Volume 3 Issue 3
Authors: S. J. Ebeid; M. R. A. Atia; M. M. Sayed
Abstract: Technology of abrasive water jet (AWJ) is one of the most important processes for machining due to its advantages over other technologies. It has proved to be an efficient process for plain milling of various materials. The paper presents a new predictive model of AWJ milling of aluminum alloy. The model is developed to predict some interesting process parameters from process variables. As AWJ is a complicated multi input-output system, its model is developed using artificial neural network (ANN) as one of the artificial intelligent models. A feed forward neural network based on back error propagation is used. The ANN training set is generated by extensive experimental work. The tests considered four process variables, which are traverse speed, water jet pressure, stand-off distance and abrasive flow rate and three process parameters, namely; surface roughness, depth of cut and material removal rate. The study of the relation between process variables and parameters yields to eliminate the stand-off distance from the training set. Therefore, the ANN has been designed to have three input neurons for process variables and three output neurons for process parameters. The designed ANN was trained and tested. The ANN succeeded to model the AWJ process by extracting the process parameters from process variables with a regression factor above 90%. This paper is a step towards a better understanding, modeling and controlling of AWJ milling process.
Keywords: Abrasive Water Jet (AWJ); Plain Water Jet (PWJ) Milling; Controlled Depth Milling (CDM); Surface Roughness; Depth of Cut; Material Removal Rate; Artificial Neural Networks
Authors: Anil Kumar Bastola; Hari Prasad Neopane
Abstract: In Nepalese hydropower plant, sediment erosion is a major technical problem as large amount of sediment flows through the Himalayan Rivers. Most of the hydropower plants are affected by the sediment erosion problem. This erosion mainly depends on sediment characteristics like size, concentration, mineral composition and shape of the sediments. This paper focuses on variation of particle size distribution, mineral composition and erosion potential of sediment samples from lower Marsyangdi hydropower plant of Nepal. Sediment samples were collected and analyzed from three different locations: headwork, intake and tailrace. Mineral analyses were carried out by physical method called particle count method in Stereo Zoom Microscope and revealed that quartz is the most abundant mineral and common particle size distribution of sediment varies at different locations of plant. Furthermore, erosion tests were also carried out in rotating disc apparatus (RDA) and found that erosion rate is directly proportional to the sediment size and quartz content.
Keywords: Sediment; Particle size distribution; Mineral; Particle count method; Stereo zoom microscope; Erosion rate