ANALYSIS AND MODELLING OF LASER-MICRO EDM-BASED HYBRID MICRO MILLING ON STAINLESS STEEL (SUS304) USING BOX BEHNKEN DESIGN
Keywords:
EDM, Laser, MillingAbstract
Hybrid micro milling is drawing the attention of researchers and engineers to generate micro products with intense dimensional precision to use in discrete applications like aerospace, electronics, and optics. Laser milling is a faster machining process with high material removal rate. It arises some problems like insufficient cutting depth, burrs, uneven edges, charred corners, and many more. On the other hand, the µEDM milling process is slower but produces a better surface finish with perfect alignment without compromising inaccuracy. This study aims to incorporate both machining advantages also to investigate the most substantial laser parameter that influences the output responses of µEDM milling time. In this hybrid micro-milling process, the stainless-steel (304) workpiece (0.5 mm thickness) was used to conduct laser micro-milling varying the laser input parameters such as scanning speed, power, pulse repetition rate, and loop. Sequentially the workpiece was shifted to the µEDM machine to continue µEDM milling with constant EDM parameters (Voltage 80 V, capacitance 1 nF, EDM milling speed 5 µm/sec) using a tungsten tool (0.5 mm thickness) and the total set of experiments (25) was run according to Box Behnken design (BBD). It was found that an increase in scanning speed (ss) factors (A) increases the µEDM milling time slightly. The laser power effect shows that higher laser power machined slot channel consumes less µEDM milling time, which is quite significant as compared to the scanning speed effect. A mathematical model was developed to find a correlation between the laser input parameters and out responses of µEDM milling time. The optimization results reveal that power is the most significant factor affecting the µEDM milling time. Based on the Response Surface Methodology (RSM), the predicted optimized input laser parameter was scanning speed 1577.085 mm/sec, power 15.179 W, pulse repetition rate 8.42 KHz rate, and loop 5.959 nos in where the µEDM milling time would be lower 37.390 min.
References
Melkote, S., Kumar, M., Hashimoto, F., & Lahoti, G. (2009). Laser-assisted micro-milling of hard-to-machine materials. CIRP annals, 58(1), 45-48.
Masuzawa, T. (2000). State of the art of micromachining. Cirp Annals, 49(2), 473-488.
Rao, D. B., Rao, K. V., & Krishna, A. G. (2018). A hybrid approach to multi-response optimization of micro milling process parameters using Taguchi method-based graph theory and matrix approach (GTMA) and utility concept. Measurement, 120, 43-51.
Davim, J. P., & Jackson, M. J. (Eds.). (2009). Nano and micromachining (pp. 175-207). ISTE.
Kuram, E., & Ozcelik, B. (2013). Multi-objective optimization using Taguchi-based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill. Measurement, 46(6), 1849-1864.
Thepsonthi, T., & Özel, T. (2014). An integrated toolpath and process parameter optimization for high-performance micro-milling process of Ti–6Al–4V titanium alloy. The International Journal of Advanced Manufacturing Technology, 75, 57-75. https://doi.org/ 10.1007/s00170-014-6102-2
Campanelli, S. L., Casalino, G., Ludovico, A. D., & Bonserio, C. (2013). An artificial neural network approach for the control of the laser milling process. The International Journal of Advanced Manufacturing Technology, 66, 1777-1784.
Pham, D. T., Dimov, S. S., Petkov, P. V., & Dobrev, T. (2005, June). Laser milling for micro tooling. In Proceedings of VIII LAMDAMAP Conference (pp. 362-371).
Pham, D. T., Dimov, S. S., Ji, C., Petkov, P. V., & Dobrev, T. (2004). Laser milling as a ‘rapid’ micromanufacturing process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 218(1), 1-7.
Bissacco, G., Valentincic, J., Hansen, H. N., & Wiwe, B. D. (2010). Towards the effective tool wear control in micro-EDM milling. The International Journal of Advanced Manufacturing Technology, 47, 3-9.
Rajurkar, K. P., Levy, G., Malshe, A., Sundaram, M. M., McGeough, J., Hu, X., ... & DeSilva, A. (2006). Micro and nano machining by electro-physical and chemical processes. CIRP annals, 55(2), 643-666.
Rashid, M. A. N., Saleh, T., Noor, W. I., & Ali, M. S. M. (2021). Effect of laser parameters on sequential laser beam micromachining and micro electro-discharge machining. The International Journal of Advanced Manufacturing Technology, 114, 709-723.
Noor, W. I., Saleh, T., Rashid, M. A. N., Mohd Ibrahim, A., & Ali, M. S. M. (2021). Dual-stage artificial neural network (ANN) model for sequential LBMM-?EDM-based micro-drilling. The International Journal of Advanced Manufacturing Technology, 117(11-12), 3343-3365.
Bhushan, R. K. (2013). Multiresponse optimization of Al Alloy-SiC composite machining parameters for minimum tool wear and maximum metal removal rate. Journal of Manufacturing Science and Engineering, 135(2).
Bhardwaj, B., Kumar, R., & Singh, P. K. (2014). Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box–Cox transformation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(2), 223-232.
Kuram, E., & Ozcelik, B. (2016). Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling. Journal of Intelligent Manufacturing, 27, 817-830.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Kulliyyah of Engineering, IIUM
This work is licensed under a Creative Commons Attribution 4.0 International License.
