Methods and Applications of Artificial Intelligence E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: ISTE Invoiced
• Sprache: Englisch

Seitenzahl: 319

Veröffentlichungsjahr: 2025

Beschreibung

Artificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation.

Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs.

In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc.

Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.

Leseprobe

Artificial Intelligence in Mechanics Set

coordinated byAbdelkhalak El Hami

Volume 2

Methods and Applications of Artificial Intelligence

Dynamic Response, Learning, Random Forest, Linear Regression, Interoperability, Additive Manufacturing and Mechatronics

Edited by

Abdelkhalak El Hami

First published 2025 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK

www.iste.co.uk

John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA

www.wiley.com

© ISTE Ltd 2025The rights of Abdelkhalak El Hami to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.

Library of Congress Control Number: 2024949558

British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-999-0

Preface

Artificial intelligence (AI) is currently one of the most discussed technologies, by both scientists and public media. Several factors have contributed to its development throughout the years. The first one is access to a large amount of data. In the industrial domain, it is the advent of Industry 4.0 that has promoted automation and data sharing in several technologies. An additional factor is the continuous enhancement of computational power due to the development of increasingly high-performance processors and algorithm optimization. These two limitations being removed, most developments in AI focus on the quality of predictions. Integrating AI in the industrial field is a new and exciting frontier for innovation.

Similar to the transformation brought about by AI in many other sectors, its application to technologies in mechanics offers the possibility to significantly improve design, manufacturing and quality control processes; from computer aided design (CAD) to the optimization of printing parameters, passing through fault detection and real-time monitoring. This type of technology requires information systems, data with management systems and advanced algorithms that can be used by AI.

In mechanics, AI offers several possibilities in engineering industry, predictive maintenance, monitoring of facilities, robotics, additive manufacturing, materials, vibrations, etc.

This book focuses on Methods and Applications of Artificial Intelligence in mechanics. It is organized into eight chapters.

The first chapter presents the dynamic response of a thermoplastic composite (PP-GF70) using an intelligent method. It uses Crashworthiness to redimension the size of future vehicles, by integrating composite materials lighter than steel that have a good mechanical and dynamic behavior. The work conducted in this chapter involves the comparison of the results of size-impact between an all-steel body and a body integrating a thermoplastic composite (PP-GF70). This material represents an innovative solution in several industries, thanks to these mechanical and thermal advantages and also to its manufacturing process. In the automotive industry, the race to save energy and to reduce pollutant gas emissions attracts an increasingly high interest of builders and equipment manufacturers for the use of low-density materials.

Chapter 2 takes a close look at the opportunities and challenges associated with AI integration into AM (Additive Manufacturing). It explores the various potential applications of AI in AM, such as topology optimization, prediction of printed part performances and automated management of manufacturing processes. Furthermore, we discuss the ethical considerations and safety implications of using AI in this context. This chapter explores the convergence of two dynamic domains: AI and AM, in order to address a critical issue of manufacturing. While AM evolves, it is increasingly clear that AI may play an essential role in the optimization and improvement of this manufacturing process. The objective is to explore the possibilities offered by AI in the context of additive manufacturing, by highlighting the potential advantages, the challenges and the research paths to be explored. Through this examination, this chapter aims to provide stimulating perspectives for the researchers and industry professionals, while contributing to a deeper understanding of the synergies between AI and AM.

Chapter 3 focuses on the reliability of electronic devices depending on the welded joints that connect the electronic components to the printed circuit boards. It is particularly essential for BGAs (Ball Grid Arrays), which integrate multiple contacts in a limited zone. It is in this context that this work has been conducted to evaluate the use of the design optimization method based on reliability and AI for optimization applications. It pairs the finite element model developed by the Ansys APDL software and the design optimization model based on the reliability coded with MATLAB software.

Chapter 4 presents the cooperative intelligent transportation systems (C-ITS) based on communications and sharing of information between vehicles, road infrastructures and communication infrastructure to improve users’ safety, facilitate their travel and guarantee the safety of road workers. This chapter presents the technological advances in the field of ITS, particularly the democratization of the communication between vehicle and infrastructure using ITS-G5. A new electronic toll collection service based on this type of communication is presented. Its objective is to reduce maintenance costs, increase agents’ safety and improve traffic fluidity.

The objective of Chapter 5 is to propose an AI methodology for machine learning. It is used to help machines learn from data and make autonomous decisions. The random forest is an innovative approach in the field of machine learning, offering a robust and multi-purpose method for classification and regression. This exploration serves to discover the theoretical basis of this technique, its practical applications and the challenges associated with its deployment. In-depth comprehension of AI combined with a detailed examination of random forest prepares us for venturing into a new field in which human–machine boundary is blurred, opening the way to new perspectives and wide possibilities.

Chapter 6 is dedicated to the interaction between AI and linear regression. This symbiotic relation is a valuable asset for the AI arsenal, allowing the use of advanced techniques, such as the polynomial regression, ridge regression and LASSO to overcome the limitations of simple linear regression, while preserving its fundamental philosophy of clear interpretation of data. This chapter explores in detail the linear regression, its operation and practical application in the context of AI.

Chapter 7 is dedicated to the application of the XGBoost machine learning algorithm to a practical case study focused on the prediction of machine failures. It starts by describing the dataset used, by providing details on the key characteristics and the target variable related to machine failure indicators. The XGBoost algorithm implements predictive modeling, by refining the hyperparameters to optimize performances using cross-validation techniques. It evaluates model performances using measures such as accuracy, precision, return and ROC curve.

Chapter 8 presents an approach dedicated to the evaluation of the structural operability of intelligent systems of systems (SoS). The proposed evaluation of structural operability can be used in several ways to enhance the performance and efficient operability of the intelligent SoS. The user interface layer may provide users with operability indicators that facilitate the monitoring and understanding of the current state of the system and making informed decisions. By integrating these metrics proposed in the algorithms of analysis and intelligence of the data collection, analysis and intelligence layers, the latter can identify the potential barriers to interoperability and efficient communication between CS, thus anticipating potential problems and taking preventive measures to solve them. The evaluation of structural operability can be integrated at each layer of the architecture of intelligent SoS to provide a continuous evaluation of the system operability. This facilitates proactive decision-making and real-time adjustments that guarantee the optimal operation of the intelligent SoS, despite the potential challenges related to CS interoperability.

Finally, this book is dedicated to methods and application of AI in mechanics. Each chapter contains a clear presentation of the techniques used and developed, accompanied by illustrative examples. This book is addressed to students and is also a valuable aid to engineers and academic researchers.

Abdelkhalak EL HAMI

November 2024