3d (edited) • General discussion
White Light Inspection Robots
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The implementation of AI-guided white light robot inspection represents a significant shift toward digitizing maintenance, repair, and overhaul (MRO) workflows. This system is engineered to minimize measurement variability while maximizing inspection velocity. The primary technical objective is the creation of a centralized, life-cycle data repository that digitally captures all characteristics of in-service components, establishing a foundational dataset for consistent technical disposition throughout a part's service life.
This automation stems from the rigorous requirements of inspecting Life-Limited Parts (LLP), such as high-pressure turbine (HPT) disks located at the engine core. These components demand precise identification of surface anomalies, including specific failure modes like fretting, corrosion, dents, and scratches, which dictate engineering acceptance or rejection. Current manual methodologies depend on visual inspection using mirrors and flashlights, a process plagued by ergonomic challenges and significant operator fatigue over 8 to 12-hour shifts. Consequently, the interpretation of surface features can suffer from high variability between individual inspectors, leading to inconsistent disposition decisions.
To mitigate these human factors, the white light inspection architecture deploys two articulated industrial robots within a universal workstation. These units are equipped with advanced white light optical scanners designed to traverse the complex geometries of high-precision components, such as GE-90 turbine disks. The system utilizes AI-driven path planning to choreograph the robots' movements in close proximity to the part surface, enabling the automated capture and analysis of topographic data with repeatable accuracy and speed.
The technical platform operates by assigning distinct numerical values to specific defect taxonomies such as cracks, nicks, or fretting while linking this data to the component's serial number for full traceability. This process constructs a cloud-based chronological narrative of the asset, ensuring data integrity across global MRO sites. Evolving beyond initial static image stitching, this iteration incorporates line-scan camera technology to generate high-fidelity, video-like data streams that replicate human visual perception on a high-resolution monitor.
While the system automates data acquisition, the operational logic remains "human-in-the-loop," allowing engineers to focus on final technical disposition rather than manual data collection. The resulting high-resolution mapping of part wear serves as a superior leading indicator compared to traditional simulations, providing precise inputs for future automated workflows such as cleaning, thermal spray, and blending operations.
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White Light Inspection Robots
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