scholarly journals Analisa Kekasaran Permukaan Titanium Grade 2 pada Proses Frais

2020 ◽  
Vol 8 (2) ◽  
pp. 53
Author(s):  
AM. Mufarrih ◽  
Moh. Nasir Hariyanto ◽  
Nanang Qosim
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Materials Science ◽  
Cutting Parameters ◽  
Advanced Manufacturing Technology ◽  
P Value ◽  
Advanced Manufacturing ◽  
Multi Objective ◽  
Hard Materials ◽  
Titanium Grade

 Titanium Grade 2 termasuk jenis bahan yang sering dipergunakan di industri, utamanya pada bahan untuk implan biomedis. Titanium Grade 2 mempunyai sifat perbandingan kekakuan terhadap berat yang baik, tahan terhadap korosi dan memiliki sifat biokompatibel yang baik di dalam tubuh. Namun memiliki konduktifitas panas yang rendah, sehingga perlu memilih perameter pemesinan yang tepat untuk menghasilkan nilai kekasaran permukaan yang baik. Penelitian ini bertujuan untuk mengetahui karakteristik Titanium Grade 2 yaitu kekasaran permukaan hasil pemesinan frais. Desain penelitian menggunakan metode Taguchi L9, dengan 2 faktor dan 3 level. Parameter pemesinan yang digunakan ialah putaran spindel 500; 700; 900 rpm dan kecepatan pemakanan 25; 50; 75 mm/menit. Variabel respon yang diteliti ialah kekasaran permukaan. Proses frais dilakukan menggunakan Mesin CNC Dahlih. Kekasaran permukaan diukur menggunakan Mitutoyo surface roughess tester. Analisis data menggunakan analisis ANOVA. Hasil penelitian menunjukan bahwa ada pengaruh variasi parameter pemesinan terhadap respon kekasaran permukaan. Variabel putaran spindel mempunyai p-value sebesar 0,039 dan variabel gerak makan memiliki p-value sebesar 0,025. Hal ini menunjukkan bahwa kedua variabel bebas tersebut memiliki pengaruh yang signifikan terhadap respon kekasaran permukaan. Kekasaran permukaan terendah dapat dicapai dengan pengaturan putaran spindel sebesar 700 rpm dan kecepatan pemakanan sebesar 25 mm/menit. Kata kunci: titanium grade 2, kekasaran permukaan, frais, anova Daftar RujukanBagno, A., & Di Bello, C. (2004). Surface treatments and roughness properties of Ti-based biomaterials. Journal of Materials Science: Materials in Medicine. https://doi.org/10.1023/B:JMSM.0000042679.28493.7fBruce, 2011. (2013). Analisis Kekasaran Permukaan Dan Getaran Pada Pemesinan Bubut Menggunakan Pahat Putar Modular (Modular Rotary Tools) Untuk Material Titanium 6Al-4V Eli. Journal of Chemical Information and Modeling. https://doi.org/10.1017/CBO9781107415324.004Davim, J. P. (2011). Machining of hard materials. Machining of Hard Materials. https://doi.org/10.1007/978-1-84996-450-0Ganguli, S., & Kapoor, S. G. (2016). Improving the performance of milling of titanium alloys using the atomization-based cutting fluid application system. Journal of Manufacturing Processes. https://doi.org/10.1016/j.jmapro.2016.05.011Karkalos, N. E., Galanis, N. I., & Markopoulos, A. P. (2016). Surface roughness prediction for the milling of Ti-6Al-4V ELI alloy with the use of statistical and soft computing techniques. Measurement: Journal of the International Measurement Confederation. https://doi.org/10.1016/j.measurement.2016.04.039Kiswanto, G., Mandala, A., Azmi, M., & Ko, T. J. (2020). The effects of cutting parameters to the surface roughness in high speed cutting of micro-milling titanium alloy ti-6al-4v. Key Engineering Materials, 846 KEM, 133–138. https://doi.org/10.4028/www.scientific.net/KEM.846.133Mufarrih, A., Istiqlaliyah, H., & Ilha, M. M. (2019). Optimization of Roundness, MRR and Surface Roughness on Turning Process using Taguchi-GRA. In Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1179/1/012099Nithyanandam, J., Das, S. L., & Palanikumar, K. (2015). Inluence of Cutting Parameters in Machining of Titanium Alloy. Indian Journal of Science and Technology, 8(8), 556–562. https://doi.org/10.17485/ijst/2015/v8i/71291Oshida, Y. (2012). Bioscience and Bioengineering of Titanium Materials: Second Edition. Bioscience and Bioengineering of Titanium Materials: Second Edition. https://doi.org/10.1016/C2011-0-07805-5Setyowidodo, I., Sutanto, S., Mufarrih, A., & Sholehah, I. M. (2020). Exhaust temperature and peltier element optimization of thermoelectric generator output. In IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/850/1/012007Shucai, Y., Chunsheng, H., & Minli, Z. (2019). A prediction model for titanium alloy surface roughness when milling with micro-textured ball-end cutters at different workpiece inclination angles. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-2852-6Soepangkat, B. O. P., Pramujati, B., Effendi, M. K., Norcahyo, R., & Mufarrih, A. M. (2019). Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches. International Journal of Precision Engineering and Manufacturing. https://doi.org/10.1007/s12541-019-00017-zTapiero, H., Townsend, D. M., & Tew, K. D. (2003). Trace elements in human physiology and pathology. Copper. Biomedicine and Pharmacotherapy. https://doi.org/10.1016/S0753-3322(03)00012-XThepsonthi, T., & Özel, T. (2012). Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-012-3980-zWennerberg, A., & Albrektsson, T. (2009). Effects of titanium surface topography on bone integration: A systematic review. Clinical Oral Implants Research. https://doi.org/10.1111/j.1600-0501.2009.01775.x 

Theoretical Surface Roughness Model in High Speed Face Milling

2014 ◽  
Vol 989-994 ◽  
pp. 3331-3334
Author(s):  
Tao Zhang ◽  
Guo He Li ◽  
L. Han
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
High Speed ◽  
Cutting Parameters ◽  
Angular Speed ◽  
Face Milling ◽  
Advanced Manufacturing ◽  
Roughness Model ◽  
Machined Parts ◽  
Important Object

High speed milling is a newly developed advanced manufacturing technology. Surface integrity is an important object of machined parts. Surface roughness is mostly used to evaluate to the surface integrity. A theoretical surface roughness model for high face milling was established. The influence of cutting parameters on the surface roughness is analyzed. The surface roughness decreases when the cutter radius increases, total number of tooth and rotation angular speed, while it increases with the feeding velocity. The high speed face milling can get a smooth surface and it can replace the grinding with higher efficiency.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

Investigations on the Machinability of Titanium Alloy TC25

2014 ◽  
Vol 800-801 ◽  
pp. 92-96
Author(s):  
Hong Shan Zhang ◽  
Xing Ai ◽  
Zhan Qiang Liu ◽  
Ji Gang Liu ◽  
Zhao Lin Zhong
Keyword(s):  
Thermal Stability ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Service Life ◽  
Heat Resistance ◽  
Cutting Forces ◽  
Cutting Parameters ◽  
Aircraft Industry ◽  
Excellent Thermal Stability ◽  
Cemented Carbide Tools

Titanium alloy TC25 has been widely used in aircraft industry due to its excellent thermal stability, heat resistance and longer service life. In this paper, cemented carbide tools were applied to carry out orthogonal milling experiments for both titanium alloy TC25 and TC4 with identical cutting conditions. Cutting forces, cutting temperatures and surface roughness were measured to assess the machinability for TC25 and TC4. From the experimental results, the cutting parameters can be optimized to guide efficient machining processing of TC25.

scholarly journals Surface Quality of Ti-6Al-4V Titanium Alloy Parts Machined by Laser Cutting

2020 ◽  
Vol 10 (4) ◽  
pp. 6062-6067
Author(s):  
A. Boudjemline ◽  
M. Boujelbene ◽  
E. Bayraktar
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Laser Power ◽  
Laser Cutting ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Maximum Height ◽  
Proposed Model

This paper investigates high power CO2 laser cutting of 5mm-thick Ti-6Al-4V titanium alloy sheets, aiming to evaluate the effects of various laser cutting parameters on surface roughness. Using multiple linear regression, a mathematical model based on experimental data was proposed to predict the maximum height of the surface Sz as a function of two laser cutting parameters, namely cutting speed and assist-gas pressure. The adequacy of the proposed model was validated by Analysis Of Variance (ANOVA). Experimental data were compared with the model’s data to verify the capacity of the proposed model. The results indicated that for fixed laser power, cutting speed is the predominant cutting parameter that affects the maximum height of surface roughness.

The Effects of Cutting Parameters to the Surface Roughness in High Speed Cutting of Micro-Milling Titanium Alloy Ti-6Al-4V

2020 ◽  
Vol 846 ◽  
pp. 133-138
Author(s):  
Gandjar Kiswanto ◽  
Adrian Mandala ◽  
Maulana Azmi ◽  
Tae Jo Ko
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Weight Reduction ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Depth Of Cut ◽  
High Speed Cutting ◽  
Micro Scale

Micro-milling offers high flexibility by producing complex 3D micro-scale products. Weight reduction are one of the optimizations of the product that can make it stronger and more efficient nowadays. Titanium are the most commonly used for micro-scale products especially in biomedical industries because of the biocompatibility properties. Titanium alloys offers high strength with low density and high corrosion resistance that is suitable for weight reduction. This study aims to investigate the influence of high speed cutting parameters to the surface roughness in micromilling of titanium alloy Ti-6Al-4V as high speed cutting offers more productivity since producing more cutting length in the same time. experiments are carried out by micromilling process with variations in high speed cutting parameters of spindle speed and feed rate with a constant depth of cut using a carbide cutting tool of with a diameter of 1 mm. The machining results in the form of a 4 mm slot with a depth as the same as depth of cut, which then measures its surface roughness. It was found that higher feed rate that is followed by higher spindle speed will produce better surface roughness.

scholarly journals Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

2020 ◽  
Vol 4 (3) ◽  
pp. 64 ◽  
Author(s):  
Mahamudul Hasan Tanvir ◽  
Afzal Hussain ◽  
M. M. Towfiqur Rahman ◽  
Sakib Ishraq ◽  
Khandoker Zishan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Optimization Algorithm ◽  
Cutting Parameters ◽  
Whale Optimization Algorithm ◽  
Multi Objective Optimization ◽  
Turning Operation ◽  
Multi Objective ◽  
Whale Optimization ◽  
Optimum Cutting

In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.

scholarly journals Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

2019 ◽  
Vol 9 (18) ◽  
pp. 3684 ◽  
Author(s):  
Tao Zhou ◽  
Lin He ◽  
Jinxing Wu ◽  
Feilong Du ◽  
Zhongfei Zou
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Quality ◽  
Regression Tree ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Gradient Boosting ◽  
Multi Objective Optimization ◽  
Multi Objective

Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

scholarly journals Machining Performance of TiAlN-Coated Cemented Carbide Tools with Chip Groove in Machining Titanium Alloy Ti-6Al-0.6Cr-0.4Fe-0.4Si-0.01B

Metals ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 850 ◽  
Author(s):  
Zhaojun Ren ◽  
Shengguan Qu ◽  
Yalong Zhang ◽  
Xiaoqiang Li ◽  
Chao Yang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cemented Carbide ◽  
Machining Performance ◽  
Cemented Carbide Tools ◽  
Optimal Cutting Parameters ◽  
Coated Cemented Carbide

In this paper, TiAlN-coated cemented carbide tools with chip groove were used to machine titanium alloy Ti-6Al-0.6Cr-0.4Fe-0.4Si-0.01B under dry conditions in order to investigate the machining performance of this cutting tool. Wear mechanisms of TiAlN-coated cemented carbide tools with chip groove were studied and compared to the uncoated cemented carbide tools (K20) with a scanning electron microscope (SEM) and energy dispersive spectrometer (EDS). The effects of the cutting parameters (cutting speed, feed rate and depth of cut) on tool life and workpiece surface roughness of TiAlN-coated cemented carbide tools with chip groove were studied with a 3D super-depth-of-field instrument and a surface profile instrument, respectively. The results showed that the TiAlN-coated cemented carbide tools with chip groove were more suitable for machining TC7. The adhesive wear, diffusion wear, crater wear, and stripping occurred during machining, and the large built-up edge formed on the rake face. The optimal cutting parameters of TiAlN-coated cemented carbide tools were acquired. The surface roughness Ra decreased with the increase of the cutting speed, while it increased with the increase of the feed rate.

Parameters Optimization about Precision Milling Titanium Alloy TC4 Used for Aviation

2012 ◽  
Vol 472-475 ◽  
pp. 1818-1822
Author(s):  
Wei Hua Wu ◽  
Yan Yan Guo ◽  
Can Zhao ◽  
Tao Xu ◽  
Jia Yang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Production Efficiency ◽  
Design Method ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Parameters Optimization ◽  
Processing Efficiency

To improve the production efficiency and product quality of titanium alloy TC4, with the minimum of cutting force F, surface roughness Ra, and surface peak valley height Pv as the optimized goal, using orthogonal rotating combination design method of three factors quadratic regression, the influence of cutting speed vc, feed per tooth fz and cutting width ae to cutting force (Fx, Fy) surface peak valley height Pv and surface roughness Ra are mainly studied, and the best cutting amount combination is chosen. Experiment results indicate that the best cutting parameters of titanium alloy are vc=28.588 m/min、fz=0.043 3 mm/z、ae=0.1 mm, the optimal values are =3.346 N、 =47.01 N、 =673.89 nm and =201.78 nm. This research is of theoretical significance for improving the processing efficiency and machining quality and reducing the production cost.

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