AIML for Industry4. 2024 : Artificial Intelligence and Machine Learning for Industry 4.0
Call For Papers
About the book:
Intelligent automation is widely considered as the greatest potential of Industry 4.0 innovations for corporations. Artificial intelligence, defined as computational models that simulate behavioral intelligence, is set to unleash the coming era in technological revolution and provide businesses with an edge over its competitors. The significance of AI is not found in its computational models, but in how humans can use them. Industry things are increasingly being upgraded to machines with intelligence that can perceive, act, evolve, and interact in a particular environment. The buzz word "Industry 4.0" refers to the Fourth Industrial Revolution. Manufacturing technologies constitute an aspect of this transformation. Amongst the technologies featured in this class are the Internet of Things (IoT), cyber-physical systems (CPS), and artificial intelligence (AI). The concept of artificial intelligence pertains to a machine's competence to perform human capabilities such as reasoning, learning, and problem solving. Machine intelligence agents are likely to sense and communicate through the environment using sensor technology. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. Consistently observing an equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance anticipates an equipment failure in addition to typical equipment maintenance, which employs a periodic schedule rather than responding to equipment problems. Industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery like machines used to make auto parts. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning. This reveals precisely how AI and ML-based models are becoming more prevalent in various industries for smart functioning and greater productivity. The proposed book aspires to emphasize technological developments that could have a greater influence on an industrial revolution. It also intends to deliver the fundamental technologies offering experts will be responsible for directing the development of innovative firms.
The confluence of Industry 4.0 technologies has led to an ideological shift in which the barriers between physical, electronic means, and biological are increasingly vanishing. The foundation of this technological convergence process, which will lead to the digitization of the business and the community at all the levels, represents a new era associated with hyperconnectivity and interoperability. In an Industry 4.0 context, data produced by sensor networks necessitates machine learning and data analysis tools. Industry 4.0 is enabling industrial facilities to convert into innovative factories by harnessing intelligent technologies. The primary drivers of this information-driven business shift are artificial intelligence and machine learning along with IoT.
Decision-making requiring a vast intake of data and customization in the manufacturing process is something that managers and machines both have to deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets would be necessary. Predictive maintenance is an option, which helps to reduce expenses, maintain control, and improve the efficiency of production although minimizing machine downtime. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. This book fosters contemporary technological advancements in the fields of AI and ML from an industry 4.0 perspective. It also looks at the prospects, obstacles, and potential applications of AI and ML in industry 4.0 research.
The topics are related to but not limited to the following:
1. Introduction to Industry 4.0 and role of AI-ML
2. Era of technologies in an industrial revolution.
3. Technologies driving Industry 4.0
4. Convergence of AI-ML with other technologies for Industry 4.0
5. Architectural framework for Industry 4.0
6. Applications of AI and ML in Industry 4.0
7. AI enabled predictive maintenance
8. AI-ML Empowered Smart Buildings and Factories.
9. AI-ML based Technologies for Process Optimization and Quality Control
10. Human-Robot collaboration and Ergonomics
11. Conceptual framework for Industrial AI
12. Predictive models for retail and distribution: A Case Study
13. Intelligent Predictive models for Smart industries: A Case Study
14. Business intelligence for Industry 4.0
15. Challenges and Opportunities in Industry 4.0
All queries related to the submission should be emailed to firstname.lastname@example.org