This paper presents Taguchi optimization of bevel angle in plasma jet cutting process of aluminium alloy EN AW 5083. Experimentations for this paper were carried out on the basis of standard L27 Taguchi's orthogonal array in which three plasma jet cutting parameters such as cutting speed, arc current and cutting height were arranged at three levels. From the analysis of means, analysis of variance and two-way interactions plot, significant plasma jet cutting process parameters and optimal combination of their levels that lead to minimal bevel angle were identified. The results showed that all three process parameters significantly affect bevel angle response. The predicted response at optimal plasma jet cutting conditions has a good fit with result of bevel angle from observed experiment.
Additive manufacturing is a technology of making a three-dimesional solid object of any shape from a digital model. Today on the global market exist various additive manufacturing processes. All of these processes build parts by applying material layer by layer. In a wide range of different processes there is a problem of selecting an adequate process for a user or company interested in additive manufacturing technology. Solving of such a problem is possible by using multicriteria decision methods which result in ranking of alternatives. Thus the user or company can easily select one of the available additive manufacturing processes. In this paper basic methodology of application of three different multicriteria decision methods in solving the mentioned problem was shown. These methods are: Analytic hierarchy process (AHP), Fuzzy AHP and Preference ranking organization method (PROMETHEE). Available alternatives are processes: 3D printing, Fused Deposition Modeling, Selective Laser Sintering and Photopolymer Jetting.
Abstract:In this paper Artificial Neural Network (ANN) model was developed for prediction of kerf width in plasma jet metal cutting process. Process parameters whose influence was analyzed are cutting height, cutting speed and arc current. An L18 (2 1 x3 7 ) Taguchi orthogonal array experiment was conducted on aluminium sheet of 3 mm thickness. Using the experimental data a feed -forward backpropagation artificial neural network model was developed. After the prediction accuracy of the developed model was verified, the model was used to generate plots that show influence of process parameters and their interactions on analzyed kerf width and to get conlusions about process parameters values that lead to minimal kerf width.
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