Abstract. Flexible manufacturing (FM) has progressed to the stage where the concepts and the available technology are well established and proven. Investment in FM can be very profitable, but must be aimed at achieving clearly defined objectives. This article describes the scope of FM and shows that the required technical specification is determined by the nature of the potential benefits.The ability to quantify all the potential benefits of FM in financial terms means that investment should not be made without a financial evaluation, but should be treated as any other investment decision. Advice is given to show companies how to identify the companywide benefits of FM and to relate these to the required technology before selecting potential suppliers.

Mathematical models, in which many input variables are involved, require a range of input and output data, since the number of parameters increases with the input variables. GMDH (Group Method of Data Handling) has been used for the identificationof a mathematical model that has many input variables but limited data needs by using a hierarchical structure. This paper proposes a neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH. GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network. As an example of applying the algorithm, the wheel wear equation is identified, using data from experiments of abrasive cut-off. In the model, characteristics of work materials, grinding fluids, factors of wheels, wheel velocity and table feed are used as input variables, and the grinding ratio is the resulting output. The validity of the model is confirmed within the predicted accuracy by using additional data.

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