Real-time tool condition monitoring in milling by means of control charts for auto-correlated data

Real time monitoring of tool condirions and machining processes has been extensively studies in tne last decades, but a wide gap is stiil present between research activities and commercial tools. One of the factors which currently limit the utilization of these systems is the low flexibility of off-the-shelf solutions: in most cases they need dedicated off-line training sessions to acquire the reference patterns and thresholds, and/or the need for several input data to be defined a priori by a human operator. Instead of exploiting off-line learning sessions and a prior defined thresholds, this paper proposes an approach for automatic modelling of a cutting process and real-time monitoring of its stability that is based only on data acquired on-line during the process itself. This approach avoids any a-priori assumption about expected signal patterns, and it is characterized by an innovative implementation of well known Statistical Process Control techniques. In particular, with regard to milling processes, the paper proposes the utilization of cross-correlation coefficient between repeating signal profiles as the feature to be monitored, and an EWMA (Exponentially Weighted Moving Average) control chart for auto-correlated data as monitoring tool.