Kataiev / Kacprzyk / Zaporozhets Neural Calibration of Coordinate Measuring Arms

1. Auflage 2026
ISBN: 978-3-032-32918-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 96 Seiten

Reihe: Engineering (R0)

This book presents a groundbreaking fusion of classical metrological modelling and modern artificial intelligence to dramatically enhance the accuracy of geometric measurements in industrial environments. Designed for researchers, engineers, and practitioners in precision measurement and power equipment manufacturing, this book offers a comprehensive and rigorously validated methodology for compensating both kinematic and non kinematic errors in coordinate measuring arms (CMAs). At the core of the work is a universal calibration strategy that employs three reference standards within a single procedure. This innovative approach enables the simultaneous assessment of systematic sensing errors, point reproducibility, and linear movement stability—capabilities rarely addressed together in existing calibration methods. Building on this foundation, this book introduces a single point polynomial correction model that achieves a fourfold reduction in residual kinematic errors, offering a practical and efficient enhancement to traditional calibration techniques. This book’s most distinctive contribution lies in its application of artificial neural networks to compensate for destabilising factors and variable measurement conditions that conventional models cannot fully capture. Through the evaluation of 432 neural network configurations, the research identifies an optimal architecture capable of reducing non kinematic residual errors by a factor of six. This extensive experimental validation provides rare empirical depth and actionable insights for the integration of machine learning into industrial metrology. A complete software information system is presented, integrating modules for calibration, data preprocessing, neural network training, filtering, and automated compensation. The system’s architecture is designed for real world deployment in demanding production environments, particularly in the manufacturing of power equipment components where measurement stability is critical. Detailed discussions of Denavit–Hartenberg modelling, synthetic data generation, noise simulation, and dataset partitioning equip the reader with a full methodological toolkit for developing robust AI enhanced measurement systems. By combining theoretical rigour with practical implementation, Neural Calibration of Coordinate Measuring Arms establishes a new paradigm for precision measurement.

Zielgruppe

Research

Autoren/Hrsg.

Kataiev, Denys

Kacprzyk, Janusz

Zaporozhets, Artur

Inhaltsverzeichnis

Modern Approaches to Reducing Kinematic and Non Kinematic Errors in Coordinate Measuring Arms.- Methodology for Reducing Kinematic and Dynamic Errors in Coordinate Measuring Arms.- Software and Information System for Coordinate Measuring Arms with Neural Network Error Compensation.- Algorithm for Generating and Normalising Synthetic Calibration Data for Coordinate Measuring Systems.- Neural Network Calibration of Coordinate Measuring Systems Architecture Hyperparameter Study and Experimental Validation.