A Novel Approach for Minimization of Tool Vibration and Surface Roughness in Orthogonal Turn Milling of Silicon Bronze Alloy
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The advanced manufacturing system is aimed to produce components at right quantity, quality and cost. Turnmilling is one of the advanced machining techniques that combines turning and milling processes for high metal removal rate. In orthogonal turn milling, bottom surface of the end mill cutter removes material from the surface of a rotating workpiece. Optimization of process parameters plays an important role in machining to improve quality and productivity and reduce production cost. In the present work, an advanced teaching learning based optimization (TLBO) technique was introduced to optimize process parameters in orthogonal turn milling of Silicon Bronze. Experiments were conducted at five levels of cutting speed, feed and depth of cuts. Experimental results of surface roughness and amplitude of cutter vibration were analysed using analysis of variance. The experimental results were also used to optimize process parameters through TLBO. Experiments were also conducted using TLBO optimized process parameters and the results were compared with TLBO results. The TLBO results were found to be in good agreement with target values of the responses. Artificial neural networks were developed for the surface roughness and amplitude of cutter vibration to verify optimization.
KeywordsTurn milling Silicon bronze TLBO ANN Tool vibration Optimization
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This work (Major project) was funded by Science and Engineering Research Board, Department of Science and Technology, Government of India. Grant No.: SERB/F/1761/2015-16.
- 1.Milling Technical Guide. Sandvik Coromant, http://www.coromant. Sandvik.com. Accessed in May 2017
- 3.Ramaswamy N (1968) Koeningsberger Experiments with Self propelled rotary cutting tools. In: Proceedings of 9th IMTDR conference, Part, vol 2, pp 945–959Google Scholar
- 16.Rao RV, Kalyankar VD (2013) Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Scientia Iranica E 20(3):967–974Google Scholar
- 17.Rao KV, Murthy PBGSN, Vidhu KP (2017) Assignment of weightage to machining characteristics to improve overall performance of machining using GTMA and utility concept CIRP. J Manuf Sci Technol. https://doi.org/10.1016/j.cirpj.2016.12.001
- 19.Rao RV (2015) Teaching learning based optimization algorithm and its engineering application, Springer publishing company, 2nd chap, 1st edn. ISBN: 3319227319 9783319227313Google Scholar
- 20.Rao KV, Murthy PBGSN (2016) Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM. ANN and SVM. J Intell Manuf. https://doi.org/10.1007/s10845-016-1197-y
- 24.https://www.astm.org/Standards/B98.htm viewed on 10-03-2018
- 26.Sivaiah P, Chakradhar D (2018) Analysis and modeling of cryogenic turning operation using response surface methodology. Silicon. https://doi.org/10.1007/s12633-018-9816-1
- 31.Kianfar E, Shirshahi M, Kianfar F, Kianfar F (2018) Simultaneous prediction of the density, viscosity and electrical conductivity of pyridinium-based hydrophobic ionic liquids using artificial neural network. Silicon. https://doi.org/10.1007/s12633-018-9798-z
- 32.Khan A, Maity K (2018) A comprehensive GRNN model for the prediction of cutting force, surface roughness and tool wear during turning of CP-Ti grade 2. Silicon. https://doi.org/10.1007/s12633-017-9749-0