Abstract: Provided is a computer-implemented method for receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; extracting at least one additional information of each log file of the at least two log files; and combining each log file of the at least two log files with the extracted additional information into at least two processed log files; wherein the at least two processed log files comply with a coherent representation. A corresponding computer program product and generating unit is also provided.

Abstract: A method of virtually inspecting a quality of a product in a production environment includes receiving production information associated with the product, wherein the information is indicative of an operation performed on the product in relation to a first process of the production environment. The method further includes: determining a label for the product using a first classifier model based on the received information associated with the product; comparing the determined label against inspection data associated with an inspection of the product; and storing the production information and the inspection data associated with the product in an extension buffer, based on the comparison of the determined label and the inspection data, for retraining the first classifier model. The above method allows for storing new fault category information that is then dynamically used to retrain the model. Accordingly, this allows for retraining of classifier models without human intervention.

Abstract: In an example aspect, a first object (e.g., an electronic component) is inserted by a robot into a second object (e.g., a PCB). An autonomous system can capture a first image of the first object within a physical environment. The first object can define a mounting interface configured to insert into the second object. Based on the first image, a robot can grasp the first object within the physical environment. While the robot grasps the first object, the system can capture a second image of the first object. The second image can include the mounting interface of the first object. Based on the second image of the first object, the system can determine a grasp offset associated with the first object. The grasp offset can indicate movement associated with the robot grasping the first object within the physical environment. The system can also capture an image of the second object. Based on the grasp offset and the image of the second object, the robot can insert the first object into the second object.

Abstract: Various embodiments of the teachings herein include methods and/or systems for enhancing performance of a machine learning (ML) classification task. An example method includes: obtaining a first prediction generated by a first ML classification model provided with production data as input; obtaining a second prediction generated by a second ML classification model provided with the production data as input; and determining a prediction result for the production data by calculating a weighted sum of the first prediction and the second prediction based on weights for the first ML classification model and the second ML classification model. The first ML classification model comprises a few-shot learning model having a first feature extractor followed by a metric-based classifier. The second ML classification model has a second feature extractor followed by a fully-connected classifier.

Abstract: The invention relates to a production planning method using a plurality of manufacturing devices (INTMA) according to which tasks (TD) of a work plan (BOP) are compared (MA) with manufacturing capabilities (SD) of the manufacturing devices (INTMA) and, depending on the one or more results (MAQ) of said comparison (MA), at least one or more manufacturing devices (INTMA) are commissioned to match their manufacturing capabilities (SD) with the task(s) (TD).

Abstract: Robotics control system (10) and method for training said robotics control system are provided. Disclosed embodiments make a gracefully blended utilization of Reinforcement Learning (RL) with conventional control by way of a dynamically adaptive interaction between respective control signals (20, 24) generated by a conventional feedback controller (18) and an RL controller (22). Additionally, disclosed embodiments make use of an iterative approach for training a control policy by effective use of virtual sensor and actuator data (60) interleaved with real-world sensor and actuator data (54). This is effective to reducing a training sample size to fulfill a blended control policy for the conventional feedback controller and the reinforcement learning controller. Disclosed embodiments may be used in a variety of industrial automation applications.

Abstract: Provided is a computer-implemented method for receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; extracting at least one additional information of each log file of the at least two log files; and combining each log file of the at least two log files with the extracted additional information into at least two processed log files; wherein the at least two processed log files comply with a coherent representation. A corresponding computer program product and generating unit is also provided.

Abstract: The invention relates to a method for adjusting a protective function during operation of a machine (1), in which a transfer region (5) is monitored by a plurality of protective devices (L1, L2), the transfer region (5) being arranged between a risk region (2), in which a dangerous movement is performed by the machine (1), and a surrounding region (3), the transfer region (5) being monitored by first protective devices (L1) in respective first monitoring directions (UI) and, independently thereof, by second protective devices (L2) in respective second monitoring directions (U2), at least one of the first protective devices (L1) and at least one of the second protective devices (L2) being arranged relative to one another in such a way that the first monitoring direction (UI) of the at least one first protective device (L1) and the second monitoring device (U2) of the at least one second protective device (L2) have a point of intersection (S).

Abstract: A design and manufacturing system includes a multi-axis machine tool including a cutting head able to support a plurality of available tools and a part support, the cutting head and part support fully controllable in at least two axes, a design system operable using a computer to generate a 3-D model of a part to be manufactured, and a machine learning model operable using the computer to analyze the part to be manufactured to identify features and develop a manufacturing plan at least partially based on the multi-axis machine tool and the plurality of available tools, the manufacturing plan including a type of tool used for each feature, a feed-rate for each type of tool for each feature, and a speed of the tool for each type of tool for each feature.

Abstract: The invention relates to a method for adjusting a protective function during operation of a machine (1), in which a transfer region (5) is monitored by a plurality of protective devices (L1, L2), the transfer region (5) being arranged between a risk region (2), in which a dangerous movement is performed by the machine (1), and a surrounding region (3), the transfer region (5) being monitored by first protective devices (L1) in respective first monitoring directions (UI) and, independently thereof, by second protective devices (L2) in respective second monitoring directions (U2), at least one of the first protective devices (L1) and at least one of the second protective devices (L2) being arranged relative to one another in such a way that the first monitoring direction (UI) of the at least one first protective device (L1) and the second monitoring device (U2) of the at least one second protective device (L2) have a point of intersection (S).

Abstract: Provided is a Computer-implemented method for Receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; Extracting at least one additional information of each log file of the at least two log files; and Combining each log file of the at least two log files with the extracted additional information into at least two processed log tiles; wherein the at least two processed log files comply with a coherent representation. Further, the invention relates to a corresponding computer program product and generating unit.

Abstract: A system may include an insighter engine configured to access conceptual plans for previously manufactured products, and a given conceptual plan may include a bill of materials (BoM), a bill of processes (BoP), and a bill of resources (BoR). The insighter engine may be configured to represent the conceptual plans according to an insighter ontology and apply machine learning, using the conceptual plans represented according to the insighter ontology as training data, to learn a manufacturing constraint not already represented in the conceptual plans. The system may also include a predictor engine configured to access a BoM for a variant product that differs from the previously manufactured products and apply the learned manufacturing constraint to generate a predicted BoP and a predicted BoR for the BoM of the variant product.

Abstract: The invention relates to a production planning method using a plurality of manufacturing devices (INTMA) according to which tasks (TD) of a work plan (BOP) are compared (MA) with manufacturing capabilities (SD) of the manufacturing devices (INTMA) and, depending on the one or more results (MAQ) of said comparison (MA), at least one or more manufacturing devices (INTMA) are commissioned to match their manufacturing capabilities (SD) with the task(s) (TD).