that preliminary process design with use of the Design of Experiment (DoE) technique via the use of Taguchi, Response Surface Methodology etc. can be employed for the determination of the feasible combinations of process parameters that will enhance excellent mechanical properties [13][14][15][16]. Previous studies have also shown that the microstructure and mechanical properties of brass can also be enhanced with a carefully balanced composition of Cu-Zn and other alloying elements, addition of additives as well as the use of appropriate heat treatment technique [17][18].…”