Adaptive Compaction Path Planning for Composite Ply Rollout
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Solution Overview
Problem
Current compaction of ceramic matrix composite plies in composite manufacturing is manual, leading to variable quality, inconsistency, and increased life cycle time due to the need for skilled technicians and time-intensive manual operations.
Innovation Solution
Developing automated path plans for compaction using machine learning to adapt from previous compaction history data, incorporating surface imaging to identify inconsistencies, and generating adaptive path plans to optimize compaction routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual compaction operations are performed using compaction roller hand tools, then skilled technicians can perform the compaction, but the quality is variable and inconsistent
Solution Approach 1:
The patent replaces manual mechanical compaction operations with an automated robotic system that uses imaging technology (optical/electromagnetic fields) to detect inconsistencies and automatically adjusts compaction parameters. The robotic compaction roller is controlled by a system that processes image data to identify areas requiring additional compaction, eliminating human variability and achieving consistent quality.
Solution Approach 2:
The system incorporates real-time feedback through surface imaging technology that captures images of the composite ply during compaction. The imaging system detects inconsistencies and feeds this information back to the control system, which automatically adjusts the compaction roller's pressure and movement patterns to correct identified issues, ensuring consistent quality outcomes.
2Productivity
If manual compaction operations are performed, then compaction can be completed, but the life cycle time is increased due to inspection and rework
Solution Approach 1:
The automated compaction system performs continuous compaction operations without interruption for inspection or rework. The imaging system operates concurrently with the compaction process, detecting inconsistencies in real-time and triggering immediate corrective actions by the robotic controller, thereby maintaining continuous productive action and eliminating time losses associated with manual inspection cycles.
Solution Approach 2:
The system converts potential defects (harm) into opportunities for immediate correction (benefit). By detecting inconsistencies during the compaction process itself rather than after completion, the system can apply corrective pressure or additional passes to affected areas immediately, preventing defective parts from progressing to later inspection stages where rework would be required.
3Speed
If automated path plans are used for compaction, then compaction speed can be increased, but inconsistencies in compaction may occur
Solution Approach 1:
The system employs dynamic path planning where the robotic compaction roller's trajectory, speed, and pressure are continuously adjusted based on real-time imaging feedback. Rather than following a fixed predetermined path, the system dynamically modifies its operation to respond to detected inconsistencies, maintaining both high speed and uniformity by adapting to actual ply conditions during compaction.
Solution Approach 2:
The system applies different compaction parameters to different local areas of the composite ply based on imaging-detected characteristics. When inconsistencies are detected in specific regions, the controller automatically adjusts pressure, speed, or number of passes for those local areas while maintaining standard parameters in already-satisfactory regions, thereby achieving uniform overall quality without sacrificing overall compaction speed.
Data Source
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AI summary
Methods for developing a path plan for rollout compaction of a composite ply during composite manufacturing include: selecting a working path plan data file from a working path plan repository, running a path adaptation application program to access compaction history data files in a compaction history repository relating to the working path plan data file and to identify an inconsistency in the compaction history data files that is not resolved in the working path plan data file and generating a path adaptation for the working path plan data file based on the inconsistency that is not resolved to form an adapted path plan data file defining an adapted path plan that includes the path adaptation. Additional methods include using path plans for compaction on the layup tool, capturing image data and processing the image data to build and/or add to the compaction history data file.