Cognitive Assistance for Manual Aircraft Assembly Adaptation
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Solution Overview
Problem
Aircraft assembly remains largely manual and personnel-intensive due to the inflexibility of existing automation solutions, which struggle to predict and adapt to human behavior in complex assembly situations, leading to insufficient collaboration and error-prone interactions between human workers and digital assistance systems, particularly in quality control and assembly operations.
Innovation Solution
A system that utilizes a monitoring system to acquire physical and physiological data from human workers, implementing a cognitive model to provide state information and predictive data on human behavior, enabling a system control to dynamically adapt to human actions and improve collaboration and quality control through real-time feedback and proactive assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional collaborative robots are used to assist assembly workers, then automation level increases, but the system becomes inflexible and unable to adapt to human behavior in complex assembly situations
Solution Approach 1:
The system continuously monitors assembly workers' actions, positions, and task progress through sensors and tracking systems. This real-time feedback enables the control system to dynamically adjust robotic assistance, predict worker intentions, and adapt to individual working styles, resolving the contradiction between automation and adaptability
Solution Approach 2:
The patent implements dynamic control where robotic assistance parameters (speed, position, force) are continuously adjusted based on real-time worker state and task requirements. This dynamic adaptation allows the system to maintain high automation while remaining flexible to human behavior variations
2Manufacturing precision
If post-assembly quality inspections are conducted to ensure completeness and correctness, then manufacturing precision is improved, but additional work time and post-process fixing requirements increase
Solution Approach 1:
The system performs preliminary quality verification continuously during the assembly process by monitoring component installation status, fastening torque, and assembly sequence compliance. This real-time quality control prevents defects before they occur, eliminating the need for time-consuming post-assembly inspections and rework
Solution Approach 2:
Quality metrics are continuously monitored and fed back to the control system, which can immediately alert workers to potential issues or automatically adjust assembly parameters to maintain quality standards, ensuring precision without delaying production
3Manufacturing precision
If in-process quality inspections are implemented during assembly to improve quality control, then manufacturing precision increases, but the process becomes more complex and costly
Solution Approach 1:
The monitoring system serves multiple functions simultaneously: tracking worker position, monitoring assembly sequence compliance, verifying component installation, and detecting quality issues. This multi-functional approach achieves comprehensive in-process quality control without requiring separate complex inspection systems
Solution Approach 2:
The system uses the existing assembly process data and worker actions to automatically verify quality compliance, eliminating the need for separate inspection devices or additional sensors. The assembly process itself generates the quality verification information
Data Source
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AI summary
A system for cognitive assistance in at least partially manual aircraft assembly comprises a monitoring system configured to monitor an aircraft assembly process and acquire physical and physiological data on a human worker performing assembly operations as part of the aircraft assembly process; a cognitive model of the human worker implemented on a data-processing device and communicatively coupled to the monitoring system to receive the acquired physical and physiological data of the human worker, wherein the cognitive model is configured to provide state information on the human worker and prognostic data on the expected behavior of the human worker during the aircraft assembly process based on the acquired physical and physiological data, wherein the state information and the prognostic data are continuously updated during the aircraft assembly process; and a system control communicatively coupled to the monitoring system and the cognitive model and configured to assess a current state of the aircraft assembly process based on the monitored aircraft assembly process, the state information of the of the human worker and the prognostic data of the cognitive model.