Abstract: Described herein are methods and manufacturing systems that select fasteners for pre-feeding and, in some examples, pre-feed the selected fasteners. These methods involve aggregating historical manufacturing data, comprising hole identifications and fastener grip lengths, previously selected for these hole identifications. A specific grip length and a corresponding fastener repeatability rating are then determined for each hole identification from this historical manufacturing data. For example, a specific grip length corresponds to the most frequently selected grip length for this hole identification. In some examples, the historical manufacturing data is analyzed using machine learning. The fastener repeatability rating is compared to an operating threshold, in some examples, to determine if the corresponding grip length should be selected for a particular hole location.

Abstract: Described herein are methods and manufacturing systems that select fasteners for pre-feeding and, in some examples, pre-feed the selected fasteners. These methods involve aggregating historical manufacturing data, comprising hole identifications and fastener grip lengths, previously selected for these hole identifications. A specific grip length and a corresponding fastener repeatability rating are then determined for each hole identification from this historical manufacturing data. For example, a specific grip length corresponds to the most frequently selected grip length for this hole identification. In some examples, the historical manufacturing data is analyzed using machine learning. The fastener repeatability rating is compared to an operating threshold, in some examples, to determine if the corresponding grip length should be selected for a particular hole location.

Abstract: Described herein are methods and manufacturing systems that select fasteners for pre-feeding and, in some examples, pre-feed the selected fasteners. These methods involve aggregating historical manufacturing data, comprising hole identifications and fastener grip lengths, previously selected for these hole identifications. A specific grip length and a corresponding fastener repeatability rating are then determined for each hole identification from this historical manufacturing data. For example, a specific grip length corresponds to the most frequently selected grip length for this hole identification. In some examples, the historical manufacturing data is analyzed using machine learning. The fastener repeatability rating is compared to an operating threshold, in some examples, to determine if the corresponding grip length should be selected for a particular hole location.

Abstract: A system and method for cutting at least one of a plurality of features in a workpiece with at least one of a plurality of cutting tools controlled by at least one of a plurality of cutting tool machines is disclosed. The method receives process information describing cutting parameters from cutting tool machines, retrieves predicted cutting tool wear information relating a predicted cutting tool wear value to the cutting parameters, retrieves cutting tool information that include measured cutting tool wear information for the one of the plurality of cutting tools, computes a predicted cutting tool wear value for the one of the plurality of cutting tools from the measured cutting tool wear information, and cuts the at least one feature according to the computed predicted cutter wear value.

Abstract: A system and method for mapping the cutting of a plurality of features in a three dimensional workpiece with a plurality of cutting tools controlled by a plurality of cutting tool machines according to process information is disclosed. The method comprises receiving the process information describing cutting parameters the plurality of features from the plurality of cutting tool machines, parsing the process information to extract the cutting parameters, transforming each feature location in the workpiece from a three dimensional space to a two dimensional space, initiating a call to retrieve cutting tool tracking information from each cutting machine for each of the plurality of cutting tools while cutting the plurality of features in the three dimensional workpiece and providing cutting status associated with each feature at the coordinate transformed location of each feature for presentation in the two dimensional space.

Abstract: A system and method for mapping the cutting of a plurality of features in a three dimensional workpiece with a plurality of cutting tools controlled by a plurality of cutting tool machines according to process information is disclosed. The method comprises receiving the process information describing cutting parameters the plurality of features from the plurality of cutting tool machines, parsing the process information to extract the cutting parameters, transforming each feature location in the workpiece from a three dimensional space to a two dimensional space, initiating a call to retrieve cutting tool tracking information from each cutting machine for each of the plurality of cutting tools while cutting the plurality of features in the three dimensional workpiece and providing cutting status associated with each feature at the coordinate transformed location of each feature for presentation in the two dimensional space.

Abstract: A system and method for cutting at least one of a plurality of features in a workpiece with at least one of a plurality of cutting tools controlled by at least one of a plurality of cutting tool machines is disclosed. The method receives process information describing cutting parameters from cutting tool machines, retrieves predicted cutting tool wear information relating a predicted cutting tool wear value to the cutting parameters, retrieves cutting tool information that include measured cutting tool wear information for the one of the plurality of cutting tools, computes a predicted cutting tool wear value for the one of the plurality of cutting tools from the measured cutting tool wear information, and cuts the at least one feature according to the computed predicted cutter wear value.