Automated Path Planning for Composite Rollout Compaction
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Solution Overview
Problem
Current compaction of ceramic matrix composite plies in composite manufacturing is a manual and time-intensive process, leading to variable quality and inconsistencies due to the reliance on skilled technicians and manual tools.
Innovation Solution
The development of automated path plans for rollout compaction using machine learning to adapt from previous path plans, optimizing the compaction routine by generating autogenrated and corrective paths, and utilizing surface imaging technology to identify inconsistencies and adapt the path plans accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual compaction operations are performed using compaction roller hand tools, then the process can be completed with existing equipment, but the quality is variable and inconsistencies occur
Solution Approach 1:
The patent replaces manual mechanical compaction operations with an automated robotic system that uses sensors to detect inconsistencies and automatically adjusts compaction parameters. The robotic arm with compaction roller is controlled by a computing device that processes sensor data and generates adaptive path plans, eliminating manual operation variability.
Solution Approach 2:
The system incorporates sensors that continuously monitor the compaction process and detect inconsistencies in real-time. The sensor data is fed back to the control system, which then adjusts the compaction path and parameters dynamically to maintain consistent quality across different plies and tools.
2Productivity
If manual compaction operations are performed by skilled technicians, then the process can be completed, but it is time intensive and requires inspection and rework
Solution Approach 1:
Real-time sensor monitoring detects compaction inconsistencies during the process, allowing immediate corrective action rather than waiting for post-compaction inspection. This feedback loop eliminates rework by preventing defects before they occur.
Solution Approach 2:
The automated system performs self-inspection through integrated sensors that continuously monitor compaction quality. The system automatically detects and corrects its own performance deviations without requiring separate manual inspection steps, reducing both time and labor requirements.
3Adaptability or versatility
If a fixed path plan is used for compaction, then the process is simple to implement, but it cannot adapt to inconsistencies in compaction history data
Solution Approach 1:
The path plan transitions from a static, pre-defined route to a dynamic trajectory that adapts in real-time based on sensor feedback. The robotic system continuously modifies its compaction path and parameters during operation to respond to detected inconsistencies, making the system adaptive while maintaining operational simplicity through automated control.
Solution Approach 2:
The system pre-processes compaction history data and generates adaptive path plans before actual compaction begins. By analyzing historical data in advance and preparing corrected paths beforehand, the system achieves adaptability without adding complexity during the actual compaction operation.
Data Source
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.