## 1 Introduction
In this research, the authors focus on the implementation of AR technology as a PSS in the field of Maintenance, Repair, and Training. Maintenance is a widely offered service because it is regarded as a critical activity throughout the lifecycle of a product. Engineers can now identify faults and malfunctions in products more easily and precisely thanks to recent technological advances in information and communication technologies (ICT) [13; 14]. To that end, the three steps listed below can be used towards addressing this problem. To begin, data can be retrieved from Cloud repositories (historical data from previous maintenance procedures). Thereinafter, they will be processed using appropriate algorithms. Finally, they either predict when components will fail (predictive maintenance) [15] or evaluate the health of components (condition-based maintenance). The Industrial Internet of Things (IIoT) allows businesses to provide more innovative solutions [16]. Among these technologies, AR enables the intuitive visualization of virtual information (of any kind, including data, 3D models, audio, and video) on the user's field of view, thus in modern manufacturing environments, the visualization of insightful data (machine health monitoring, machine specifications, assembly instructions, etc.) is enabled [17]. The combination of AR and maintenance has facilitated engineers to design and develop services (realized as mobile applications) for the provision of remote maintenance support (also known as tele-maintenance) [18]. Furthermore, visualizing the assembly steps of components that lead to the final product clarifies the maintenance process of complex industrial equipment [19]. Similarly, Cloud Computing and Cloud Manufacturing open up new opportunities for enterprises [20], as they can transform traditional business models in such a way that stakeholders not only maintain their competitive edge but also ensure their continued existence in the global market. To that end, this research is an extended version of the research paper in [21] and presents an Intelligent Product Service System (IPSS) for adaptive maintenance of Engineered-to-Order industrial equipment based on AR. A key contribution of this research work is to facilitate PSS providers to successfully adapt their shopfloor schedule in order to incorporate the maintenance activities required for the repair of equipment, and simultaneously take into consideration the energy demand for the completion of these tasks. Moreover, it is also imperative to achieve more efficient communication between the shopfloor technicians and the engineering department. In order to test and validate the proposed framework, an industrial case study derived from a mold-making Small-Medium Enterprise (SME) was used. The remainder of the manuscript is organized as follows. In Section 2, the most pertinent literature on the key components of this research work is investigated. Then, in Section 3 the PSS approach and the proposed system architecture are presented and discussed in detail. Then, Section 4 follows a technical discussion about the development of the PSS platform, including details about the software and hardware configuration of this application. Section 5 is dedicated to the description of the industrial case study used for the validation of the developments. Afterwards, in Section 6 a discussion on the validation results is presented. The manuscript is concluded in Section 7, along with the provision of directions for further elaboration.