Yanis, Muhammad and Mohruni, Amrifan Saladin and Sharif, Safian and Yani, Irsyadi (2019) Performance Evaluation of Green Machining on Thin-Walled Ti6Al4V using Response Surface Methodology and Artificial Neural Networks. Documentation. Doctoral Study Program in Engineering Science, Sriwijaya University, Palembang, South Sumatera, Indonesia.
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Abstract
Milling of thin-walled components is very substantial in the modern aerospace industry. Aerospace component requires best engineering metals such Ti6Al4V alloy. This alloy even though have many superior advantages, but classified as difficult-to machine material. Poor machinability of Ti6Al4V thin-walled is a challenge. This study is necessary, also according to its complicated structure of thin-walled and complicated nature of Ti6Al4V under the influence of fluctuating cutting force. The fluctuation resulted vibration, therefore making it hard to support the expected surface finish. In the interest of expected surface quality, this milling is also wiser obtained by green machining with the use of vegetable oil as non-toxic and ecofriendly cutting fluid. Neat coconut oil was chosen as a good local source. Thereafter, in an effort to minimize the price of coconut oil and the high cost of milling Ti6Al4V then machining operation achieved by the utilization of minimum quantity lubrication (MQL) technique. The aim of this study was to evaluate the machining performance of Ti6Al4V on dependent variables (surface roughness Ra and cutting speed Fc). RSM and ANN were developed to determine modeling predictions and optimization. Variations in the value of independent variables (cutting speed Vc, feed rate fz, radial ar and axial ax depth of cut) based on the CCD method (Central Composite Design) consist of 30 test data. Machining uses coated and uncoated carbide tools. The best mathematical equation results based on RSM for surface roughness prediction using coated tools was quadratic model, and using uncoated tools was linear model. The best mathematical equation results for cutting force prediction using coated tools was quartic model, and using uncoated tools was quadratic model. Optimal conditions for the minimum dependent variable according to RSM were for coated tool Vc = 113.9 m/min, fz = 0.04 mm/tooth, ar = 0.27 mm, ax = 5 mm that obtained Ra = 0137 μm and Fc = 25.29 N. Optimal conditions for uncoated tool Vc = 125 m/min, fz = 0.04 mm/tooth, ar = 0.25 mm, ax = 5 mm that obtained Ra = 0.161 μm and Fc = 14.89 N. The best accuracy prediction on ANN with Back Propagation obtained was Levenberg-Marquardt (LM) algorithm. Network structure to achieve the lowest MSE value for surface roughness with coated and uncoated tools were 4-10-1 and 4-13-1, respectively. Network structure to achieve the lowest MSE value for cutting force with coated and uncoated tools were 4-8-1 and 4-10-1, respectively. Based on the MSE value, the accuracy prediction of surface roughness using ANN was better than RSM with coated and uncoated at 62.27% and 93.05%, respectively. The accuracy prediction of cutting force using ANN was better than RSM with coated and uncoated at 99.17% and 96.61%, respectively. The MSE of RSM and ANN both surface roughness and cutting force shows that the prediction were close to the results of the experiment. Surface roughness was most affected by feed rate. Low feed rates and depth of cut resulted in low surface roughness, but high cutting speeds reduced surface roughness. The cutting force was most affected by the depth of cut. Reduction in depth of cut and feed rate resulted in low cutting forces, but the effect of cutting speed is very small. All dependent variables were lower on non-thin walled machines compared to thin walled machines. The values of cutting force on coated tool are higher than uncoated tool, whereas the surface roughness value of coated tool was lower than uncoated tool and this tendency occurs both in thin-walled and non-thin-walled. All machining conditions used in this study did not cause chatter.
| Item Type: | Monograph (Documentation) |
|---|---|
| Uncontrolled Keywords: | Ti6Al4V, Thin-walled, MQL, Coconut Oil, RSM and ANN |
| Subjects: | T Technology > TN Mining engineering. Metallurgy > TN600-799 Metallurgy > TN693.T5.M36 Titanium alloys--Research. Composite materials--Research. Machining. Hard materials--Machining. |
| Divisions: | 03-Faculty of Engineering > 21001-Engineering Science (S3) |
| Depositing User: | Dr. Muhammad Yanis, ST, MT |
| Date Deposited: | 20 Nov 2019 01:34 |
| Last Modified: | 20 Nov 2019 02:55 |
| URI: | http://repository.unsri.ac.id/id/eprint/17206 |
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