Künstliche Intelligenz in der Messtechnik

In the age of Industry 4.0 and intelligent automation, precision measurement systems face unprecedented demands in terms of accuracy, adaptability, and real-time performance. Artificial Intelligence in Precision Metrology presents a rigorous and application-oriented exploration of how artificial intelligence (AI) can elevate modern measurement systems beyond conventional limits.

This book bridges theoretical foundations with real-world applications, focusing on the integration of machine learning (ML) and neural networks in metrological systems. It covers essential concepts of measurement science, signal processing, and uncertainty analysis, and then delves deeply into the role of AI in tasks such as object localization, edge detection, anomaly recognition, and system calibration.

A distinctive aspect of this book is its practical orientation: complete neural network models are implemented from scratch in MATLAB, enabling the reader to move seamlessly from theory to practice. Code examples include regression networks, multi-class classifiers, handwritten digit recognition, and 3D spiral classification—all tailored to measurement-related challenges.

Key topics include:

• Fundamentals of measurement systems, signal types, and uncertainty quantification

• Core principles of artificial intelligence and machine learning

• Classical and AI-enhanced edge detection for industrial image analysis

• Hybrid architectures combining conventional algorithms with deep learning

• Robustness to lighting variations, occlusion, and noise in real-world conditions

• Complete implementation of neural networks in MATLAB with annotated source code

• Real-case application: automatic diameter measurement using Sobel filters

This book is intended for researchers, engineers, and graduate students in metrology, robotics, computer vision, and intelligent systems. It is also suitable for professionals seeking to apply AI in high-precision industrial environments. Each method and model is supported by practical examples and a comprehensive GitHub repository.