General awareness on energy consumption is globally growing, becoming a significant performance parameter also in the manufacturing field. The current work outlines a reduced model for the analysis of the energy consumption of a machine tool during face milling operations. The model is characterized by a minimum set of significant parameters describing the product, the process and the machine. The influence of these parameters on energy consumption is represented by a feed forward neural network with 20 inputs, two hidden layers and one output. The input parameters are identified a priori by means of simplifications based on physical and technological considerations, while their relevance is evaluated by a sensitivity analysis using the neural network. The network has been trained and validated on 800 experiments consisting of runs of a continuous-time simulator that estimates the energy consumption of a machine tool during part program execution

The quality of a workpiece resulting from a grinding process is strongly influenced by the static and dynamic behavior of the mechanical system, composed by machine tool, wheel, fixture and workpiece. In particular, the dynamic compliance may cause vibrations leading to poor surface quality. In order to evaluate in advance the process performance in terms of surface quality, a simulation model for surface grinding has been developed, based on workpiece discretization by means of a z-buffer approach. The volume engaged by the wheel is associated to the grinding force by means of a variable specific energy that is a function of the equivalent chip thickness. The model is able to provide static and dynamic grinding force components taking into account the following aspects: nonlinearity of the grinding force with respect to cutting parameters, grinding damping effect, contact stiffness, machine-workpiece dynamics in all the relevant degrees of freedom (radial and tangential both for wheel and workpiece). The implementation in Matlab/Simulink™ environment allows an easy connection with any given mechatronic models of the grinding machine. Stable surface grinding tests with force measurements have been performed on a commercial CNC grinding machine for identifying the model parameters; then, the validation was extended to the dynamic case by introducing an artificial wheel unbalance