Experimental modeling of the milling process of aluminum alloys used in the aerospace industry

• Autorzy: Țîțu Aurel Mihail , Pop Alina Bianca , Nabiałek Marcin , Dragomir Camelia Cristina , Sandu Andrei Victor

Identyfikatory

DOI

10.24425/bpasts.2021.138565

• Języki publikacji: EN

Abstrakty

EN

This research presents an experimental study carried out for the modeling and optimization of some technological parameters for the machining of metallic materials. Certain controllable factors were analyzed such as cutting speed, depth of cut, and feed per tooth. A dedicated research methodology was used to obtain a model which subsequently led to a process optimization by performing a required number of experiments utilizing the Minitab software application. The methodology was followed, and the optimal value of the surface roughness was obtained by the milling process for an aluminum alloy type 7136-T76511. A SECO cutting tool was used, which is standard in aluminum machining by milling. Experiments led to defining a cutting regime that was optimal and which shows that the cutting speed has a significant influence on the quality of the machined surface and the depth of cut and feed per tooth has a relatively small impact on the chosen ranges of process parameters.

Słowa kluczowe

EN

mathematical modeling; experimental research; process parameters; machined surface quality; quality assurance

PL

modelowanie matematyczne; badania eksperymentalne; parametry procesu; jakość obrabianej powierzchni; zapewnienie jakości

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2021
• Tom: Vol. 69, nr 5
• Strony: art. no. e138565
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Țîțu Aurel Mihail

• Lucian Blaga University of Sibiu, 10 Victoriei Street, 550024, Sibiu, Romania
• The Academy of Romanian Scientists, 54 Splaiul Independenței, Sector 5, 050085, Bucharest, Romania

• https://orcid.org/0000-0002-0054-6535

autor

Pop Alina Bianca

• Technical University of Cluj-Napoca, 62A Victor Babeș Street, Baia Mare, Romania

autor

Nabiałek Marcin

• Department of Physics, Częstochowa University of Technology, Al. Armii Krajowej 19, 42-200 Częstochowa, Poland

autor

Dragomir Camelia Cristina

• The Academy of Romanian Scientists, 54 Splaiul Independenței, Sector 5, 050085, Bucharest, Romania
• Transilvania University of Brasov, 500036 Brasov, Romania

autor

Sandu Andrei Victor

• Gheorghe Asachi Technical University, Blvd. D. Mangeron 71, 700050 lasi, Romania
• Romanian Inventors Forum, Str. Sf. P. Movila 3, 700089 Iasi, Romania

Bibliografia

• [1] B. Reddy, J. Sidda, Suresh Kumar, and K. Vijaya Kumar Reddy, “Optimization of surface roughness in CNC end milling using response surface methodology and genetic algorithm”, Int. J. Eng. Sci. Technol., vol. 3, no. 8, pp. 102‒109, 2011.
• [2] N.V. Prajina, “Multi-response optimisation of CNC end milling using response surface methodology and desirability function”, Int. J. Eng. Res. Technol., vol. 6, no. 6, pp. 739‒746, 2013.
• [3] K.V. Raju, K. Murali, G.R.Janardhana, P.N. Kumar, and V.D.P. Rao, “Optimization of cutting conditions for surface roughness in CNC end milling”, Int. J. Precis. Eng. Manuf., vol. 12, no. 3, pp. 383‒391, 2011.
• [4] S. Patel, Bharat, and H. Pal, “Optimization of machining parameters for surface roughness in milling operation”, Int. J. Applied Eng. Res., vol. 7, no. 11, 2012.
• [5] A.M. Țîțu and A.B. Pop, “A Comparative Analysis of the Machined Surfaces Quality of an Aluminum Alloy According to the Cutting Speed and Cutting Depth Variations”, Lecture Notes in Network and Systems: New Technologies, Development and Application , vol II, no. 76, pp. 212‒218, 2019.
• [6] A.B. Pop and A.M. Țîțu, “A Comparative Analysis of the Machined Surfaces Quality of an Aluminum Alloy According to the Cutting Speed and Feed per Tooth Variations”, Lecture Notes in Network and Systems: New Technologies, Development and Application, vol II, no. 76, 238‒244, 2019.
• [7] A.M. Țîțu, A.V. Sandu, A.B. Pop, Ș. Țîțu, and T.C. Ciungu, “The Taguchi Method application to improve the quality of a sustainable process”, IOP Conf. Ser.: Mater. Sci. Eng., vol. 374, p. 012054, 2018.
• [8] A. Abdallah, B. Rajamony, and A. Embark, “Optimization of cutting parameters for surface roughness in CNC turning machining with aluminum alloy 6061 material” Optimization, vol 4, no. 10, pp. 1‒10, 2014.
• [9] K. Kadirgama, M.M. Noor, M.M. Rahman, M.R.M. Rejab, Ch.H. CheHaron, and K. A. Abou-El-Hossein,“Surface roughness prediction model of 6061-T6 aluminium alloy machining using statistical method”Euro. J. Sci. Res., vol. 25, no. 2, pp. 250‒256, 2009.
• [10] Q. Arsalan, S. Nisar, and A. Shah, M.S. Khalid, and M.A. Sheikh, “Optimization of process parameters for machining of AISI-1045 steel using Taguchi design and ANOVA”, Simul. Modell. Pract. Theory, vol 59, pp. 36‒51, 2015.
• [11] F.Kahraman, “The use of response surface methodology for the prediction and analysis of surface roughness of AISI 4140 steel”, Mater. Technol., vol. 43, pp. 267–270, 2009.
• [12] B.C. Routara, A. Bandyopadhyay, and P. Sahoo, “Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation”, Int. J. Adv. Manuf. Technol. , vol 40, no. 11‒12, pp. 1166‒1180, 2009.
• [13] P. Sahoo, “Optimization of turning parameters for surface roughness using RSM and GA”, Adv. Prod. Eng. Manag., vol. 6 no. 3, pp. 197–208, 2011.
• [14] R.H. Myers and D.C. Montgomery, “Response surface methodology process and product optimization using designed experiments”, John Wiley and Sons, New York, 2002.
• [15] G.E.P Box and N.R. Draper, “Response surface mixtures and ridge analysis”, John Wiley and Sons, New Jersey, 2007.
• [16] R.H. Myers, D.C. Montgomery, and C. M. Anderson-Cook, “Response surface methodology: process and product optimization using designed experiments”, John Wiley & Sons, Inc, 2016.
• [17] T.Prvan and D.J. Street, “An annotated bibliography of application papers using certain classes of fractional factorial and related designs”, J. Stat. Plann. Inference, vol. 106, pp. 245‒269, 2002.
• [18] A.M. Țîțu et al., “Design of Experiment in the Milling Process of Aluminum Alloys in the Aerospace Industry”, Appl. Sci., vol. 10, p. 6951, 2020.
• [19] M. Kuntoğlu, A. Aslan, D.Y. Pimenov, K. Giasin, T. Mikolajczyk, and S. Sharma, “Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel”, Materials, vol. 13, p. 4242, 2020.
• [20] X. Li, Z. Liu, and X. Liang, “Tool Wear, Surface Topography, and Multi-Objective Optimization of Cutting Parameters during Machining AISI 304 Austenitic Stainless Steel Flange”, Metals, vol. 9, p. 972, 2019.
• [21] Y. Su, G. Zhao, Y. Zhao, J. Meng, and C. Li, “Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel”, Metals, vol. 10, p. 217, 2020.
• [22] A. Ahmad, M.A. Lajis, N.K. Yusuf, and S.N. Ab Rahim, “Statistical Optimization by the Response Surface Methodology of Direct Recycled Aluminum-Alumina Metal Matrix Composite, MMC-AlR) Employing the Metal Forming Process”, Processes, vol. 8, p. 805, 2020.
• [23] A.K. Parida, and K. Maity, “Modeling of machining parameters a_ecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology, RSM)”, Measurement, vol. 137, pp. 375–381, 2019.
• [24] N.K. Sahu and A.B. Andhare, “Modelling and multiobjective optimization for productivity improvement in high-speed milling of Ti–6Al–4V using RSM and GA”, J. Braz. Soc. Mech. Sci. Eng., vol. 39, pp. 5069–5085, 2017.
• [25] I. Asilturk, S. Neseli, and M.A. Ince, “Optimization of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods”, Measurement, vol. 78, pp. 120–128, 2016.
• [26] M. Beniyel, M. Sivapragash, S.C. Vettivel, P. Senthil Kumar, K.K. Ajith Kumar, and K. Niranjan, “Optimization of tribology parameters of AZ91D magnesium alloy in dry sliding condition using response surface methodology and genetic algorithm”, Bulletin of The Polish Academy of Sciences, Technical Sciences, vol. 69(1), 1‒10, 2021.
• [27] S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 4, pp. 729‒739, 2019.
• [28] M. Nabiałek, “Influence of the quenching rate on the structure and magnetic properties of the Fe-based amorphous alloy”, Arch. Metall. Mater., vol. 61, no. 1, pp. 439–444, 2016.
• [29] J. Michalczyk, M. Nabiałek, and M. Szota, “Mathematical modelling of thermo-elasto-plastic problems and the solving methodology on the example of the tubular section forming process”, Arch. Metall. Mater., vol. 61, no. 3, pp. 1655–1662, 2016.