AI-First Digital Engineering that ships.
AI-First Digital Engineering that ships.
AI-First Digital Engineering that ships.
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
Client
Leading Oil & Gas Company
Industry
Aerospace · Manufacturing
TAGS
Computer Vision, Deep Learning, Edge AI
Technologies
Custom CNN, PyTorch, OpenCV, NVIDIA Edge Compute, Python, REST API, MRO System Integration
Engagement
Forge™️
Client Overview
A MRO unit handling V2500 aircraft engine maintenance. Every 13–15 months a "C" check is performed — a detailed inspection of 4,000+ individual mechanical fasteners for structural and surface defects.
Results:
-60%
Inspection time per overhaul cycle
Zero
Missed structural defects in post-deployment audit
4,000+
Components inspected per engine cycle
The Challenge
Manual visual inspection of 4,000+ fasteners was slow, inconsistent, and error-prone. Two failure categories dominated: missing defects and false positives. Fatigue-driven error rates were unacceptable for aviation. The client needed sub-millimetre defect detection at speeds human inspection couldn't match.
The Solution
Palpx designed a full Automated Visual Inspection (AVI) system using custom-trained convolutional neural networks. The pipeline included a feeding system, optical capture, AI inference, and sorting. It utilized high-res cameras, NVIDIA edge compute, and an automated conveyor. It operates without cloud dependency directly on the shop floor.
Client
Leading Oil & Gas Company
Industry
Aerospace · Manufacturing
TAGS
Computer Vision, Deep Learning, Edge AI
Technologies
Custom CNN, PyTorch, OpenCV, NVIDIA Edge Compute, Python, REST API, MRO System Integration
Engagement
Forge™️
Client Overview
A MRO unit handling V2500 aircraft engine maintenance. Every 13–15 months a "C" check is performed — a detailed inspection of 4,000+ individual mechanical fasteners for structural and surface defects.
Results:
-60%
Inspection time per overhaul cycle
Zero
Missed structural defects in post-deployment audit
4,000+
Components inspected per engine cycle
The Challenge
Manual visual inspection of 4,000+ fasteners was slow, inconsistent, and error-prone. Two failure categories dominated: missing defects and false positives. Fatigue-driven error rates were unacceptable for aviation. The client needed sub-millimetre defect detection at speeds human inspection couldn't match.
The Solution
Palpx designed a full Automated Visual Inspection (AVI) system using custom-trained convolutional neural networks. The pipeline included a feeding system, optical capture, AI inference, and sorting. It utilized high-res cameras, NVIDIA edge compute, and an automated conveyor. It operates without cloud dependency directly on the shop floor.
AI-Powered Automated Visual Inspection for Aircraft Engine MRO
Tech Stack
Custom CNN, PyTorch, OpenCV, NVIDIA Edge Compute, Python, REST API, MRO System Integration
Outsourced Services
DevOps and Cloud
Custom Software Development
AI and ML Development
Client
Leading Oil & Gas Company
Industry
Aerospace · Manufacturing
TAGS
Computer Vision, Deep Learning, Edge AI
Technologies
Custom CNN, PyTorch, OpenCV, NVIDIA Edge Compute, Python, REST API, MRO System Integration
Engagement
Forge™️
Client Overview
A MRO unit handling V2500 aircraft engine maintenance. Every 13–15 months a "C" check is performed — a detailed inspection of 4,000+ individual mechanical fasteners for structural and surface defects.
Results:
-60%
Inspection time per overhaul cycle
Zero
Missed structural defects in post-deployment audit
4,000+
Components inspected per engine cycle
The Challenge
Manual visual inspection of 4,000+ fasteners was slow, inconsistent, and error-prone. Two failure categories dominated: missing defects and false positives. Fatigue-driven error rates were unacceptable for aviation. The client needed sub-millimetre defect detection at speeds human inspection couldn't match.
The Solution
Palpx designed a full Automated Visual Inspection (AVI) system using custom-trained convolutional neural networks. The pipeline included a feeding system, optical capture, AI inference, and sorting. It utilized high-res cameras, NVIDIA edge compute, and an automated conveyor. It operates without cloud dependency directly on the shop floor.
