A computer-implemented method for quality inspection of a component of a manufacturing device includes obtaining operational data relating to operation of the manufacturing device. The operational data includes a time series of one or more physical properties of the manufacturing device. Status data relating to a component of the manufacturing device is obtained. The status data includes events relating to and/or characteristic properties relevant for utilization of the component within the manufacturing device. The computer-implemented method includes labelling one or more subsets of the operational data by associating one or more of the events and/or characteristic properties to the one or more subsets and providing the one or more subsets as labelled training data for training a machine learning model. The machine learning model serves for outputting a quality indicator based on the labelled training data input. The trained machine learning model is provided for quality inspection.

A turning thread automatic diagnosing method includes collecting a spindle actual speed and a feeding shaft actual speed of a lathe; based upon the spindle actual speed and the feeding shaft actual speed, continuously calculating thread errors to obtain a thread error curve; and analyzing an error cause according to the thread error curve. In addition, a turning thread automatic diagnosing system is disclosed, using the method. A data processing system and a storage medium are also disclosed. The technology of the embodiments can automatically identify the problem causing a thread error and provide a corresponding solution.

A turning thread automatic diagnosing method includes collecting a spindle actual speed and a feeding shaft actual speed of a lathe; based upon the spindle actual speed and the feeding shaft actual speed, continuously calculating thread errors to obtain a thread error curve; and analyzing an error cause according to the thread error curve. In addition, a turning thread automatic diagnosing system is disclosed, using the method. A data processing system and a storage medium are also disclosed. The technology of the embodiments can automatically identify the problem causing a thread error and provide a corresponding solution.

A control system for evaluating residual operating life of a machine component of a machine tool includes a sensor and a control system. The sensor is disposed on the machine tool and is configured to sense movement of the machine component. The control device is configured to calculate a length of time based on a sense signal received from the sensor and sampled at a time point right after the machine component is freshly oiled, and to enable output of an alarm when determining that the length of time thus calculated is smaller than a predetermined length.

A control system for evaluating residual operating life of a machine component of a machine tool includes a sensor and a control system. The sensor is disposed on the machine tool and is configured to sense movement of the machine component. The control device is configured to calculate a length of time based on a sense signal received from the sensor and sampled at a time point right after the machine component is freshly oiled, and to enable output of an alarm when determining that the length of time thus calculated is smaller than a predetermined length.

A body and at least one control unit that stores and/or controls data for drilling processes is disclosed. At least one machining tool is located on the body extends outward from the body, and provides part shaping, at least one image capturing device that is controlled by the control unit and connected with the control unit for capturing images is also present.

A body and at least one control unit that stores and/or controls data for drilling processes is disclosed. At least one machining tool is located on the body extends outward from the body, and provides part shaping, at least one image capturing device that is controlled by the control unit and connected with the control unit for capturing images is also present.

In an approach for early notification of degradation of 3D printed parts, a processor completes an initial scan of a 3D printed part using backscatter techniques when the 3D printed part is installed and idle in the unit. A processor completes a second scan of the 3D printed part using backscatter techniques when the unit is in operation. A processor determines a baseline delta between the initial scan and the second scan. A processor performs an additional scan after a preset time interval of the 3D printed part using backscatter techniques in operation within the unit. A processor determines whether the additional scan is within the baseline delta.

In an approach for early notification of degradation of 3D printed parts, a processor completes an initial scan of a 3D printed part using backscatter techniques when the 3D printed part is installed and idle in the unit. A processor completes a second scan of the 3D printed part using backscatter techniques when the unit is in operation. A processor determines a baseline delta between the initial scan and the second scan. A processor performs an additional scan after a preset time interval of the 3D printed part using backscatter techniques in operation within the unit. A processor determines whether the additional scan is within the baseline delta.

A failure prediction device is provided with a machine learning device configured to learn the state of a brake of a motor with respect to data on the brake. The machine learning device observes brake operating state data indicative of an operating state of the brake when the brake is in a normal state, as state variables representative of a current environmental state, and uses the observed state variables to learn a distribution of the state variables with the brake in the normal state.