Non Destructive Evaluation of Surface Integrity Produced by Milling and Grinding Using Barkhausen Noise Signals

This paper deals with investigation of hard milled and ground surfaces via non destructive Barkhausen noise (BN) technique. The paper compares the raw BN signals, extracted BN features such as effective (rms) values and appearance of hysteresis loops produced by grinding and milling cycles. Information about surface state is correlated and confronted with metallographic observations, SEM readings as well as residual stress state. The paper also discusses the specific character of BN signals (and the corresponding BN features) produced by hard milled surface as a result of the magnetic shape memory effect when the machined surface undergoes severe plastic deformation at elevated temperatures

Neural networks in modeling of CNC milling of moderate slope surfaces

Computer numerical control (CNC) allows achieving a high degree of automation of machine tools by pre-programmed numerical commands. CNC milling process is widely used in industry for machining of complex parts. The need of a description of the CNC milling process is necessary for production of precise parts. This paper introduces artificial neural network based modeling, while the CNC milling of moderate slope shapes is studied. The developed neural models consist of two inputs and two outputs. The created neural models were experimentally tested on the real data. Then, the evaluation and comparison of all models were performed