Maintenance and its cost continue, over the years, drawing the attention of production management, since the unplanned failures decrease the reliability of the system and the return of investments. Maintenance services of manufactured products are among the most common services in the industry; they account for more than half of the total costs and influence the environmental impact of the product. In order for manufacturers to increase their productivity, by performing accurate and quick maintenance, advanced monitoring systems should be considered in order to easily detect machine tool failures before they occur. Toward that end, a cloud-based platform for condition-based preventive maintenance, supported by a shop-floor monitoring service and an augmented reality (AR) application, is proposed as a product-service system (CARM2-PSS). The proposed AR maintenance service consists of algorithms of automated generation of assembly sequences, part movement scripts, and improved interface that aim to maximize existing knowledge usage while creating vivid AR service instructions. Moreover, the proposed monitoring system is supported by a wireless sensor network (WSN), and is deployed on a Cloud environment together with the AR tool. The monitoring system monitors the status of the machine tools, calculates their remaining operating time between failures (ROTBF), and identifies the available windows of the machine tools in order to perform the AR remote maintenance. In order to validate the proposed methodology and calculate its impact, it is applied in a real-life case study of a white-goods industry.

The way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloud-based approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloud-based platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools.

With the advent of the fourth industrial revolution (Industry 4.0), manufacturing systems are transformed into digital ecosystems. In this transformation, the internet of things (IoT) and other emerging technologies pose a major role. To shift manufacturing companies toward IoT, smart sensor systems are required to connect their resources into the digital world. To address this issue, the proposed work presents a monitoring system for shop-floor control following the IoT paradigm. The proposed monitoring system consists of a data acquisition device (DAQ) capable of capturing quickly and efficiently the data from the machine tools, and transmits these data to a cloud gateway via a wireless sensor topology. The monitored data are transferred to a cloud server for further processing and visualization. The data transmission is performed in two levels, i.e., locally in the shop-floor using a star wireless sensor network (WSN) topology with a microcomputer gateway and from the microcomputer to Cloud using Internet protocols. The developed system follows the loT paradigm in terms of connecting the physical with the cyber world and offering integration capabilities with existing industrial systems. In addition, the open platform communication—unified architecture (OPC-UA) standard is employed to support the connectivity of the proposed monitoring system with other IT tools in an enterprise. The proposed monitoring system is validated in a laboratory as well as in machining and mold-making small and medium-sized enterprises (SMEs).

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Although the market started expressing the need for high product variety three decades ago, manufacturing systems have not kept the pace with these social and economic developments. Modern industrial shop-floors are highly affected by the ever-increasing product variety and volatile market demands introduced by the currently established mass customization paradigm. The increasing complexity and high unpredictability in manufacturing activities require immediate reactions to emerging disturbances, such us reducing bottlenecks, and avoiding brake-downs and idleness of critical equipment in order to increase productivity and therefore, the company's competitiveness. The framework proposed in this research work includes machine monitoring techniques for the near real-time identification of machine status, in order to allow a predictive maintenance engine to diminish machine tool failures. Moreover, an adaptive short-term scheduling mechanism is employed, using monitoring data for the refinement of production schedules based on the current and future conditions of the shop-floor. Data acquired from a multisensory pilot installation together with information directly retrieved from the machine tool's controller are fused and comprise the input to subsequent software modules. The monitoring module is described, which analyses sensory data during the machine operation, as well as the Adaptive Scheduling Engine that dispatches schedules and refines future schedules based on awareness regarding machine availability. The software architecture and hardware setups are described and comprise the roadmap for the framework development. A possible application of the framework is described in the context of a European SME manufacturer.