Abstract: The invention relates to a method for monitoring a milling process of a printed circuit board, having the steps of: (a) detecting (S1) the rotational speed of a milling head (2) of a milling machine (1) and at least one other operating parameter of the milling machine (1) during the milling process, wherein the other operating parameter is an electric supply current for operating the milling machine, and (b) analyzing (S2) the detected rotational speed and the detected operating parameter using a trained adaptive algorithm for detecting anomalies during the milling process.

Abstract: A computer-implemented method of monitoring a cyclically operating manufacturing device includes measuring actual values of a physical property relating to operation of the manufacturing device during multiple cycles of the operation of the manufacturing device. Reference values are determined for the physical property for each of the multiple cycles based on a trained machine learning model. A distribution of the actual values is compared with a distribution of the reference values based on a distance function, and an alert is initiated when the distance function exceeds a predetermined threshold.

Abstract: A computer-implemented method of monitoring a cyclically operating manufacturing device includes measuring actual values of a physical property relating to operation of the manufacturing device during multiple cycles of the operation of the manufacturing device. Reference values are determined for the physical property for each of the multiple cycles based on a trained machine learning model. A distribution of the actual values is compared with a distribution of the reference values based on a distance function, and an alert is initiated when the distance function exceeds a predetermined threshold.

Abstract: A computer-implemented method of monitoring a cyclically operating manufacturing device includes measuring actual values of a physical property relating to operation of the manufacturing device during multiple cycles of the operation of the manufacturing device. Reference values are determined for the physical property for each of the multiple cycles based on a trained machine learning model. A distribution of the actual values is compared with a distribution of the reference values based on a distance function, and an alert is initiated when the distance function exceeds a predetermined threshold.

Abstract: A computer-implemented method, a data processing system, and a computer program product for indicating a failure of a manufacturing process, as well as a corresponding manufacturing machine and a computer-implemented method of training a machine learning system (MLS) for indicating states of a manufacturing process, are provided. An input signal of a sensor is transformed into a parameter. The parameter is provided to the MLS, which derives latent features. The latent features are mapped into one of a number of distinct clusters each representing a mode of the manufacturing process. A failure of the manufacturing process based on the different states of the manufacturing process may be indicated.

Abstract: The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.

Abstract: A computer-implemented method, a data processing system, and a computer program product for indicating a failure of a manufacturing process, as well as a corresponding manufacturing machine and a computer-implemented method of training a machine learning system (MLS) for indicating states of a manufacturing process, are provided. An input signal of a sensor is transformed into a parameter. The parameter is provided to the MLS, which derives latent features. The latent features are mapped into one of a number of distinct clusters each representing a mode of the manufacturing process. A failure of the manufacturing process based on the different states of the manufacturing process may be indicated.

Abstract: The invention relates to a method for monitoring a milling process of a printed circuit board, having the steps of: (a) detecting (S1) the rotational speed of a milling head (2) of a milling machine (1) and at least one other operating parameter of the milling machine (1) during the milling process, wherein the other operating parameter is an electric supply current for operating the milling machine, and (b) analyzing (S2) the detected rotational speed and the detected operating parameter using a trained adaptive algorithm for detecting anomalies during the milling process.

Abstract: The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.