Hossam Eldin Salem

This article explores the integration of Maintenance 4.0 into HVAC (heating, ventilation, and air conditioning) systems, highlighting its essential role within the framework of Industry 4.0. Maintenance 4.0 utilizes advanced technologies such as artificial intelligence and IoT sensing technologies. It also incorporates sophisticated data management techniques to transform maintenance strategies into HVAC and indoor ventilation systems. These innovations work together to enhance energy efficiency, air quality, and overall system performance. The paper provides an overview of various Maintenance 4.0 frameworks, discussing the role of IoT sensors in real-time monitoring of environmental conditions, equipment health, and energy consumption. It highlights how AI-driven analytics, supported by IoT data, enable predictive maintenance and fault detection. Additionally, the paper identifies key research gaps and challenges that hinder the widespread implementation of Maintenance 4.0, including issues related to data quality, model interpretability, system integration, and scalability. This paper also proposes solutions to address these challenges, such as advanced data management techniques, explainable AI models, robust system integration strategies, and user-centered design approaches. By addressing these research gaps, this paper aims to accelerate the adoption of Maintenance 4.0 in HVAC systems, contributing to more sustainable, efficient, and intelligent built environments.

Production engineering focuses on designing, optimizing, and managing manufacturing processes to produce goods efficiently. Turning is a machining process where a cutting tool removes material from a rotating workpiece to create cylindrical shapes. Key parameters include cutting speed, feed rate, and depth of cut. Surface roughness is a key challenge in turning, impacting product quality. Achieving the desired finish is crucial for tight tolerance and performance. Engineers use optimization techniques to minimize roughness. Advancements in tool materials and technology help address roughness challenges for improved efficiency in turning. Predictive models for surface roughness are vital for optimizing machining processes, ensuring quality, and enhancing performance. They guide decision-making, improve efficiency, and drive innovation in manufacturing. In this paper, 25 machine learning models have been used and optimized to accurately predict the surface roughness of the turning process of five previous studies on Titanium alloy. Resulting in the best performance with the lowest MSE was for the artificial neural network with 3 hidden layers: the first has 5 neurons, the second has 10 neurons and the last one has 5 neurons. The MSEs are 0.053072, 0.555763, 0.059667, 0.051867, and 0.554829 for the five studies, respectively.