Abstract: A method, (e.g., a computer-implemented method), of generating a production plan is provided. The method includes obtaining one or more product and/or production characteristics, (e.g., a production content, a production time, and/or a production quantity, for example, in a E-BOM, M-BOM, and/or CAD file format), for a product to be manufactured. The method further includes determining, by a rule engine, based on the one or more product and/or production characteristics, one or more library elements from a plurality of library elements, wherein each library element includes one or more process descriptions relating to the characteristic. The method further includes arranging, by the rule engine, the one or more process descriptions into a production plan.

Abstract: An apparatus comprising: an input unit to receive production-related data of the production system; a mapping engine to map the production-related data to instance data of a first knowledge graph according to a given mapping definition; a first validation unit to validate a consistency and/or an integrity of the instance data using declarative constraints and to output a first validation result; a simulator to generate a computer-implemented material flow simulation model of the production system based on the instance data and depending on the first validation result; a generator to generate simulated production logs using the material flow simulation model; a second validation unit to validate the simulated production logs against measured production logs of the production system and to output a second validation result; and an output unit to output the second validation result for monitoring the operability of the production system.

Abstract: In various software applications, wizards can assist users in navigating through selected, predefined workflows for automating some repetitive interaction patterns. It is recognized herein, however, that such conventional wizards are generally limited to few use cases that are hard-wired in a given wizard's implementation, such that user still needs to master the respective tool's complexity. In an example aspect, a computing system within an automation system can determine a goal state associated with a target application. The system can extract an initial state from the target application. Based on knowledge obtained by the system, the system can generate a plurality of plans. The plans can define respective sequences of actions for reaching the goal state from the initial state. The system can render options on a user interface of the target application. Based on the options, the system receives selections related to the sequences of actions so as to define selected actions.

Abstract: Observed network communication events are provided as edges in a knowledge graph and processed by an anomaly detection component, generating an alert for the event if its likelihood is below a threshold. An explanation generation component generates explanations for some of the alerts. A filter component removes alerts for which no explanation has been generated. A verbalizer verbalizes the generated explanation for at least one of the remaining alerts. Embodiments employ a hybrid approach by combining symbolic and sub-symbolic algorithms on knowledge graphs in order to improve the explainability and quality of IDS-generated alerts in modem industrial systems, increasing their usefulness for analysts. Explainable AI i.e., the explainability of AI algorithms—enables analysts to understand how embodiments of the system are reaching its conclusions and possibly allows them to interact with it in a collaborative manner.

Abstract: Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.

Abstract: Providing data generally involves a transformation of an OPC UA data model into an ontology. Such an ontology readily provides desired capabilities for validation, querying, and analytics of the OPC UA data model using sophisticated standard tools adapted to the ontology. A method for transforming a semantically enriched and graph-based data model into a target ontology is suggested. By a first act, identifications of nodes are retrieved of the data model. Subsequently or concurrently, semantic descriptions of one or more references interconnecting one or more nodes are retrieved. These semantic descriptions are expressed by at least one or more predicates and one or more concepts (e.g., classes) in the target ontology to be produced. Eventually, the target ontology is structured by a semantic ontology language and output to a triple store.

Abstract: A production support system for generating a plant bill of process, PBoP, from a product bill of process, PdBoP, for a production plant, a corresponding method and a corresponding computer program product is provided. The production support system includes: (a) a reasoning component for determining possible cells; (b) a selection component for selecting all those cells that address operation capabilities used in the PdBoP; (c) an operation capabilities identifying component for identifying, additional operation capabilities for the selected cells which include at least two production entities, PET, based on the operation capabilities of the individual production entities, PET; and (d) a PBoP generating component for generating the PBoP from the PdBoP using the additional operation capabilities identified by the operations identifying component.

Abstract: A method for automatically determining of a changed manufacturing process of a product with changed manufacturing process data, including providing basic data which include historical manufacturing process data of at least one historical manufacturing process of the product, historical product data of the product, target manufacturing process data of a planned manufacturing process or target product data of a planned product classifying the basic data determining the changed manufacturing process with the aid of the classified data, wherein the providing, the classifying, and/or the determining of the changed manufacturing process is carried out with the aid of graph technology. In addition to the method, an apparatus with a computer system for carrying out of the method is provided. The computer system includes at least one insighter engine for the executing of the method and the insighter engine includes at least one data providing tool for providing of the basic data.

Abstract: A computer-implemented method for generating an adapted task graph, including the steps of providing a first input data set with at least one task graph and at least one task context and/or a second input data set with at least one constraint and at least one task context, generating an adapted task graph using a trained neural network based on the first input data set and/or the second input data set, and providing the adapted task graph.

Abstract: Providing data generally involves a transformation of an OPC UA data model into an ontology. Such an ontology readily provides desired capabilities for validation, querying, and analytics of the OPC UA data model using sophisticated standard tools adapted to the ontology. A method for transforming a semantically enriched and graph-based data model into a target ontology is suggested. By a first act, identifications of nodes are retrieved of the data model. Subsequently or concurrently, semantic descriptions of one or more references interconnecting one or more nodes are retrieved. These semantic descriptions are expressed by at least one or more predicates and one or more concepts (e.g., classes) in the target ontology to be produced. Eventually, the target ontology is structured by a semantic ontology language and output to a triple store.

Abstract: A method of operating an intelligent programmable logic controller (PLC) as part of a production process within an automation system includes the intelligent PLC receiving automation system data and a semantic context model comprising a plurality of ontologies providing formal specifications of conceptual entities associated with the automation system. The intelligent PLC creates one or more semantic annotations for the automation system data using the semantic context model. These semantic annotations are stored along with the automation system data in a non-volatile storage medium included in the intelligent PLC.

Abstract: Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.

Abstract: The disclosure relates to a method for checking whether and/or how a technical process may be carried out using a technical system with two or more units having technical capabilities that are potentially relevant to the technical process. At least one ontology is used which describes the technical system and the technical process, and a constraint satisfaction problem is generated using the at least one ontology. In the method, solutions are found for the constraint satisfaction problem. The solutions may be stored or output.

Abstract: A production support system for generating a plant bill of process, PBoP, from a product bill of process, PdBoP, for a production plant, a corresponding method and a corresponding computer program product is provided. The production support system includes: (a) a reasoning component for determining possible cells; (b) a selection component for selecting all those cells that address operation capabilities used in the PdBoP; (c) an operation capabilities identifying component for identifying, additional operation capabilities for the selected cells which include at least two production entities, PET, based on the operation capabilities of the individual production entities, PET; and (d) a PBoP generating component for generating the PBoP from the PdBoP using the additional operation capabilities identified by the operations identifying component.

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: An autonomous production system including a transformation unit adapted to transform an ontology representation of actions of the autonomous production system into a corresponding planning representation; an artificial intelligence planning unit adapted to compute an action plan including a sequence of instantiated actions based on the planning representation; and a retransformation unit adapted to transform the computed action plan into an ontology language representation providing transparency information about the computed action plan.

Abstract: The disclosure relates to a method for checking whether and/or how a technical process may be carried out using a technical system with two or more units having technical capabilities that are potentially relevant to the technical process. At least one ontology is used which describes the technical system and the technical process, and a constraint satisfaction problem is generated using the at least one ontology. In the method, solutions are found for the constraint satisfaction problem. The solutions may be stored or output.

Abstract: A method for producing rare earth metal-doped quartz glass includes the steps of (a) providing a blank of the rare earth metal-doped quartz glass, and (b) homogenizing the blank by softening the blank zone by zone in a heating zone and by twisting the softened zone along a rotation axis. Some rare earth metals, however, show a discoloration of the quartz glass, which hints at an unforeseeable and undesired change in the chemical composition or possibly at an inhomogeneous distribution of the dopants. To avoid this drawback and to provide a modified method which ensures the production of rare earth metal-doped quartz glass with reproducible properties, during homogenization according to method step (b), the blank is softened under the action of an oxidizingly acting or a neutral plasma.

Abstract: The disclosure relates to a method for determining diagnostic models for time series of a technical system and to a diagnostic method. In the method for determining one or more diagnostic model/s for time series of a technical system for the purpose of diagnosing an event, firstly, one or more diagnostic models are formulated and, in a first act, possible extensions of the diagnostic model/s are determined; in a second act, for each extension of the/each diagnostic model/s, a set of sequences is determined from the time series, in which the extension is contained; in a third act, each sequence of said set is tested to determine whether the sequence is connected with the event; and in a fourth act, the/those extensions for which the ratio of the number of sequences of the set which are connected with the event to the number of sequences of the set is largest, is/are set as the new diagnostic model.

Abstract: A method of operating an intelligent programmable logic controller (PLC) as part of a production process within an automation system includes the intelligent PLC receiving automation system data and a semantic context model comprising a plurality of ontologies providing formal specifications of conceptual entities associated with the automation system. The intelligent PLC creates one or more semantic annotations for the automation system data using the semantic context model. These semantic annotations are stored along with the automation system data in a non-volatile storage medium included in the intelligent PLC.