Browsing by Author "Ezugwu, E. O."

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Effects of cutting parameters on surface roughness during high-speed turning of Ti-6AI-4V Alloy
(INSInet Publication, 2009) Fadare, D. A.; Sales, W. F.; Ezugwu, E. O.; Bonney, J.; Oni, A. O.
Surface roughness constitutes one of the most critical constraints for the selection of machine tools and cutting parameters in metal cutting operations. In this study, the steepest descent method was used to study the effects of cutting parameters (cutting speed, feed rate and depth of cut) on surface roughness of machined Ti-6AI-4V alloy workpiece at high-speed conditions. Machining trials were conducted at different cutting conditions using uncoated carbide inserts with ISO designation CNMG 120412 under conventional coolant supply, while a stylus type instrument was used to measure the centerline average surface roughness (Ra). The results revealed that, surface roughness was more sensitive to variation in feed rate followed by cutting speed and depth of cut. The study is of importance to machinist in the selection of appropriate combinations of machining parameters for high-speed turning of Ti-6AI-4V alloy workpiece.
Intelligent tool condition monitoring in high-speed turning of Titanium Ti-6Al-4V alloy
(Kwame Nkrumah University of Science and Technology (KNUST), 2009) Fadare, D. A.; Ezugwu, E. O.; Bonney, J.
Intelligent Tool Condition Monitoring (TCM) is an essential requirement in the drive towards automated machining operations. In this paper, a Multi-Layered Perceptron (MLP) neural network model has been developed for on-line condition monitoring of tool wear in high-speed turning of Titanium-based alloy (Ti-6Al-4V). Machining trials were conducted for typical rough and finish turning operations with cutting speed (90 – 120 m/min), feed rate (0.15 – 0.2 mm/rev), and depth of cut (0.5 -2.0 mm) using uncoated cemented carbide (K10 grade) inserts with Inter-national Standard Organization (ISO) designation “CNMG 120412”. The tool maximum flank wear (VBmax), cutting forces (feed force, Fx, and tangential force, Fz), and spindle motor power were measured during each machining operation. The cutting parameters (cutting speed, feed rate, and depth of cut), and cutting force and spindle power were used in isolation or in combination as input dataset in training the neural network to predict wear land on cutting tool at different stages of wear propagation (light, medium and heavy). The neural network model was designed using Matlab® neural toolbox. Accuracy of model for the prediction of tool wear at different wear stages were evaluated based on the Percentage Error (PE) for both roughing and finishing operations. Results showed that, the heavy wear stage (PE = ±5%) was predicted more accurately compared to the light (PE = +5 to -10%) and medium (PE = +25 to -30%) wear stages. The combination of the force, power signals and cutting parameters improved performance of the model.
Machining of nickel-base, Inconel 718, alloy with ceramic tools under finishing conditions with various coolant supply pressures
(Elsevier Limited, 2005) Ezugwu, E. O.; Bonney, J..; Fadare, D. A.; Sales, W. F
Machining of Inconel 718 with whisker reinforced ceramic tool gave better performance in terms of tool life under high-pressure coolant supplies up to 15 MPa compared to conventional coolant supplies. The use of 15 MPa coolant supply pressure tend to suppress notching during machining thus improving tool life, while the use of higher coolant supply pressure of 20.3 MPa did not show improvement in tool life due probably to accelerated notch wear caused by water jet impingement erosion, Cutting forces decreased with increasing coolant supply pressure due to improved cooling and lubrication at the cutting interface as well as effective chip segmentation ensured by the momentum of the coolant jet. Surface roughness generated were well below the rejection criteria. This can be attributed to the round shape of the insert which tend to encourage smearing of the machined surface with minimum damage. Microstructure analysis of the machined surfaces show evidence of plastic deformation and hardening of the top layer up to 0.15 mm beneath the machined surface as a result of increase in dislocation density.
Modeling of tool wear parameters in high-pressure coolant assisted turning of Titanium Alloy Ti-6Al-4V using artificial neural networks
(Akamai University, 2009) Fadare, D. A.; Ezugwu, E. O.; Bonney, J .
Titanium alloy (Ti-6Al-4V) can be economically machined with high-pressure coolant (HPC) supply. In this study, an artificial neural network (ANN) model was developed for the analysis and prediction of tool wear parameters when machining Ti-6Al-4V alloy with conventional flow and high-pressure coolant flow, up to 203 bar. Machining trials were conducted at different cutting conditions for both rough and finish turning operations with uncoated carbide (K10 grade) and double TiAIN/TiN, PVD coated carbide (K10 substrate) inserts. The cutting parameters (cutting speed, feed rate, depth of cut, coolant pressure, and tool type) and the process parameters (cutting forces, feed force, machined surface roughness, and circularity) were used as input data set to train the three-layered feed-forward, back-propagation artificial neural networks. The networks were trained to predict tool life and wear rate separately. The results show that the correlation coefficients between the neural network predictions and experimental values of tool life, tool wear and wear rate were 0.996 and 0.999, respectively, suggesting the reliability of the neural network model for analysis and optimization of cutting process.
Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network
(Elsevier Limited, 2005-03) Ezugwu, E. O.; Fadare, D. A.; Bonney, J.; Da Silva, R. B.; Sales, W. F.
An artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between cutting and process parameters during high-speed turning of nickel-based, Inconel 718, alloy. The input parameters of the ANN model are the cutting parameters: speed, feed rate, depth of cut, cutting time, and coolant pressure. The output parameters of the model are seven process parameters measured during the machining trials, namely tangential force (cutting force, Fz), axial force (feed force, Fx), spindle motor power consumption, machined surface roughness, average flank wear (VB), maximum flank wear (VBmax) and nose wear (VC). The model consists of a three-layered feedforward backpropagation neural network. The network is trained with pairs of inputs/outputs datasets generated when machining Inconel 718 alloy with triple (TiCN/Al2O3/TiN) PVD-coated carbide (K 10) inserts with ISO designation CNMG 120412. A very good performance of the neural network, in terms of agreement with experimental data, was achieved. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the process parameters in metal-cutting operations and for the optimisation of the cutting process for efficient and economic production.