ANALISIS PENGARUH KONDISI PEMOTONGAN TERHADAP KEKASARAN PERMUKAAN Ti6A14V PADA PROSES FRAIS DENGAN METODE ARTIFICIAL NEURAL NETWORK

RIDLO, M. RIV’AT and Mohruni, Amrifan Saladin (2016) ANALISIS PENGARUH KONDISI PEMOTONGAN TERHADAP KEKASARAN PERMUKAAN Ti6A14V PADA PROSES FRAIS DENGAN METODE ARTIFICIAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya university.

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Abstract

many cutting tooth rotating pivot either clockwise or anti clockwise. Cutting or slicing the workpiece can occur if the movement of the chisel cuts iniposed on the workpiece that has gripped there will be friction / collision that will residt in cuts in the workpiece, this can happen because the constituent material hardness over the chisel has a hardness of the workpiece. In machining surface roughness (juality is very important because surface roughness is definingfactor to know good or not product resu/ling form machining process. In this study used a endmill tool wilh Carbide material and titanium alloy H6A14V as workpieces using CNC mi/ling. Cutting process parameters such as cutting speed (Vc), feed rate (fz), depth of cut radial (ar), and depth of cut axial (aa), to know the optimization of surface roughness values with artificial mitrai network method. Artificial mural networks (ANN) is a computational method that mimics a biological mural network system in the human brain. Based on the results testing of surface roughness affects ihe variation of cutting conditions. Ti6Al4V surface roughness maximum value obtained w as 1.76 pm in the can on testing to 20. When the value of Vc = 100 m / min, fz = 0.158 m m/tooth, ar 0.32 mm, and 7.07 mm aa - 10 mm, Meanwhile, the minimum value obtained 776Al4Vsurface roughness was 0.22 pm is on testing 10. With test cutting conditions, Vc ~ 125 m / min, feed rate (fz) = 0.04 mm / tooth, Depth of cut radial (ar) = 0,25 mm, Depth of cut axiaI (aa> = 10 mm. lt can be concluded that the bigest value of the cutting speedgeneraled the bigest value ofRa. Similarly, the feed rate, the bigest offeed rate valued generated the bigest of surface roughness Ra value so the rough surface is generated. Then the test result data were analyzed using ANN method backpropagation with using neura network architecture 4-1-1, 4-2-1, 4-3-1, 4-4-1, 4-5-1, 4-6-1, 4-7- 1, and 4-S-l model. To knowing w ha t neura/ network architecture have the best prediction by looking a t the architectur e which has the smallest error rate and the correlation surface roughness values between the surface roughness predictive values based analyzed ANN and the surface roughness values from test. Analysis was done by using MATLAB Software, be obtained neural network ANN 4-7-1 is the besi neura! network withMSE (mean square error) value 0,02. Prediction value Ra ana/ysis ANN optimum is 0,29 pm, on the second testing with cutting speed condition (Vc 125 m/min) and feed rale (fz 0,04 mm/tooth). Keyword : Surface Roughness, Thin walled machining, Ti6Al4V, Artificial Neura/ Network, MATLAB

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kekasaran Permukaan, Thin walledMachining, Ti6A14V, Artificial Neural Nelwork, MATLAB
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ227-240 Machine design and drawing
Divisions:	03-Faculty of Engineering > 21201-Mechanical Engineering (S1)
Depositing User:	Ichlasyah Aisyah
Date Deposited:	08 Sep 2025 01:20
Last Modified:	08 Sep 2025 01:20
URI:	http://repository.unsri.ac.id/id/eprint/183685

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