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Abstrakty
Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.
Wydawca
Czasopismo
Rocznik
Tom
Strony
116--128
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
- Mechanical Engineering Department, Government Polytechnic, Murtizapur, Maharashtra, India
autor
- Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India
Bibliografia
- Agarwal S., Dhangde S., Chakraborty S. (2019). Development of association rules to study the parametric influences in non-traditional machining processes. Sadhana, 44, 230.
- Arikatla S.P., Mannan K.T., Krishnaiah A. (2017). Parametric optimization in wire electrical discharge machining of titanium alloy using response surface methodology. Materials Today: Proceedings, 4,
- Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1993). Classification and Regression Tree. New York: Chapman and Hall.
- Dabade U.A., Karidkar S.S. (2016). Analysis of response variables in WEDM of Inconel 718 using Taguchi technique. Procedia CIRP, 41, 886–891.
- Devarajaiah D., Muthumari C. (2018). Evaluation of power consumption and MRR in WEDM of Ti-6Al4V alloy and its simultaneous optimization for sustainable production. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 8, 1–18.
- Han J., Kamber M., Pei J. (2012). Data Mining Concepts and Techniques. USA: Elsevier Inc.
- Ho K.H., Newman S.T., Rahimifard S., Allen R.D. (2004). State of the art in wire electrical discharge machining (WEDM). International Journal of Machine Tools & Manufacture, 44, 12-13, 1247–1259.
- Kass G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 29, 2, 119–127.
- Kumar A., Kumar V., Kumar J. (2013). Multi-response optimization of process parameters based on response surface methodology for pure titanium using WEDM process. International Journal of Advanced Manufacturing Technology, 68, 9–12, 2645–2668.
- Lal S., Kumar S., Khan Z.A., Siddiquee, A.N. (2015). Optimization of wire electrical discharge machining process parameters on material removal rate for Al7075/SiC/Al2O3 hybrid composite. Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 229, 5, 802–812.
- Lusi N., Muzaka K., Soepangkat B.O.P. (2016). Parametric optimization of wire electrical discharge machining process on AISI H13 tool steel using weighted principal component analysis (WPCA) and Taguchi method, ARPN Journal of Engineering and Applied Sciences, 11, 2, 945–951.
- Mandal A., Dixit A.R. (2014). State of art in wire electrical discharge machining process and performance. International Journal of Machining and Machinability of Materials, 16, 1, 1–21.
- Manjaiah M., Laubscher R.F., Kumar A., Basavarajappa S. (2016). Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach. International Journal of Mechanical and Materials Engineering, 11, 7, 2–9.
- Nayak B.B., Abhishek K., Mahapatra S.S. (2018). Parametric appraisal of WEDM taper cutting process using maximum deviation method. Materials Today: Proceedings, 5, 11601–11607.
- Patel V.D., Vaghmare R.V. (2013). A review of recent work in wire electrical discharge machining (WEDM). International Journal of Engineering Research and Applications, 3, 3, 805–816.
- Ramanan G., Elangovan R. (2018). Parametric optimization of wire cut electrical discharge machining on Al9% PAC composites using desirability approach. International Journal of Vehicle Structures and Systems, 10, 6, 467–470.
- Ramani J., Dandge S., Chakraborty S. (2020). Machinability study of plain carbon steels using data mining technique. AIP Conference Proceedings, 2273, 050005.
- Sarker B., Chakraborty S. (2021). Structural equation modeling-based performance estimation and parametric analysis of wire electrical discharge machining processes. Sadhana, 46, 1–14.
- Srinivasarao G., Suneel D. (2018). Parametric optimization of WEDM on α-β titanium alloy using desirability approach. Materials Today: Proceedings, 5, 7937–7946.
- Tan P.N., Steinbach M., Kumar V. (2006). Introduction to Data Mining Instructor’s Solution Manual. USA: Pearson Addison Wesley.
- Vignesh M., Ramanujam R. (2018). Response optimization in wire electrical discharge machining of AISI tool steel using Taguchi GRA approach. International Journal of Machining and Machinability of Materials, 20, 5, 474–495.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1c6862b8-6ca4-4380-855a-0330cdc695a3