Artificial Intelligence Based Solutions For Industrial Applications
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Artificial Intelligence based Solutions for Industrial Applications
Artificial Intelligence based Solutions for Industrial Applications aims to examine the utilization of artificial intelligence (AI) technologies to tackle difficult industrial issues and offers readers a thorough understanding of how these technologies are being employed to address intricate industrial challenges and to stimulate innovation. This book explores the fundamental principles of artificial intelligence (AI) and its practical use in industrial environments. This book improves understanding of core concepts, the present state of the art and real-time implementation of AI in many industrial applications. This book describes the detailed implementation of AI in the industrial sector as well as related case studies for in-depth understanding. Basic concepts, related work reviews, illustrations, empirical results, and tables are integrated within each chapter to give the readers the opportunity to gain maximum knowledge and to easily understand the methodology and results presented. This book introduces a variety of smart algorithms to help in filtering important information and to solve problems in the application domains. Application of machine learning and deep learning in the industry demonstrates the capabilities by which it may be used to solve practical problems in the 'Fourth Industrial Revolution', and it equips readers with the necessary knowledge and tools to design solutions by themselves with the help of theory and practical examples dealt with. The fourth industrial revolution and its consequences on society and organizations are discussed in this book. Features: Detailed understanding of the industrial application of AI. Discussion of core concepts of different machine learning and deep learning techniques such as artificial neural networks, support vector machines, K –nearest neighbour, decision tree, logistic regression, and many more. Detailed study on various industrial applications of machine learning and deep learning in healthcare, education, entertainment, share market, manufacturing, and many more. Case studies on industrial application of AI Summataion of the fourth industrial revolution and its consquences on society and organizations. This book is primarily written for graduate students, engineers, and academic researchers, industrial practitioners, and anyone who wants to optimize production processes, explore AI technology, or stay ahead in the industrial field. It covers the complexities of AI in industrial contexts from core basic understanding to complex implementation.
Artificial Intelligence for Digitising Industry Applications
This book provides in-depth insights into use cases implementing artificial intelligence (AI) applications at the edge. It covers new ideas, concepts, research, and innovation to enable the development and deployment of AI, the industrial internet of things (IIoT), edge computing, and digital twin technologies in industrial environments. The work is based on the research results and activities of the AI4DI project, including an overview of industrial use cases, research, technological innovation, validation, and deployment. This book’s sections build on the research, development, and innovative ideas elaborated for applications in five industries: automotive, semiconductor, industrial machinery, food and beverage, and transportation. The articles included under each of these five industrial sectors discuss AI-based methods, techniques, models, algorithms, and supporting technologies, such as IIoT, edge computing, digital twins, collaborative robots, silicon-born AI circuit concepts, neuromorphic architectures, and augmented intelligence, that are anticipating the development of Industry 5.0. Automotive applications cover use cases addressing AI-based solutions for inbound logistics and assembly process optimisation, autonomous reconfigurable battery systems, virtual AI training platforms for robot learning, autonomous mobile robotic agents, and predictive maintenance for machines on the level of a digital twin. AI-based technologies and applications in the semiconductor manufacturing industry address use cases related to AI-based failure modes and effects analysis assistants, neural networks for predicting critical 3D dimensions in MEMS inertial sensors, machine vision systems developed in the wafer inspection production line, semiconductor wafer fault classifications, automatic inspection of scanning electron microscope cross-section images for technology verification, anomaly detection on wire bond process trace data, and optical inspection. The use cases presented for machinery and industrial equipment industry applications cover topics related to wood machinery, with the perception of the surrounding environment and intelligent robot applications. AI, IIoT, and robotics solutions are highlighted for the food and beverage industry, presenting use cases addressing novel AI-based environmental monitoring; autonomous environment-aware, quality control systems for Champagne production; and production process optimisation and predictive maintenance for soybeans manufacturing. For the transportation sector, the use cases presented cover the mobility-as-a-service development of AI-based fleet management for supporting multimodal transport. This book highlights the significant technological challenges that AI application developments in industrial sectors are facing, presenting several research challenges and open issues that should guide future development for evolution towards an environment-friendly Industry 5.0. The challenges presented for AI-based applications in industrial environments include issues related to complexity, multidisciplinary and heterogeneity, convergence of AI with other technologies, energy consumption and efficiency, knowledge acquisition, reasoning with limited data, fusion of heterogeneous data, availability of reliable data sets, verification, validation, and testing for decision-making processes.
Machine Learning and Artificial Intelligence with Industrial Applications
This book presents the tools used in machine learning (ML) and the benefits of using such tools in facilities. It focus on real life business applications, explaining the most popular algorithms easily and clearly without the use of calculus or matrix/vector algebra. Replete with case studies, this book provides a working knowledge of ML current and future capabilities and the impact it will have on every business. It demonstrates that it is also possible to carry out successful ML and AI projects in any manufacturing plant, even without fully fulfilling the five V (Volume, Velocity, Variety, Veracity and Value) usually associated with big data. This book takes a closer look at how AI and ML are also able to work for industrial area, as well as how you could adapt some of the standard tips and techniques (usually for big data) for your own needs in your SME. Organizations which first understand these tools and know how to use them will benefit at the expense of their rivals.