DESIGN OF EXPERIMENT IN INVESTIGATION REGARDING MILLING MACHINERY

Authors

DOI:

https://doi.org/10.20998/2078-7405.2020.92.09

Keywords:

full factorial design, fractional factorial design, Taguchi method, response surface methodology, milling technology.

Abstract

Design of experiment (DOE) is a systematic method used to determine the relationships between independent factors and dependent variables. This information can be used either to get deep knowledge of the existing problems or to explore new processes. The DOE is important because it can give more details about the processes with the minimum usage of resources, materials and time. In this paper, four methods of design of experiment and their applications in the field of milling machines (full factorial, fractional factorial, Taguchi method and response surface methodology) are argued. The aim of this paper is to give a comprehensive overview and classification of the use of these methods and present the current trends in investigation of milling technology.

Author Biographies

Mgherony Abdul W., Óbuda University, Budapest

PhD student, Óbuda University, Budapest, Bánki Donát Faculty of Mechanical and Safety Engineering Institute of Material and Manufacturing Science Department of Manufacturing Engineering, Hungary

Mikó Balázs, Óbuda University, Budapest

Associate Professor, Óbuda University, Budapest, Bánki Donát Faculty of Mechanical and Safety Engineering Institute of Material and Manufacturing Science Department of Manufacturing Engineering, Hungary

Drégelyi-Kiss Ágota, Óbuda University, Budapest

PhD, Assiciate professor, Óbuda University, Budapest, Bánki Donát Faculty of Mechanical and Safety Engineering Institute of Material and Manufacturing Science Department of Manufacturing Engineering, Hungary

References

B. Durakovic: Design of experiments application, concepts, examples: State of the art. Periodicals of Engineering and Natural Sciences, 5(12):421–439 2017.

A. Drégelyi-Kiss, R. Horváth, B. Mikó: Design of experiments (DOE) in investigation of cutting technologies. Development in Machining Technology/Scientific-Research Reports 3:20-34 2013.

J. Antony: Design of Experiments for Engineers and Scientists. Elsevier Science, 2014.

R. Noorani, Y. Farooque, T. Ioi: Improving surface roughness of CNC milling machined aluminium samples due to process parameter variation. CiteSeerX: http://ineer. org/Events/ICEEiCEER2009/full_papers/full_paper_188.pdf 2009.

M. Kasim, M. Atan, C. Haron, J. Ghani, M. Sulaiman, E. Mohamad, M. Minhat, M. Hadzley, and J. Saedon: Analysis of tool performance during ball-end milling of aluminium alloy 6061-T6. Applied Mechanics and Materials, 761(05):318–323 2015.

D. S. R. Kiran and S. P. Kumar: Multi objective optimization of tool life and total cost using 3-level full factorial method in CNC end milling process. Int. J. Mech. Eng. Robot. Res, 2(3):255–270 2013.

V. Lakshmi and K. V. Subbaiah: Modelling and optimization of process parameters during end milling of hardened steel. International Journal of Engineering Research and Applications 2(2):674–679 2012.

H. Shahrajabian and M. Farahnakian: Multi-constrained optimization in ball-end machining of carbon fiberreinforced epoxy composites by PSO. Cogent Engineering, 2(1):993157 2015.

A. T. Abbas, A. E. Ragab, A. Bahkali, E. Ali, E. Danaf, and E. Adel: Optimizing cutting conditions for minimum surface roughness in face milling of high strength steel using carbide inserts. Advances in Materials Science and Engineering ID:7372132 2016.

K. Vipindas, B. Kuriachen, and J. Mathew: Investigations into the effect of process parameters on surface roughness and burr formation during micro end milling of Ti-6Al-4V. Int. J of Adv Manuf Tech 100(5-8):1207–1222 2019.

D. T. Deshmukh: Experimental investigation of factors affecting milling operation. MAYFEB Journal of Mechanical Engineering, 2: 2017.

G. Bolar, A. Das, and S. N. Joshi: Measurement and analysis of cutting force and product surface quality during end-milling of thin-wall components. Measurement, 121:190–204 2018.

B. Mikó, J. Nagy: Surface profile error of free form surface in z-level milling. Development in Machining Technology 9:75-85 2019.

T. Ryan: Statistical Methods for Quality Improvement. Wiley Series in Probability and Statistics, Wiley 2011.

S. K. Saini and S. K. Pradhan: Optimization of machining parameters for CNC turning of different materials. Applied Mechanics and Materials 592:605–609 2014.

L. D. K. Catherine, R. Ma’arof, R. Aziz, and S. Suresh: A study on the impact of the milling parameters on the surface roughness when using polyurethane board as a base material in manufacturing automotive checking fixtures. Materials Science Forum 819:449–454 2015.

T.-L. B. Tseng, U. Konada, Y. J. Kwon: A novel approach to predict surface roughness in machining operations using fuzzy set theory. Journal of Computational Design and Engineering 3(1):1-13 2016.

Y. El-Taybany, M. Hossam, H. El-Hofy: Experimental investigation of ultrasonic-assisted milling of soda glass using factorial design of experiments. Procedia CIRP 58:381–386 2017.

R. Roy: A Primer on the Taguchi Method. Society of Manufacturing Engineers 2010.

D. P. Singh, R. Mall: Optimization of surface roughness of aluminum by Anova based taguchi method using Minitab15 software. International Journal For Technological Research In Engineering 2(11):2782–2787 2015.

K. Ramesh: Optimization of cutting parameters for minimizing cycle time in machining of SS 310 using Taguchi methodology and Anova. IOSR Journal of Mechanical and Civil Engineering 12(1):31–39 2015.

K. G. Malay, H. N. K. Jaideep Gangwar, A. M. Nitya Prakash Sharma, R. G. Sudhir Kumar: Optimization of process parameters of CNC milling. International Journal of Advance Research and Innovation 3(4):59–63 2016.

S. Ghalme, A. Mankar, Y. Bhalerao: Parameter optimization in milling of glass fiber reinforced plastic (GFRP) using DOE - Taguchi method. SpringerPlus 5(1):1376 2016.

C. Ratnam, K. A. Vikram, B. Ben, B. Murthy: Process monitoring and effects of process parameters on responses in turn-milling operations based on sn ratio and Anova. Measurement 94:221–232 2016.

A. Gupta, C. Krishna, S. Suresh: Modelling and analysis of CNC milling process parameters on aluminium silicate alloy. International Journal of Engineering Technology Science and Research 4(8):1038-1043 2017.

A. Kumar, N. Kumar, S. Kumar, R. Garg: Comparative study of parametric optimization of the end milling of Al2024-SiC MMC on surface roughness using Taguchi technique with applied statistical plots. International Journal of Applied Engineering Research 12(21):10816–10823 2017.

P. B. Sosa, R. D. Makwana, G. Acharya: Optimization of machining parameters on end milling of EN 8 back shaft for power press. Trends in Mechanical Engineering & Technology 8(3) 2018.

E. J. Kim, C. M. Lee: A study on the optimal machining parameters of the induction assisted milling with Inconel 718. Materials 12(2):233 2019.

S. U. Ahmed, R. Arora: Quality characteristics optimization in CNC end milling of A36 K02600 using Taguchi’s approach coupled with artificial neural network and genetic algorithm. International Journal of System Assurance Engineering and Management 10(4):676–695 2019.

R. H. Myers, D. C. Montgomery, C. M. Anderson-Cook: Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons 2016.

M. Subramanian, M. Sakthivel, K. Sooryaprakash, R. Sudhakaran: Optimization of end mill tool geometry parameters for AL7075-T6 machining operations based on vibration amplitude by response surface methodology. Measurement 46(10):4005–4022 2013.

S. Jeyakumar, K. Marimuthu, T. Ramachandran: Prediction of cutting force, tool wear and surface roughness of AL6061/SiC composite for end milling operations using RSM. Journal of Mechanical Science and Technology 27(9):2813–2822 2013.

B. Patel, H. Nayak, K. Araniya, G. Champaneri: Parametric optimization of temperature during CNC end milling of mild steel using RSM. International Journal of Engineering Research & Technology 3(1):69–73 2014.

D. Kumar, G. Rajamohan: Optimization of surface roughness and flatness in end milling of aluminium alloy AL6063-T6. International Journal of Advances in Engineering & Technology 8(6):937 2015.

K. V. Rao, P. Murthy: Modelling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. Journal of intelligent manufacturing 29(7):1533–1543 2018.

M. K. N. Khairusshima, B. M. H. Zakwan, M. Suhaily, I. S. S. Sharifah, N. M. Shaffiar, M. A. N. Rashid: The optimization study on the tool wear of carbide cutting tool during milling carbon fiber reinforced (CFRP) using response surface methodology (RSM). IOP Conference Series: Materials Science and Engineering 290:012068 2018.

G. Başar, F. Kahraman, G. T. Önder: Mathematical modelling and optimization of milling parameters in AA5083 aluminum alloy. European Mechanical Science 3(4):159-163 2019.

A. P. Singh, A. Samad, A. K. Saraf: Enhancement of surface finish by optimization technique employed for AL6061 considering different parameters using RSM. Proceedings of International Conference on Advancements in Computing & Management (ICACM) 1083-1090 2019.

Downloads

Published

2020-07-01

Issue

No. 92 (2020): CUTTING & TOOL IN TECHNOLOGICAL SYSTEM

Section

Mechanical processing of materials, the theory of cutting materials, mathematical and computer simulation of machining p

License

Copyright Notice

Authors who publish with this Collection agree to the following terms:

1. Authors retain copyright and grant the Collection right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this Collection.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the Collection's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this Collection.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.