A system and method is provided for monitoring a production process. In some aspects, the system may include an aseptic sampling device in fluidic connection with a process fluid, the aseptic sampling device operative to collect one or more samples from the process stream. A pretreatment device may be included to receive and pretreat the one or more samples. An analyzer is operative to analyze the pretreated samples and to produce one or more mass spectrometry (MS) spectra. A classifier receives and classifies the one or more MS spectra to provide a measure of product quality of the process fluid corresponding to the sampling location and time of sampling.

The invention relates to an HMI system with a mobile operating and monitoring device for operating a production machine for plastics processing. The HMI system is installed on a control unit of the production machine and has a mobile operating and monitoring device.

Embodiments provide systems and methods for utilizing acoustic sensors to detect defects via online or in situ monitoring of additive manufacturing (AM) processes. Sensors may capture acoustic waves associated with AM manufacturing operations. The acoustic emissions in combination with other sensing data, such as cameras or thermometers, may be used to characterize the state of the AM process, such as to detect a defect has occurred or confirm a defect has not occurred. When defects are detected, the AM process may be stopped to prevent further processing of a defective part. When defects are predicted as likely to occur, operational parameters of the AM device or process may be adjusted to mitigate the occurrence of a defect. The techniques disclosed herein enable detection of defects that occur underneath the surface of the part being manufactured, as well as correct issues with the AM device or process before a defect occurs.

The present disclosure relates to a manufacturing method for a sensor transmitter for continuous glucose monitoring and, more particularly, to a manufacturing method for a sensor transmitter, capable of generating quality control information in which production information of a sensor and production information of a transmitter that are used for the sensor transmitter during an assembly process of the sensor transmitter are mapped to each other, and storing and managing the generated quality control information in a server, so as to monitor a distribution channel or a sales channel of the sensor transmitter by using the quality control information, use for quality control of the sensor transmitter, or easily track a bad sensor transmitter.

A method of characterizing plasmas during semiconductor processes may include receiving operating conditions for a semiconductor process, where the semiconductor process may be configured to generate a plasma inside of a chamber of a semiconductor processing system. The method may also include providing the operating conditions for the semiconductor process as inputs to a model, where the model may have been trained to characterize plasmas in the chamber. The method may also include generating, using the model, a characterization of the plasma in the chamber resulting from the operating conditions of the semiconductor process.

Embodiments provide systems and methods for utilizing acoustic sensors to detect defects via online or in situ monitoring of additive manufacturing (AM) processes. Sensors may capture acoustic waves associated with AM manufacturing operations. The acoustic emissions in combination with other sensing data, such as cameras or thermometers, may be used to characterize the state of the AM process, such as to detect a defect has occurred or confirm a defect has not occurred. When defects are detected, the AM process may be stopped to prevent further processing of a defective part. When defects are predicted as likely to occur, operational parameters of the AM device or process may be adjusted to mitigate the occurrence of a defect. The techniques disclosed herein enable detection of defects that occur underneath the surface of the part being manufactured, as well as correct issues with the AM device or process before a defect occurs.

An anomaly determination device and an anomaly determination method determine an anomaly of a device based on state data of the device, by using a first determination model configured to determine whether a predetermined anomaly has occurred in the device, and a second determination model configured to classify state of the device, and output the determined anomaly of the device as an unknown anomaly in a case where the anomaly of the device is not the predetermined anomaly.

The subject of this invention is a method for testing the data and control interface of individual machines intended for interconnection in an inline system for solar cell production. Furthermore, an Interface-Tester suitable for executing the testing method is disclosed. The method for testing comprises the steps of feeding a dummy workpiece to the tested machine and connecting the interface tester to the standard interface of the machine. Consecutively the interface tester sends controlling signals to the machine and receives the signals from the tested machine. The received signals are compared to reference signals and evaluated. The interface tester comprises a standard interface for coupling the machines in an inline system for solar cell production. Furthermore, the interface tester is equipped with at least one CPU, a volatile and/or non-volatile memory, communication modules, couplers and connectors and at least one human-machine interface.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving image data after an operation is performed by an industrial automation device on a product; analyzing the image data based an object-based image analysis (OBIA) model to classify the product as one of a plurality of conditions related to manufacturing quality and the OBIA model includes property layers associated with features related to a manufacturing of the product; determining whether the one of the conditions indicates an anomaly being present in the product; sending a notification indicative of the one of the plurality of conditions is presently associated with the product; identifying a property layer associated with classifying the one of the plurality of conditions; and updating the OBIA model based on the property layer and the input indicative of the anomaly being incorrectly associated with the product.

A method for providing output values with associated uncertainties for a flexible modular plant or machine comprising an arrangement of modular entities, wherein uncertainty information associated with an operation of the modular entity is assigned to a plurality of modular entities and input values are provided based on an operation of the modular entities, where a computing unit calculates an output value based on said input values, calculates an input value uncertainty for each input value based on the uncertainty information of the modular entity, and calculates at least one output value uncertainty associated with the output value based on propagation of uncertainty and using the input value uncertainties, and where the output value and the at least one output value uncertainty are output.