Inspector Plus
Lead Participant:
BLOC DIGITAL LTD
Abstract
Our **vision for this project** is to disrupt the market for component Inspection in Aerospace by transforming transactional part sentencing into a life cycle value optimisation engine.
Based on our extensive experience, developing geometry and data management solutions for Inspection, Maintenance, Repair and Overhaul service-support solutions for customers like Rolls-Royce, BAE Systems etc., we believe that the market is open to disruption.
Bloc Digital offers a mobile inspection application called **Inspector**. Inspector is used to gather degradation data and images of parts in MRO shops and the field to generate a fleet-wide perspective on part performance in service.
This project will create the **InspectorPlus** application, simplifying scanning and creation of 3D digital twins at part serial number level and transforming the ability to extract critical insight from the data.
**\* InspectorPlus** will enable mobile device users to easily gather accurate Inspection data and apply deep learning to create accurate digital twins of each part.
**\* InspectorPlus** will convert data into insights, enabling optimised life cycle design and support policies to be developed, saving up to 30% of total life cycle cost for typical high wear components.
**\* InspectorPlus** will enable organisations to anticipate and avoid the worst impact of degradation through improved design, contain operational disruption through improved inspection planning and recover part usable life through optimised inspection criteria and repair opportunities.
The digital twin will track part condition over time, transforming understanding of how parts degrade with use. Serialised digital twins can be combined virtually to generate insight into how the design standard, product and system are performing.
The project applies an innovative mixture of technologies to create a step-change in the efficiency of creation of part digital-twins. Key innovations include the application of Neural Radiance Fields (NeRFs) to the gathering of data, the application of deep learning to create a parametric digital twins, highlighting the variations in degradation and deformation of the part based on known factors.
Bloc Digital who will lead the project and develop and deliver the production application.
The University of Derby's Department of Computing Science will contribute their extensive knowledge in data analytics and machine learning.
DFS Consulting will join the project as a sub-contractor with their lead Consultant, Andy Harrison, a former Rolls-Royce Engineering Associate Fellow for Service Knowledge Management / Aston University Visiting Professor in Design for Service, providing his deep technical expertise on extracting the maximum service value from the data we gather.
Based on our extensive experience, developing geometry and data management solutions for Inspection, Maintenance, Repair and Overhaul service-support solutions for customers like Rolls-Royce, BAE Systems etc., we believe that the market is open to disruption.
Bloc Digital offers a mobile inspection application called **Inspector**. Inspector is used to gather degradation data and images of parts in MRO shops and the field to generate a fleet-wide perspective on part performance in service.
This project will create the **InspectorPlus** application, simplifying scanning and creation of 3D digital twins at part serial number level and transforming the ability to extract critical insight from the data.
**\* InspectorPlus** will enable mobile device users to easily gather accurate Inspection data and apply deep learning to create accurate digital twins of each part.
**\* InspectorPlus** will convert data into insights, enabling optimised life cycle design and support policies to be developed, saving up to 30% of total life cycle cost for typical high wear components.
**\* InspectorPlus** will enable organisations to anticipate and avoid the worst impact of degradation through improved design, contain operational disruption through improved inspection planning and recover part usable life through optimised inspection criteria and repair opportunities.
The digital twin will track part condition over time, transforming understanding of how parts degrade with use. Serialised digital twins can be combined virtually to generate insight into how the design standard, product and system are performing.
The project applies an innovative mixture of technologies to create a step-change in the efficiency of creation of part digital-twins. Key innovations include the application of Neural Radiance Fields (NeRFs) to the gathering of data, the application of deep learning to create a parametric digital twins, highlighting the variations in degradation and deformation of the part based on known factors.
Bloc Digital who will lead the project and develop and deliver the production application.
The University of Derby's Department of Computing Science will contribute their extensive knowledge in data analytics and machine learning.
DFS Consulting will join the project as a sub-contractor with their lead Consultant, Andy Harrison, a former Rolls-Royce Engineering Associate Fellow for Service Knowledge Management / Aston University Visiting Professor in Design for Service, providing his deep technical expertise on extracting the maximum service value from the data we gather.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| BLOC DIGITAL LTD | £140,689 | £ 98,482 |
Participant |
||
| UNIVERSITY OF DERBY | £60,005 | £ 60,005 |
People |
ORCID iD |
| Frank McQuade (Project Manager) |