CLOUD Manufacturing for On-Demand manufacturing sErvices (CLOUD MODE)

The scientific problems dealt with in the CLOUD MODE project proposal are in line with the objectives and action lines of the HORIZON 2020 Work Programme, in particular as concerns the development of the Factories of the Future (FoF) paradigm within the “Industrial leadership” pillar. In this framework, a major focus is related to boosting the industrial deployment of Key Enabling Technologies (KETs), such as ICT-based engineering technologies, through their development and integration for improving industrial productivity and innovation capacity. The specific challenge to be addressed in the CLOUD MODE project is related to the Cloud Manufacturing (CM) paradigm, that is one of the most innovative KETs for modern manufacturing industry. Cloud Manufacturing can be defined as an integrated Cyber-Physical System (CPS) that can provide on-demand manufacturing services digitally and physically to best utilize manufacturing resources. It has been conceived as an extension of the Cloud Computing paradigm to the manufacturing sector: compared with cloud computing, the services that are managed in CM include not only computational and software resources, but also various digital and physical manufacturing resources. According to CM, different users in an industrial environment could have remote access to shared manufacturing resources. Several critical challenges are related to the actual realization of CM: technical issues to be solved include knowledge based resource clouding, virtual representation of resources, development of service composition strategies, collaboration between CM applications, etc. The CLOUD MODE project aims at developing a CM architecture through a tight coupling between computational and physical resources according to the concept of Cyber Physical System, a physical and engineered system whose operations are monitored, coordinated, controlled and integrated by a computing and communication core. The employment of different sensors, realized in this project through a simulation approach, will allow to collect several types of information: machine availability/status (idle, busy, off), power consumption (through power sensors), location of each resource (through GPS), data on the specific product/resource (through RFID), etc. Other historical data could be collected to know the average processing times of each machine, the setup times, the failure rate, and so on. Within the CLOUD MODE project, the information coming from sensors will be used to monitor the status of each resource, and have a mapping of the manufacturing system resources and their current state (availability, location, etc.) that is necessary to allow for sharing manufacturing resources. This method will be used to deal with the challenge related to the dynamic nature of the industrial environment (in particular in the case of small batch manufacturing industry), that should be taken into account to make the best decision making on manufacturing strategies, sharing of resources and so forth. The mapping will be realized through a virtualization of the manufacturing resources, that can be represented by digital models, containing different levels of content, including functionalities, performance, current status, etc. The shared digital data and models available in the cloud should be adaptive, in the sense that they should always represent the current status of the physical manufacturing system [8]. For this reason, they should be continuously updated with the information coming from the physical manufacturing system (e.g. through the use of sensors) and user input (e.g. company management). In this framework, the project activities are aimed at developing methodologies to virtualize and encapsulate manufacturing resources into services in a cloud architecture. The project will also deal with issues related to how these manufacturing resources can be searched and combined to fulfill customer tasks, and how optimal solutions in terms of service composition strategies and production planning can be found. To perform decision making, integration of analytical and simulation-based approaches will be carried out.