Fastener Pre-Feeding by Repeatability Rating in Automated Drilling
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Solution Overview
Problem
Automated drilling machines face delays due to the need to measure stack thickness for each hole before selecting the appropriate fastener grip length, leading to inefficiencies in the manufacturing process.
Innovation Solution
A method that aggregates historical manufacturing data to determine specific grip lengths and repeatability ratings for each hole location, allowing for predictive pre-feeding of fasteners based on historical data analysis, including machine learning techniques, to eliminate the need for real-time measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time stack thickness measurement is performed for each hole before fastener selection, then manufacturing precision is ensured, but processing time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing the optimal fastener grip length for each hole identification in a database before actual production. Historical measurement data is analyzed in advance to determine the most appropriate grip length for each hole, eliminating the need for real-time measurement and selection during manufacturing operations
Solution Approach 2:
The system creates a digital copy of the part geometry and hole locations from CAD data, and uses this virtual model to retrieve pre-determined fastener specifications from the database. This virtual copying approach replaces physical measurement with information retrieval, significantly reducing processing time while maintaining precision
2Manufacturing precision
If conventional measurement and selection process is used for each hole, then appropriate fastener grip length is determined, but production efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing the optimal fastener grip length for each hole identification in a database before actual production. Historical measurement data is analyzed in advance to determine the most appropriate grip length for each hole, eliminating the need for real-time measurement and selection during manufacturing operations
Solution Approach 2:
The system implements feedback by continuously collecting measurement data from produced parts, analyzing this data to improve the accuracy of fastener grip length predictions, and updating the database accordingly. This closed-loop feedback mechanism enhances both precision and efficiency over time by learning from actual production results
Data Source
AI summary
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.