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.
Highlights
Titanium alloy (Ti-6Al-4V) is characterized by excellent mechanical properties, such as high tensile strength, high stiffness, good formability and excellent corrosion resistance in addition to its outstanding strength-to-weight ratio
The Artificial Neural Network (ANN) demonstrated a better predictive ability than the Response Surface Methodology (RSM) as observed by the magnitude of its predictions being closer to the actual values for both the temperature and the surface roughness as opposed to the RSM, though, the errors generated by both approaches were negligible and were found to be within the permissible limit (Table 9)
The prediction of the temperature and surface roughness during the milling operation of Ti6Al4V was successfully carried out using the RSM and ANN
Summary
Titanium alloy (Ti-6Al-4V) is characterized by excellent mechanical properties, such as high tensile strength, high stiffness, good formability and excellent corrosion resistance in addition to its outstanding strength-to-weight ratio. It finds an extensive range of applications in different industries, such as biomedical, aerospace, automotive, marine, railway etc. The degree of the surface finish often influences the product’s quality, integrity and performance, the optimization of the process parameter is key during the machining operation of the titanium alloy. Temperature is an important parameter, which determines changes in the mechanical behaviour, microstructure, surface finish and performance of the work piece as well as the cutting tool during machining operations. Cutting operations under controlled temperature can enhance the cutting oper-
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