School of Chemistry and Physics: eBooks

Amidst the dynamic landscape of academic discourse, a pivotal challenge beckons scholars to explore the profound convergence of self-sustaining systems with artificial intelligence (AI) and the Internet of Things (IoT). This convergence is rapidly reshaping our understanding of sustainability, resource management, and operational efficiency across various fields and industries. Picture a world where autonomous systems operate continuously and intelligently, utilizing real-time data to make informed decisions. Such systems have the potential to revolutionize agriculture, urban infrastructure, and industrial automation. This transformation, often termed the Internet of Self-Sustaining Systems (IoSS), is a pivotal topic that demands academic attention and exploration. Addressing this critical issue head-on is The Convergence of Self-Sustaining Systems With AI and IoT , which offers an in-depth examination of this transformative convergence. It serves as a guiding light for academic scholars seeking to unravel the vast potential of self-sustaining systems coupled with AI and IoT. Inside its pages, readers will delve into AI-driven autonomous agriculture, eco-friendly transportation solutions, and intelligent energy management. Moreover, the book explores emerging technologies, security concerns, ethical considerations, and governance frameworks. Join us on this intellectual journey and position yourself at the forefront of the AI and IoT revolution that promises a sustainable, autonomous future.

Injection Molding Process Modelling presents the application of CAE, statistics and AI in defect identification, control, and optimization of injection molding process for quality production. It showcases CAE in determining the optimal placement of injection points, designing cooling channels, and ensuring that the mold will produce parts with the desired specifications. The book illustrates the capability of the CAE tools to simulate molten plastic flow within a mold during the injection molding process. Explaining how the use of CAE, statistical tools and AI enhances efficiency, accuracy, and collaboration, the book explores the contributions to injection molding in product design and visualization; prototyping and testing; mold design; and analysis and simulation. It emphasizes the integration of statistical tools for optimized efficiency and waste reduction, including statistical process control (SPC), Design of Experiments (DOE), Regression Analysis, Capability Indices, Interaction effects, and many more. The book also illustrates the predictive modelling of typical injection molded product defects using intelligent algorithms. The book will interest industry professionals and engineers working in manufacturing, production, automation, and quality control.