A study is presented to model surface roughness in end-milling using soft computing (SC) or computational intelligence (CI) techniques. The techniques include the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). ANFIS combines the learning capability of ANN and the effective handling of imprecise information in fuzzy logic. Prediction models based on multivariate regression analysis (MRA) are also presented for comparison. The machining parameters, namely, the spindle speed, feed rate, and depth of cut, were used as inputs to model the workpiece surface roughness. The model parameters were tuned using the training data maximizing the modelling accuracy. The trained models were tested using the set of validation data. The effects of different machining parameters, number, and type of model parameters on the prediction accuracy were studied. The procedure is illustrated using the experimental data of end-milling 6061 aluminium alloy. Although statistically all three models predicted roughness with satisfactory goodness of fit, the test performance of ANFIS was better than ANN and MRA. In comparison with MRA, the performance of ANN was better in training but similar in test. The results show the effectiveness of CI techniques in modelling surface roughness.
Facility preparation and curriculum design issues have been studied and a Stereolithography Apparatus has been purchased to be used by students of Polymer Processing, Computer Integrated Manufacturing, and Metal Casting-at "GMI Engineering& Management Institute. The apparatus was installed in January of 1996. This is being followed by six months of training in solid modeling and use of the equipment, as well as finalization of early implementation plans for the Polymer Processing and Computer Integrated Manufacturing classes in the summer and fall of 1996. These plans will begin as part design information along with mold design requirements, derived and gathered by Polymer Processing students, are fed to students of Computer Integrated Manufacturing. Upon interfacing with the Polymer students, and equipped with SLA requirements, CIM students will develop appropriate solid models for prototyping. The development of permanent molds by students of metal casting is planned for long term. Plans for the use of this apparatus in pre-college programs is also planned for, with vision toward developing small focus groups comprised of faculty, graduate students, undergraduate students, and prospective students. Young people will be exposed to modern rapid prototyping technology and how it is implemented within an actual manufacturing system. It is expected that in the future, the addition of stereolithography to GMI's Polymer Processing/Computer Integrated Manufacturing laboratories will provide current exposure to manufacturing systems engineering for GMI students and inspiration in the area of Manufacturing Systems Engineering (MSE) to young people considering further education in this field.
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