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. This grip length selection is then used for pre-feeding a corresponding fastener into an automated drilling machine, thereby saving significant processing time relative to conventional processes.

A numerical controller stores status data representative of its status in association with a manual data input program executed in this status. In performing a manual data input operation, the stored status data is scored based on its similarity to the current status, and manual data input programs executed in the past are recommended to an operator in the order of priority based on the result of the scoring.

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. This grip length selection is then used for pre-feeding a corresponding fastener into an automated drilling machine, thereby saving significant processing time relative to conventional processes.

A processing program generation method for generating a processing program (PR) for a work machine on the basis of use-results information for a tool (4) attached to a work machine (2), wherein the use-results information for each part of the tool (4) is obtained, a target use part of the tool (4) is set on the basis of the obtained use-results information, and the processing program (PR) is generated in a manner such that a workpiece (W) is processed using the set target use part of the tool (4).