Abstract: Techniques for aircraft manufacture and product support are described. These techniques include providing a three-dimensional (3D) user interface for an aircraft and identifying a component relating to an area of concern for the aircraft, based on input to the 3D user interface. The techniques further include generating structured text for a service request relating to the area of concern based on the identified component, and submitting the service request to facilitate resolving the area of concern for the aircraft.

Abstract: A method of making a reinforcement part for a vehicle includes applying a release film directly onto a surface of the vehicle, applying a first metallic layer directly or indirectly onto the release film, applying a curable bonding layer directly onto the first metallic layer, and applying a second metallic layer directly onto the curable bonding layer. The method also includes applying pressure to the first metallic layer, the curable bonding layer, and the second metallic layer so that the first metallic layer, the curable bonding layer, and the second metallic layer are compressed against the surface. The method further includes curing the curable bonding layer when pressure is applied to the curable bonding layer to form the reinforcement part. The method additionally includes, after curing the curable bonding layer, removing the reinforcement part from the surface.

Abstract: A core structure panel includes a first skin and a triply periodic minimal surface (TPMS) core additively manufactured as a unit. The core structure panel also includes a second skin coupled to the TPMS core. The TPMS core is positioned between the first skin and the second skin.

Abstract: A method of making a reinforcement part for a vehicle includes applying a release film directly onto a surface of the vehicle, applying a first metallic layer directly or indirectly onto the release film, applying a curable bonding layer directly onto the first metallic layer, and applying a second metallic layer directly onto the curable bonding layer. The method also includes applying pressure to the first metallic layer, the curable bonding layer, and the second metallic layer so that the first metallic layer, the curable bonding layer, and the second metallic layer are compressed against the surface. The method further includes curing the curable bonding layer when pressure is applied to the curable bonding layer to form the reinforcement part. The method additionally includes, after curing the curable bonding layer, removing the reinforcement part from the surface.

Abstract: Techniques for aircraft manufacture and product support are described disclosed. These techniques include providing a three-dimensional (3D) user interface for an aircraft and identifying a component relating to an area of concern for the aircraft, based on input to the 3D user interface. The techniques further include generating structured text for a service request relating to the area of concern based on the identified component, and submitting the service request to facilitate resolving the area of concern for the aircraft.

Abstract: The present disclosure provides for predictive query processing for complex system lifecycle management by identifying a seed document including natural language text describing an issue with a complex system; creating a synthesized document for the seed document by: removing portions of the seed document determined to satisfy filtering rules; identifying a first term set included in the natural language text that map to terminology identified in a data dictionary; extracting term-order data based on locations of the first term set in the natural language text; and including the first term set and the term-order data in the synthesized document; identifying, from a corpus of historic electronic documents, a first document set that are related to the seed document based on the term-order data included in the synthesized document; and outputting at least a portion of the first set of historic electronic documents.

Abstract: Techniques for identifying relevant, natural language documents within a data repository. An electronic document specifying natural language text describing an issue with a complex system is received. One or more portions are removed from the electronic document. The portions are determined to satisfy predefined filtering rules. A first set of terms are determined using a data dictionary structure, and a second set of terms are determined, where at least one term in the second set of terms satisfies at least one predefined pattern matching rule. A third set of terms are determined by processing the electronic document as an input to a machine learning model trained to recognize relevant terms within the electronic document. A synthesized electronic document is generated from the first, second and third sets of terms, and the synthesized electronic document is used to identify a set of relevant documents within the data repository.

Abstract: Techniques for identifying relevant, natural language documents within a data repository. An electronic document specifying natural language text describing an issue with a complex system is received. One or more portions are removed from the electronic document. The portions are determined to satisfy predefined filtering rules. A first set of terms are determined using a data dictionary structure, and a second set of terms are determined, where at least one term in the second set of terms satisfies at least one predefined pattern matching rule. A third set of terms are determined by processing the electronic document as an input to a machine learning model trained to recognize relevant terms within the electronic document. A synthesized electronic document is generated from the first, second and third sets of terms, and the synthesized electronic document is used to identify a set of relevant documents within the data repository.

Abstract: Techniques for identifying relevant, natural language documents within a data repository. An electronic document specifying natural language text describing an issue with a complex system is received. One or more portions are removed from the electronic document, where the portions are determined to satisfy one or more predefined filtering rules. A first set of terms are determined using a data dictionary structure, and a second set of terms are determined, where at least one term in the second set of terms satisfies at least one predefined pattern matching rule. A third set of terms are determined by processing the electronic document as an input to a machine learning model trained to recognize relevant terms within the electronic document. A synthesized electronic document is generated from the first, second and third sets of terms, and the synthesized electronic document is used to identify a set of relevant documents within the data repository.

Abstract: The present disclosure provides for predictive query processing for complex system lifecycle management by identifying a seed document including natural language text describing an issue with a complex system; creating a synthesized document for the seed document by: removing portions of the seed document determined to satisfy filtering rules; identifying a first term set included in the natural language text that map to terminology identified in a data dictionary; extracting term-order data based on locations of the first term set in the natural language text; and including the first term set and the term-order data in the synthesized document; identifying, from a corpus of historic electronic documents, a first document set that are related to the seed document based on the term-order data included in the synthesized document; and outputting at least a portion of the first set of historic electronic documents.

Abstract: Techniques for identifying relevant, natural language documents within a data repository. An electronic document specifying natural language text describing an issue with a complex system is received. One or more portions are removed from the electronic document, where the portions are determined to satisfy one or more predefined filtering rules. A first set of terms are determined using a data dictionary structure, and a second set of terms are determined, where at least one term in the second set of terms satisfies at least one predefined pattern matching rule. A third set of terms are determined by processing the electronic document as an input to a machine learning model trained to recognize relevant terms within the electronic document. A synthesized electronic document is generated from the first, second and third sets of terms, and the synthesized electronic document is used to identify a set of relevant documents within the data repository.