This Ai In Engineering Practice eBook from 3D-LABS is written for engineers and students who want a clear, practical reference they can apply immediately.
AI in Engineering Practice covers machine learning, computer vision, and NLP applied to structural health monitoring, equipment fault diagnosis, design optimisation, and code compliance checking with Python examples and validation methodology.
What standards does this book reference?
ISO/IEC 42001:2023 AI management, IEEE 2857-2021 privacy engineering, EU AI Act 2024 Annex III (high-risk AI in critical infrastructure), and ASME V&V 40-2018 for AI model validation.
What AI validation framework is used?
ASME V&V 40-2018: (1) Code verification – does model implement intended algorithm? (2) Solution verification – output accurate for training distribution? (3) Model validation – agrees with physical experiments within uncertainty bounds? IEC 61508 Part 3 Annex C for SIL 2+ AI.
What AI engineering applications have code examples?
1D-CNN bearing fault diagnosis (>95% accuracy on CWRU dataset), YOLOv8 weld defect detection (mAP@0.5 >0.92), ResNet-50 crack detection, and XGBoost structural load prediction with SHAP explainability. Code: Python 3.11, TensorFlow 2.14, PyTorch 2.1.
How do I access this book after purchase?
eBook PDF: Instant download via email and 3D-LABS dashboard. Paper Book: Printed and shipped to India.
What’s Included
An instant PDF download covering the core concepts, practical examples, and key references — ready to read on any device.
