ANALISA PARAMETER KEKASARAN PERMUKAAN BAHAN ALUMUNIUM JENIS Al Mg Si 3.6082 DIN 1725 PADA PROSES PEMESINAN CNC MILLING
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Published:
Apr 1, 2012
Keywords:
CNC Milling, Al Mg Si 3.6082 DIN 1725, surface roughness, linier regression
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Abstract
Machining process will produce a good product / work in accordance with the instructions specified by the finishing process, while still a lot of operator / mechanic machine tools are still faced with determining machining parameters such as cutting speed, feed rate, and the dept of cut.Melalui results of this study expected to contribute a measure of optimal parameters in CNC operations carried out by means of experiments Milling.Penelitian Al Mg Si material 3.6082 DIN 1725 using a CNC Milling. Any specimen that has been done on a CNC Milling with three variations of cutting speed, feed rate, and the dept of cut measured surface roughness (Ra). From the results of further experimental data are analyzed with regression models to obtain mathematical models. Mathematical models that produced a regression equation Y = 0.880-0.001 n- 0.004 f + 0.316 a 69.8% level of eligibility.
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yunus, moch, Suryana, D., & Mulyadi, M. (2012). ANALISA PARAMETER KEKASARAN PERMUKAAN BAHAN ALUMUNIUM JENIS Al Mg Si 3.6082 DIN 1725 PADA PROSES PEMESINAN CNC MILLING. AUSTENIT, 4(01). https://doi.org/10.5281/zenodo.4544317
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References
1. Chang-Xue. (2002), Surface Roughness Predictive Modeling: Neural Networks versus Regression. Departmen of Industrial & Manufacturing Engineering, College of Engineering and Technology Bradley University: Illinois USA.
2. Huang L, Chen, J.C, 2001, A Multiple
Regression Model to Predict In-process Surface Roughness in Turning Operation Via Accelerometer, Journal of Industial Technology,Vol. 17, No. 2, February to April 2001, http://www.nait.org.
3. Hando o, B. Tulun Prayo o, (2012), “ Studi Parameter Pemesinan Optimum Pada Operasi CNC End Milling Finishing Bahan Alumunium:,Universitas Gajah Mada, http://www.scribd.com/doc/25352002/Stu di-Paramater-Pemesinan-Optimum-
Pada-Operasi-Cnc, 25 Maret 2012.
4. Her an Sa utro dan Sunaryo,” Predi i
Kekasaran Permukaan Baja ST 40
Berbasis Model Analisa Regresi Ganda ada Pe e inan CNC Frai ”, Univer ita Sebelas Maret Surakarta, http://www.docstoc.com/docs/113842922
/Prediksi-Kekasaran-Permukaan-Baja- ST-40-Berbasis-Model-Analisa-Regresi- Ganda-Pada-Pemesinan-CNC-Frais, 25
Februari 2012
5. Jerard, R.B., et al, 2001, Online
Optomozation of Cutting Conditions for NC Machining, 2001 NSF Design, Manufacturing and Industrial Innovation Resaech Conference, January 7-10-
2001, Tampa, Florida.
6. Jonathan Sarwono, 2012,”A li a i untu Riset Eksperi ental”, PT Elex Media Komputindo, Jakarta.
7. Yang, JL., Chen, JC., 2001, “A Sytematic
Approach for Identifying Optimum Surface Roughness Performance in End Milling Operations”, Journal of Industrial Technology, Vol. 17, No. 2, February to April 2001, http://www.nait.org.
2. Huang L, Chen, J.C, 2001, A Multiple
Regression Model to Predict In-process Surface Roughness in Turning Operation Via Accelerometer, Journal of Industial Technology,Vol. 17, No. 2, February to April 2001, http://www.nait.org.
3. Hando o, B. Tulun Prayo o, (2012), “ Studi Parameter Pemesinan Optimum Pada Operasi CNC End Milling Finishing Bahan Alumunium:,Universitas Gajah Mada, http://www.scribd.com/doc/25352002/Stu di-Paramater-Pemesinan-Optimum-
Pada-Operasi-Cnc, 25 Maret 2012.
4. Her an Sa utro dan Sunaryo,” Predi i
Kekasaran Permukaan Baja ST 40
Berbasis Model Analisa Regresi Ganda ada Pe e inan CNC Frai ”, Univer ita Sebelas Maret Surakarta, http://www.docstoc.com/docs/113842922
/Prediksi-Kekasaran-Permukaan-Baja- ST-40-Berbasis-Model-Analisa-Regresi- Ganda-Pada-Pemesinan-CNC-Frais, 25
Februari 2012
5. Jerard, R.B., et al, 2001, Online
Optomozation of Cutting Conditions for NC Machining, 2001 NSF Design, Manufacturing and Industrial Innovation Resaech Conference, January 7-10-
2001, Tampa, Florida.
6. Jonathan Sarwono, 2012,”A li a i untu Riset Eksperi ental”, PT Elex Media Komputindo, Jakarta.
7. Yang, JL., Chen, JC., 2001, “A Sytematic
Approach for Identifying Optimum Surface Roughness Performance in End Milling Operations”, Journal of Industrial Technology, Vol. 17, No. 2, February to April 2001, http://www.nait.org.