S. Markos

Satisfying various requirements in different levels and stages of machining using one general ANN-based process model

Abstract Reliable process models are extremely important in different fields of computer integrated manufacturing. They are required, e.g., for selecting optimal parameters during process planning, for designing and implementing adaptive control systems or model based monitoring algorithms. Artificial neural networks (ANNs) can be used as process models because they can handle strong non-linearities, a large number of parameters, missing information and can be used also when no exact knowledge is available about the relationships among the various parameters of manufacturing [2] , [14] . The input–output configuration of the used ANN strongly influences the accuracy of the developed model especially if dependencies between parameters are non-invertable. At various stages of production (e.g., in planning, optimisation or control) different tasks arise, consequently, the estimation capabilities of the related applied models are different even if the same set of parameters is used. One of the main goals of the research to be reported here was to find a general model for a set of assignments, which can satisfy accuracy requirements. Research was also focused on how to apply the general model for various tasks.

Investigation of machinability of iron based metal matrix composite (MMC) powder metallurgy parts

One of the advantages of powder metallurgy technology is that we may produce the final geometry of the required part saving considerable time and cost. However there are several applications that require parts need additional machining for example when the product contains threads, cross bore or slots. In these cases cutting of the hard and porous material may causes difficulties in manufacturing. The aim of the introduced research is the experimental investigation of the machinability of the iron based MMC powder metallurgy parts, determining the favourable composition of the powder and advantageous process parameters regarding the properties of the machinability. The research try to answer to the challenge of the poorly defined expression: machinability, and after defining the features and methods of the evaluation we develop advises for the proper technology parameters.