IJCA Vol 4 i1 2025 webmag - Flipbook - Page 45
2025 | Volume 4, Issue 1
Appendix A. Sample AI-Enabled Digital
Calibration Report
45
Technician and AI Veri昀椀cation
• Calibration Performed By:
This sample report illustrates how arti昀椀cial
intelligence enhances calibration practices by
incorporating predictive analytics, self-learning
diagnostics, blockchain security, and risk-based
conformity assessment. It follows ISO 17025
standards and includes technician-AI collaboration to
improve traceability and reliability.
Report Details
Technician Name: John Doe
AI Assistant: MetrologyAI v4.0
Signature: (Digital Signature Attached)
ISO 17025 Accreditation No: 456789-MTL
• Calibration Valid Until: 2026-03-05
Comments
This instrument complies with all relevant metrology
and conformity assessment regulations. AI analysis
suggests monitoring for pressure drift and adjusting
calibration frequency accordingly.
Report No: DCR-2025-001
Issued by: Metrology Laboratory
Date of Calibration: 2025-03-05
The example mirrors the format of traditional
calibration reports while incorporating advanced
AI-enabled features. These include predictive
maintenance, enhanced traceability, and blockchainbacked authenticity. Together, they demonstrate how
Digital Calibration Reports can align with ISO 17025
standards while improving e昀케ciency and accuracy in
conformity assessment.
Customer: Boeing Aerospace
Instrument Type: Digital Pressure Sensor
Instrument Model: DPS-5000
Serial Number: SN-987654321
Calibration Location: Metrology Lab, Los Angeles,
California, USA
Measurement Results
Measurement
Parameter
Reference
Standard Used
Measured
Value
Uncertainty (±
U, 95% CI)
Pass/
Fail
Pressure
(100 kPa)
NIST-Traceable
Gauge
99.98 kPa
± 0.02 kPa
Pass
Pressure
(500 kPa)
NIST-Traceable
Gauge
499.92 kPa
± 0.03 kPa
Pass
Pressure
(1000 kPa)
NIST-Traceable
Gauge
999.88 kPa
± 0.05 kPa
Pass
Calibration Summary
• AI-Enabled Predictive Analysis: Calibration data
indicates that the instrument is within acceptable
limits but may require recalibration in 8 months
instead of the standard 12-month cycle due to wear
trends detected by AI analysis.
• Self-Learning Model Output: AI detected minor
drift tendencies, recommending adjustments for
optimized sensor stability.
• Risk-Based Conformity Assessment: AI-driven risk
assessment determined a low probability (0.5%) of
incorrect measurements within the next calibration
cycle.
• Blockchain Security Integration: This report is
digitally signed and stored on a secure blockchain
ledger to ensure authenticity and prevent tampering.
Appendix B. Examples of AI Applications in
Metrology
Arti昀椀cial intelligence (AI) is being applied across
various metrology contexts to improve measurement
accuracy, automate inspections, and anticipate
maintenance needs. Below are selected examples
of AI-driven tools and systems currently enhancing
metrology in multiple industries:
• AI-Driven Quality Control Systems: In
manufacturing, AI-powered vision systems detect
defects in real-time. For example, BMW utilizes AIdriven vision inspection on its assembly lines to
reduce defects and improve production e昀케ciency
(source: qualitymag.com).
• Virtual Metrology Tools: In the semiconductor
industry, virtual metrology uses AI algorithms to
predict process outcomes based on equipment
sensor data, reducing reliance on direct
measurement. This approach improves e昀케ciency
and enables real-time process adjustments (source:
semiengineering.com).
• AI-Enhanced Scanning Probe Microscopy:
Advanced scanning probe microscopes integrated
with AI can autonomously perform atomic-scale
measurements and manipulate atomic positions
with high precision. These systems adapt to surface
