Aircraft Workpiece Gap Estimation for Shim Selection
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Solution Overview
Problem
In aircraft manufacturing, the precise fastening of workpieces often results in gaps that require measurement, leading to increased operation man-hours and potential internal stress due to tight tolerance requirements, necessitating a method to predict and manage these gaps efficiently.
Innovation Solution
A manufacturing assistance apparatus utilizing machine learning to construct an estimation model from actual measurement data, allowing for the prediction of gap lengths between workpieces and selecting appropriate shims to maintain tolerance without manual measurement, thereby reducing operation man-hours and stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If tight tolerance requirements are applied to ensure precise fastening of workpieces, then manufacturing precision is improved, but operation man-hours increase and internal stress is generated
Solution Approach 1:
The system performs preliminary measurement of workpiece parameters (thickness, position, orientation) before fastening operations. Based on these measurements, the gap estimation model predicts the gap that will form after fastening, allowing operators to prepare appropriate shims in advance and adjust workpiece positions beforehand, thereby reducing on-site adjustment time and manual intervention.
Solution Approach 2:
The patent replaces manual measurement and gap prediction methods with an automated estimation model based on machine learning. The system uses measured workpiece parameters as input to the model, which automatically outputs predicted gap values, eliminating the need for manual gap measurement and reducing operation time while maintaining or improving precision.
2Manufacturing precision
If tight tolerance requirements are applied to ensure precise fastening of workpieces, then manufacturing precision is improved, but internal stress is generated
Solution Approach 1:
The system predicts gap formation before fastening operations by analyzing workpiece parameters (thickness variations, position deviations, orientation angles). This preliminary prediction allows operators to install appropriate shims in advance, compensating for expected gaps and preventing the generation of internal stress during fastening, thereby maintaining manufacturing precision without inducing stress.
Solution Approach 2:
The patent introduces shims as intermediary elements between workpieces to compensate for gaps. The estimation model predicts the required shim thickness based on measured workpiece parameters, allowing the shim to act as a mediator that absorbs dimensional variations and prevents direct stress transmission between fastened components.
3Measurement precision
If manual measurement of gap lengths is performed, then measurement precision is maintained, but productivity decreases
Solution Approach 1:
The patent replaces manual gap measurement with an automated estimation model that calculates predicted gap lengths based on measured workpiece parameters (thickness, position, orientation). This substitution maintains measurement precision through the model's computational accuracy while dramatically improving productivity by eliminating time-consuming manual measurement and calculation processes.
Solution Approach 2:
The system performs gap estimation as a preliminary calculation based on measured workpiece parameters before actual fastening operations. This preliminary estimation provides operators with advance information about expected gap sizes, enabling them to prepare appropriate shims and adjust workpiece positions in advance, thereby streamlining the manufacturing process and improving overall productivity.
Data Source
AI summary
A manufacturing assistance apparatus includes a learning unit and an estimator. The learning unit is configured to load a plurality of pieces of actual measurement data in each of which a gap and a plurality of parameters are associated with each other, and construct an estimation model on the basis of machine learning in which the plurality of pieces of actual measurement data serve as teacher data. The gap is provided between a first workpiece and a second workpiece that eventually structure an airframe of an aircraft and that are eventually fastened to each other. The estimation model estimates the gap from the plurality of parameters. The estimator is configured to derive an estimation value of a length of the gap on which measurement has not yet been performed, on the basis of the estimation model constructed by the learning unit and the plurality of parameters.