## 1 Introduction  
Equipment maintenance is one of the key elements consisting of manufacturing systems. In their research work, [PERSON] et al. in Reference [1] highlight the importance equipment maintenance as part of the production lifecycle, which reaches 60%-70% of the total production cost. Therefore, being able to forecast machine maintenance operations and perform them in a short time period, can lead to successful troubleshooting and simultaneously increase machine tools availability. Additionally, since the replacement of damaged components can be as high as 70% of the total maintenance cost [1], it is one of the top priorities among manufacturing firms to discover alternative policies for cutting maintenance costs as a means of increasing their revenues. As a result, the overall production performance is optimized [2]. Besides the failure forecast, a major issue identified is the completion of accurate and error-free maintenance operations, and, thus, the reassurance of fully functional machines in the earliest time possible. Based on that concept, a considerable amount of research effort has been put on the design and development of real-time maintenance support tools and applications based on mobile devices to prevent unnecessary errors from happening [3; 4; 5; 6]. With the evolution of Information and Communication Technologies (ICT) as well as the massive break out of augmented reality (AR), the research community extended that approach by exploiting the advantages of AR for data projection during the maintenance operations [7; 8; 9; 10; 11]. Generally, under the framework of Industry 4.0 in an attempt to bridge the physical world with virtual worlds, new technologies have emerged, such as Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) [12]. These digital technologies lie under the umbrella term Extended Reality (XR). The term XR is often used by practitioners and scientists in order to describe the three immersive technologies mentioned above [13]. However, most of the research works available on the web cover basic aspects of Maintenance and Repair Operations (MRO) and provide flexible and accurate troubleshooting [3; 4; 5; 6]. The constant innovation of cutting-edge technologies unveils new opportunities for remote maintenance support. Therefore, there is an apparent need for the development of real-time AR frameworks for the support of MRO. More specifically, the proposed research deals with the design and development of a novel framework for real-time remote maintenance support based on AR. Inspired by the concepts of Cloud manufacturing and Product Service Systems (PSS), the framework was built in a cloud-enabled environment aimed at bridging the different working groups involved in the production lifecycle. Considering that companies seek increased operational availability, also known as availability (A) of their machines, engineers are constantly developing new maintenance policies that can ensure decreased Mean Time To Repair (\(MTTR\)) and increased Actual Machining Time (AMT). The following formula reflects the correlation between the above-mentioned variables and characteristics of actual machines. \[ROTBF=MTBF-AMT \tag{1}\]  
where \(ROTBF\) is the Remaining Operating Time Between Failures. \(MTBF\) is the Mean Time Between Failures. \(AMT\) is the Actual Machining Time. \[A=\frac{MTBF}{MTTR+MTBF} \tag{2}\]  
where \(A\) is the machine availability. \(MTTR\) is the Mean Time To Repair. From Equation (2), it is clear that, in order to increase the operational availability of the machine, represented by \(A\) in Equation (2), there are two possible alternatives. The first alternative is to increase the numerator, i.e., the \(MTBF\), and the second alternative is to decrease the denominator, i.e., \((MTTR+MTBF)\). However, under the scope of this research work, the aim is to develop a framework that will facilitate the maintenance process of machine tools and, by extension, it will result in the minimization of \(MTTR\).