Abstract: A system (1) for predicting anomalies of machining, in particular the anomalies generating a defect on a part machined by a machine tool, comprising: a. a signal-processing unit (10) configured to acquire raw data, in particular sensor data and machining parameters from a machine tool (2) and to process the raw data to neuromorphic-circuit input data including features extracted by the signal-processing unit; b. a neuromorphic circuit (20) connected to the signal-processing unit configured to build a neural network on an integrated circuit, wherein the neural network is trained by training data and the trained neural network enables to determine anomaly data describing anomalies of the machining in response to the neuromorphic-circuit input data, in particular to the features; and c. a controller configured to receive the determined anomaly data from the signal-processing unit and to determine at least one action, which can be taken to overcome the determined anomalies.

Abstract: A system (1) for predicting anomalies of machining, in particular the anomalies generating a defect on a part machined by a machine tool, comprising: a. a signal-processing unit (10) configured to acquire raw data, in particular sensor data and machining parameters from a machine tool (2) and to process the raw data to neuromorphic-circuit input data including features extracted by the signal-processing unit; b. a neuromorphic circuit (20) connected to the signal-processing unit configured to build a neural network on an integrated circuit, wherein the neural network is trained by training data and the trained neural network enables to determine anomaly data describing anomalies of the machining in response to the neuromorphic-circuit input data, in particular to the features; and c. a controller configured to receive the determined anomaly data from the signal-processing unit and to determine at least one action, which can be taken to overcome the determined anomalies.

Abstract: A method for the determination of lifetime of a filter of an electric discharge machine the electrical discharge machine in consideration of a maximum allowable filter pressure, wherein the time measuring unit counts the machining time ts during which an electric discharge machining process is running, a filter pressure sensor measures the filter pressure p(k), preferably with a predetermined sampling interval, the pressure measurement p(k) and the respective sampling time t(k) are stored, and the sampled measurements p(k) and the respective sampling times t(k) are used to determine the parameters of an exponential function which best fits to the plurality of sampled measurements regression analysis. The determined parameters include the filter lifetime tf, which serves to determine the residual time to the filter replacement tr and/or the calendar deadline of filter expiration.

Abstract: A method for the determination of lifetime of a filter of an electric discharge machine the electrical discharge machine in consideration of a maximum allowable filter pressure, wherein the time measuring unit counts the machining time ts during which an electric discharge machining process is running, a filter pressure sensor measures the filter pressure p(k), preferably with a predetermined sampling interval, the pressure measurement p(k) and the respective sampling time t(k) are stored, and the sampled measurements p(k) and the respective sampling times t(k) are used to determine the parameters of an exponential function which best fits to the plurality of sampled measurements regression analysis. The determined parameters include the filter lifetime tf, which serves to determine the residual time to the filter replacement tr and/or the calendar deadline of filter expiration.