Browsing by Subject "Electric discharge machining"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Controlling short circuiting, oxide layer and cavitation problems in electrochemical machining of freeform surfaces
    (Elsevier Ltd, 2018) Demirtas H.; Yilmaz O.; Kanber B.
    Freeform surfaces are widely used in the design of complex parts to satisfy aesthetic and functional requirements, particularly in automotive, aeronautics, and die-mould industries. Traditional machining of freeform surfaces is gradual and involves significant manual interactions. Non-traditional machining processes such as electrochemical machining (ECM) enable to increase productivity and cost effectiveness when machining of freeform surfaces as well as hard to cut materials in large scale production. However, some manufacturing problems may be arisen during ECM process and the control mechanisms for preventing such problems (short circuiting, oxide layer and cavitation problems) are very critical for achieving correct form of freeform surfaces and a complete process without any faults in ECM process. This paper firstly investigates possible causes of the ECM drawbacks such as short-circuiting, cavitation, and oxide-layer formation while ECMing of freeform surfaces and then proposed solutions in order to prevent these drawbacks are discussed. A closed-loop control system was developed using a micro-controller board in order to control short-circuiting. Flow analysis was carried out using an ANSYS® Workbench and four different types of apparatus were designed for preventing the cavitation formation. The conducted experiments showed that the voltage feedback was alone insufficient to prevent short-circuiting during high feed rates. In addition, it was observed that the velocity distribution prevented the cavitation when the velocity was adequate within the gap domain. Additionally, it has been showed that the oxide-layer generation was associated with the amount of contamination in the electrolyte solution. © 2018 Elsevier B.V.
  • No Thumbnail Available
    Item
    Surface roughness prediction of wire electric discharge machining (WEDM)-machined AZ91D magnesium alloy using multilayer perceptron, ensemble neural network, and evolving product-unit neural network
    (Walter de Gruyter GmbH, 2022) Gurgenc T.; Altay O.
    Magnesium (Mg) alloy parts have become very interesting in industries due to their lightness and high specific strengths. The production of Mg alloys by conventional manufacturing methods is difficult due to their high affinity for oxygen, low melting points, and flammable properties. These problems can be solved using nontraditional methods such as wire electric discharge machining (WEDM). The parts with a quality surface have better properties such as fatigue, wear, and corrosion resistance. Determining the surface roughness (SR) by analytical and experimental methods is very difficult, time-consuming, and costly. These disadvantages can be eliminated by predicting the SR with artificial intelligence methods. In this study, AZ91D was cut with WEDM in different voltage (V), pulse-on-time (μs), pulse-off-time (μs), and wire speed (mm s-1) parameters. The SR was measured using a profilometer, and a total of 81 data were obtained. Multilayer perceptron, ensemble neural network and optimization-based evolving product-unit neural network (EPUNN) were used to predict the SR. It was observed that the EPUNN method performed better than the other two methods. The use of this model in industries producing Mg alloys with WEDM expected to provide advantages such as time, material, and cost. © 2022 Walter de Gruyter GmbH, Berlin/Boston.