The article deals with the automation of the grinding machine duty cycle. There are both described, the problems of the automation of the duty cycle and the present state summarised in this paper. The control systems of grinding cycle run from...

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments.

The aim of the work was carried out research on the causes cracking formed aluminum work pieces in the assortment of rods, which sometimes occur in the mechanical machining (turning, drilling) for alloys of Al-Cu. A typical representative for ...

Alloy modifying is common practice for improving the structure of the material. One of often used material, where the modification is used is AlSi7Mg0.3 alloy. And modification of AlSi7Mg0.3 is often realized by strontium. The paper deals with...

The aim of the performed works was research on the causes of formed aluminum blanks cracking in the range of the AlCuSnBi alloy rods where cracks appear during mechanical machining (turning, drilling) of this range to the final part. AlCuSnBi ...