Augmented Reality Kit (BARK)

Objective

To enhance aircraft manufacturing and maintenance through real-time, AR-powered digital overlays that improve accuracy, reduce errors, and accelerate training and production workflows.

Value

Increased production efficiency and reduced assembly time
Decrease in human errors and costly rework
Immersive, accelerated training for new technicians
Real-time expert collaboration via remote assistance
Improved first-time quality and compliance with rigorous aerospace standards

AR Role

BARK uses AR headsets and tablets to overlay digital instructions directly onto aircraft components. This guides technicians through wiring, structural alignment, and inspections with high precision. AR enables step-by-step interactive guidance, on-the-spot quality checks, and hands-free access to engineering data in real-time.

AR Case Study Showcase

Augmented Reality Kit

Problem

Traditional aerospace manufacturing relied on paper manuals and static digital displays, which:

Slowed workflows and created room for interpretation errors
Complicated training of new employees
Led to costly delays, rework, and inconsistent quality
Boeing needed a smarter, faster, and more consistent method to support technicians while maintaining strict quality and safety standards.

Solution

Boeing developed the Boeing Augmented Reality Kit (BARK) starting in 2017. The system integrates with existing digital infrastructure and is used across:

Wiring Installation – Guiding technicians to route and secure complex harnesses
Structural Assembly – Helping align and position components precisely
Quality Control – Enabling inspectors to identify discrepancies in real time. BARK also supports remote expert guidance and training scenarios, making it versatile across use cases.

Outcomes

AR guidance eliminated reliance on paper-based manuals
First-time quality rates improved, with fewer human errors
Training time for new technicians was reduced
Remote collaboration minimized delays and travel requirements
Ongoing plans to integrate AI for predictive maintenance and expand usage across repair operations