Forensic AI platform for aerospace supply chain security

Abstract

The invention relates to a forensic and artificial intelligence (AI) based platform for tracking, verifying, and securing aerospace parts and components across their entire lifecycle. It comprises for

Description

Field of the invention: This invention relates to aerospace supply chain security. Specifically, to systems and methods employing forensic science techniques, AI analytics, and secure digital infrastructure to ensure the authenticity, integrity, and traceability of serialised and non-serialised aircraft components. Background of the Invention: Current aerospace supply chain verification processes heavily rely on physical paperwork and basic digital recordkeeping. These are prone to fraud, manipulation, and loss. Non-serialised parts are particularly vulnerable due to the absence of unique identifiers. Existing blockchain or barcode-based solutions help preserve data integrity but fail to detect sophisticated fraud, such as falsified Authorised Release Certificates (ARCs) or counterfeit parts introduced during Aircraft-on-Ground (AOG) emergencies. High-profile cases, such as the AOG Technics fraud, have demonstrated the ease with which counterfeit parts can penetrate even regulated supply chains. Furthermore, end-of-life verification failures have enabled unsafe components to be recirculated. There is a lack of centralised, verified vendor registries, incomplete digitisation of historical part documentation, and minimal integration between regulatory oversight and operational systems. Summary of the Invention: The proposed platform introduces a multi-layered forensic verification and Al-driven risk detection model. It integrates: • Digital Thread Infrastructure: A tamper-evident chain-of-custody system spanning OEMs, MROs, installers, and end-of-life processors. • Real-Time Data Sharing Protocols: Secure, interoperable channels for stakeholders to exchange compliance and risk alerts. • Lifecycle Oversight: Tracking from part manufacture to disposal, with fraud prevention measures at each stage. • Forensic Scanning: Surface optical fingerprinting and subsurface 3D imaging to detect counterfeit or damaged parts beyond visual inspection. • Accredited Vendor Registry: A centralized database of approved vendors, including identity verification and quality management compliance. • ARC Verification Engine: Automated checks against industry databases to validate key ARC fields and detect anomalies. • End-of-Life Controls: Verification and auditing of scrap / recycling vendors to prevent illicit reuse. Detailed Description: The invention is implemented as a hybrid cloud platform with secure APIs connecting OEM systems, MRO workflows, regulator databases, and third-party forensic tools. 1. Part Entry at OEM: o Each manufactured or refurbished part is scanned using optical fingerprinting to generate a unique surface signature. o Serialised and non-serialised components are registered in the digital thread with georeferenced manufacturing data. 2 Supply Chain Transfer: o Transfers between OEMs, logistics providers, and MROs are logged in realtime. o ARC documentation is digitally attached, verified against a centralized industry database, and encrypted within the chain-of-custody record. 3. MRO and Installation Stage: o Before installation, during maintenance, forensic scans are performed to confirm authenticity. o AI anomaly detection flags discrepancies in usage patterns, provenance, or documentation. 4. Regulatory Oversight: o Regulators access live compliance dashboards showing part traceability, maintenance logs, and certification status. 5. End-of-Life Processing: o Components flagged for retirement are physically verified before destruction or recycling. o Certified recycling vendors log scrap details to the system, preventing unauthorized reuse.

Claims

Independent Claim 1 — Method1. A method for forensic verification of aerospace supply chain components, comprising:o receiving component identification data, including serialised and nonserialised part attributes;o capturing surface and subsurface imagery using optical fingerprinting and 3D X-ray scanning devices;o generating forensic metadata from said imagery, including surface pattern signatures and defect mapping;o verifying the component’s chain-of-custody using a secure, tamper-evident digital ledger;o applying a machine learning model trained on historical supply chain data to detect anomalies in certification, provenance, or operational history;o generating a risk classification score and issuing a real-time alert to relevant stakeholders when the score exceeds a predetermined threshold;o storing verification results in a secure database accessible to authorised regulatory and OEM personnel.Dependent Claims (Method)2. The method of claim 1, wherein the digital ledger comprises a blockchain network implementing W3C Verifiable Credentials.

3. The method of claim 1, wherein optical fingerprinting provides unique surface identifiers for non-serialised parts.

4. The method of claim 1, wherein subsurface imagery is captured using a portable 3D X-ray scanner with resolution under 5pm.

5. The method of claim 1, wherein the machine learning model is a hybrid supervised-unsupervised model capable of identifying both known and unknown fraud patterns.

6. The method of claim 1, further comprising automated compliance checks against aviation regulatory standards including CAA CAP 747, EASA Part 21G, and BS 10754.Independent Claim 2 — System7. A forensic verification system for aerospace supply chain components, comprising: o one or more imaging devices configured to capture surface and subsurface imagery of a component;o a processor configured to generate forensic metadata from said imagery;o a secure data storage system implementing a tamper-evident digital ledger;o a machine learning engine configured to analyse component data and forensic metadata to detect anomalies;o a risk scoring module configured to generate a risk classification score;o an alerting interface configured to notify authorised stakeholders in real-time of verification results.Dependent Claims (System)8. The system of claim 7, wherein the imaging devices comprise optical fingerprinting modules and 3D X-ray scanners.

9. The system of claim 7, wherein the digital ledger is implemented using blockchain with SHA-256 hashing.

10. The system of claim 7, wherein the machine learning engine is hosted in a secure cloud computing environment compliant with ISO 27001.

11. The system of claim 7, wherein the alerting interface is integrated into an OEM maintenance, repair, and overhaul (MRO) dashboard.Independent Claim 3 — Computer-Readable Medium12. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:• Receive aerospace component identification data;• Capture and process forensic imagery from optical and X-ray sources;• Generate forensic metadata and store it in a tamper-evident ledger;• Execute a machine learning model to analyse said metadata and detect anomalies;• Generate a risk score and trigger real-time alerts when anomalies are detected.Dependent Claims (Computer-Readable Medium)13. The computer-readable medium of claim 12, wherein the instructions further cause automated compliance verification against regulatory standards.

14. The computer-readable medium of claim 12, wherein forensic imagery is pre-processed using Ai-enhanced image recognition for improved defect detection.

15. The computer-readable medium of claim 12, wherein the real-time alerts are distributed via both secure web dashboards and encrypted mobile applications.AMENDMENTS TO THE CLAIMS HAVE BEEN FILED AS FOLLOWS:-26 08 25ClaimsIndependent Claim 1 — MethodA method for forensic verification of aerospace supply chain components, comprising:• receiving component identification data, including serialised and non-serialised part attributes;• capturing surface and subsurface imagery using optical fingerprinting and 3D X-ray scanning devices;• acquiring in-process sensor telemetry generated during manufacture or refurbishment, the telemetry including one or more of temperature, pressure, vacuum level, humidity, vibration, acoustic emission, fibre-Bragg-grating strain, deposition or cure parameters;• generating forensic metadata from said imagery and telemetry, including surface pattern signatures, defect mappings, and a process-signature profile;• verifying the component’s chain-of-custody using a secure, tamper-evident digital ledger;• applying a machine-learning model trained on historical supply chain data to detect anomalies in certification, provenance, process-signature compliance, or operational history;• further verifying, during refurbishment or reinstallation, that aerospace propulsion and aerodynamic structure components critical to net-zero aviation targets are authentic, compliant, and free from counterfeiting;• ensuring only regulator-approved refurbished components are reintroduced into the supply chain;• generating a risk classification score and issuing a real-time alert to relevant stakeholders when the score exceeds a predetermined threshold; and• storing verification results in a secure database accessible to authorised regulatory and OEM personnel.Dependent Claims (Method)2. The method of claim 1, wherein the digital ledger comprises a blockchain network implementing W3C Verifiable Credentials.

3. The method of claim 1, wherein optical fingerprinting provides unique surface identifiers for non-serialised parts.

4. The method of claim 1, wherein subsurface imagery is captured using a portable 3D X-ray scanner with resolution under 5 pm.

5. The method of claim 1, wherein the machine-learning model is a hybrid supervised-unsupervised model capable of identifying both known and unknown fraud patterns.

6. The method of claim 1, further comprising automated compliance checks against aviation regulatory standards including CAA CAP 747, EASA Part 21G, and BS 10754.

7. The method of claim 1, wherein refurbishment verification further comprises assessing environmental lifecycle impacts, reducing waste, and supporting net-zero aviation objectives.26 08 258. The method of claim 1, wherein the process-signature profile is compared to an OEM- or regulator-defined reference envelope, and non-conformity blocks installation or triggers escalation.

9. The method of claim 1, wherein the in-process telemetry is cryptographically hashed and time-stamped and bound to a unique part identifier within the digital thread.

10. The method of claim 1, wherein acoustic emission and fibre-Bragg-grating signals are analysed to infer cure state, delamination onset, or residual stress as part of the anomaly detection.Independent Claim 2 — SystemA forensic verification system for aerospace supply chain components, comprising:• one or more imaging devices configured to capture surface and subsurface imagery of a component;• a sensor interface configured to ingest in-process telemetry from intelligent tooling and refurbishment equipment;• a processor configured to generate forensic metadata from said imagery and telemetry, including a process-signature profile;• a secure data storage system implementing a tamper-evident digital ledger;• a machine-learning engine configured to analyse component data, imagery, and telemetry to detect anomalies;• a refurbishment verification module configured to validate regulator-approved refurbishment and reinstallation of aerospace propulsion technologies and aerodynamic structures critical to net-zero aviation;• a risk scoring module configured to generate a risk classification score; and• an alerting interface configured to notify authorised stakeholders in real time of verification results.Dependent Claims (System)11. The system of claim 2, wherein the imaging devices comprise optical fingerprinting modules and 3D X-ray scanners.

12. The system of claim 2, wherein the digital ledger is implemented using blockchain with SHA-256 hashing.

13. The system of claim 2, wherein the machine-learning engine is hosted in a secure cloud computing environment compliant with ISO 27001.

14. The system of claim 2, wherein the alerting interface is integrated into an OEM maintenance, repair, and overhaul (MRO) dashboard.

15. The system of claim 2, wherein the sensor interface supports inputs from autoclaves, ovens, automated deposition machines, and tool-embedded sensors including temperature, pressure, vacuum, humidity, vibration, acoustic emission, and fibre-Bragg-grating strain.

16. The system of claim 2, further comprising a data-fusion engine configured to compute a process-signature match score by correlating sensor telemetry with surface / subsurface NDE results and documentation.Independent Claim 3 — Computer-Readable MediumA non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:• receive aerospace component identification data;• capture and process forensic imagery from optical and X-ray sources;• ingest in-process sensor telemetry from manufacturing or refurbishment equipment and generate a process-signature profile;• generate forensic metadata and store it in a tamper-evident ledger;• execute a machine-learning model to analyse said metadata and detect anomalies, including process-signature deviations;• verify refurbishment inspection and approval records for propulsion and aerodynamic structure components critical to net-zero aviation targets;• prevent unapproved refurbished components from re-entering the supply chain; and• generate a risk score and trigger real-time alerts when anomalies are detected.Dependent Claims (Computer-Readable Medium)17. The computer-readable medium of claim 3, wherein the instructions further cause automated compliance verification against regulatory standards.

18. The computer-readable medium of claim 3, wherein forensic imagery is pre-processed using Ai-enhanced image recognition for improved defect detection.

19. The computer-readable medium of claim 3, wherein the real-time alerts are distributed via both secure web dashboards and encrypted mobile applications.

20. The computer-readable medium of claim 3, wherein the process-signature profile is cryptographically bound to the part’s unique identifier and exposed via an API for regulator verification and environmental reporting.26 08 25

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