AR Technician Guidance With AI Process Verification
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Solution Overview
Problem
Modern avionics systems require high precision and quality in assembly and maintenance, but the complexity and automation of these systems make it challenging to guarantee that all manufacturing or maintenance steps are completed accurately and correctly before customer inspection.
Innovation Solution
The system utilizes artificial intelligence (AI) models and augmented reality (AR) guidance to validate the execution of engineering processes by analyzing sensor data from sensors associated with the workpiece, providing real-time feedback and notifications to technicians on the correctness of each engineering process element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual verification methods are used for engineering processes, then technician expertise and training are required, but this leads to increased downtime and higher costs
Solution Approach 1:
The patent replaces manual mechanical verification by technicians with an automated optical inspection system using cameras and image processing algorithms. The system captures images of the workpiece at various stages and automatically analyzes them to verify process completion, eliminating the need for human technicians to physically inspect each component.
Solution Approach 2:
The patent introduces an intermediary automated inspection system that acts as a mediator between the engineering process and final verification. This system includes image capture devices, processing units, and decision-making algorithms that collectively perform the verification function, serving as an intermediate layer that automates what was previously a manual process.
2Manufacturing precision
If traditional manual verification methods are used for engineering processes, then human inspection is required, but this leads to reduced manufacturing precision
Solution Approach 1:
The patent replaces human sensory and cognitive systems with automated optical sensors and computational algorithms. The system uses multiple cameras, lighting systems, and image processing software to achieve consistent, high-precision verification without human error, fatigue, or variability in judgment.
Solution Approach 2:
The patent creates a universal automated inspection system that can verify multiple different engineering processes across various workpieces. The same system architecture, using configurable algorithms and multiple image capture devices, handles diverse verification tasks from assembly completion to quality inspection, reducing the need for separate specialized systems.
3Reliability
If extensive technician training is provided, then higher quality maintenance is achieved, but this increases the time required to learn and perform tasks
Solution Approach 1:
The patent implements a self-service verification system where the automated inspection system performs quality checks independently without requiring trained human operators. The system autonomously captures images, processes them through algorithms, and determines whether engineering processes are complete, making the verification function self-sufficient and eliminating the need for extensive technician training.
Solution Approach 2:
The patent replaces the human expert knowledge and skill-based verification process with an automated system using computer vision and machine learning algorithms. These algorithms have been trained on extensive datasets to recognize correct process completion, effectively encoding expert knowledge into the system without requiring individual technicians to undergo lengthy training programs.
Data Source
AI summary
A system for technician support includes at least one processor, at least one stored artificial intelligence (AI) model stored therein, and a computer program for execution by the processor. The computer program includes instructions for determining an engineering process to be performed on a workpiece, and having one or more elements associated with an assembly process or a maintenance process for the workpiece, receiving sensor data from one or more sensors, the sensor data reflecting at least a physical state of the workpiece, providing the sensor data to the at least one AI model, causing the at least one AI model to process the sensor data to generate at least one classification result that indicates whether the sensor data indicates whether each element was correctly performed, and validating execution of the engineering process in response to each element being correctly performed, and providing a visual notification to a user.