Volume 3, Issue 2, June 2018, Page: 18-27
Experimental Study of Hydrodynamic Characteristics and Heat Transfer for a Fluid Flow into a Non-Traditional Machining
Raad Muzahem Fenjan, Department of Materials Engineering, Mustansiriyah University, Bagdad, Iraq
Received: Aug. 6, 2018;       Accepted: Aug. 17, 2018;       Published: Sep. 13, 2018
DOI: 10.11648/j.mlr.20180302.12      View  1202      Downloads  67
The non-traditional method used in this work was an electrochemical machining. The experimental work includes designing of machining cell, preparing of fluid solution, selecting the work piece and designing of test rig. The aim of this paper was obtain the gap profile which based on the deviation with respect to equilibrium gap width, also, the electrolyte conductivity deviation with respect to inlet electrolyte conductivity along flow path with the effect electrolyte temperature was obtained for the machining cell. A particular machining cell of two dimensions of (30 mm) width and (50 mm) length, with two dimension turbulent flow for an electrolyte in gap has been selected. For this machining cell, an electrolyte solution (10% w/w NaCl) and the work piece (En8 mild steel) are used. The influence of various parameters, such as supply voltage(12 to 18 volt), tool federate(0.35 to 1.65 mm/min), electrolyte flow rate(5 to 30 lit/min), temperature (40°C) and back pressure (0 to 6 bar) on the gap width and electrolyte conductivity profiles along flow path of the machining cell. The inlet operating parameters for the machining cell were selected within the range of industrially realistic machining circumstances. The optimal control on flow rate and temperature of a electrolyte which refers to gap width without deviation are observed experimentally.
Electrochemical Machining, Gap Width, Electrolyte Flow Rate and Temperature, Electrolyte Conductivity, Control
To cite this article
Raad Muzahem Fenjan, Experimental Study of Hydrodynamic Characteristics and Heat Transfer for a Fluid Flow into a Non-Traditional Machining, Machine Learning Research. Vol. 3, No. 2, 2018, pp. 18-27. doi: 10.11648/j.mlr.20180302.12
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