According to Gartner, the term “Big Data” determines a high volume of information, high speed, and / or great variety of information assets that require new forms of treatment to allow better decision-making processes, cognitive intuition and optimization of business processes. Big Data usually includes data sets with dimensions well beyond the capacity of the software tools usually used to acquire, process and manage data in an acceptable time. Moreover, the data sources are mostly outside the company reality, involving every possible archive accessible on the Internet. In the Smart Factory environment, the predictive production systems used operate in a Big Data environment that enables self-awareness of machines and systems, integrating with the intelligent sensors and actuators of the IoT.

The term Blockchain is used to describe a shared and immutable data structure, defined as a “digital register” whose entries are grouped in chronologically linked blocks whose integrity is guaranteed by the use of cryptography. The data transaction activities, both summary and detailed, are based on distributed digital technology, called DLT (Distributed Ledger Technology), as they are recorded simultaneously in multiple nodes of the network. Unlike traditional databases, distributed logs do not have central administration or data store functionality.

Traditionally, cloud computing provides, on specific user demand, both data and processing resources shared by computers and other devices through distributed computing platforms available on the Internet. In the future scenario of the Internet of Things, the most advanced Fog computing is proposed as technology and service designed to allow proximity calculation to provide new applications and better performance especially for latency-sensitive services: in the future, scenarios are emerging in which a large number of ubiquitous and heterogeneous devices, mainly wireless and autonomous, will be able to communicate and cooperate, among themselves and with the network, to perform memorization and processing activities in a decentralized manner without the intervention of third parties.

A cyber-physical or cyber-physical system (CPS) is a computer system capable of continuously interacting with the physical system in which it operates. The system is composed of physical elements each with a computational capacity and closely combines the so-called “three Cs”: computational capacity, communication and control capacity. The artificial structures of calculation and communication, represented by the prefix “ciber”, form a distributed system that interacts directly and dynamically with the real world that surrounds them. At the base of the system, the single element is the embedded device. Among the possible applications: smart grid, intelligent traffic control, home automation, cooperating robots, telecommunications, motoring, avionics, smart factories (called Industry 4.0).

Cyber ​​Security is the set of technologies, processes and good practices aimed at protecting networks, computers, programs and data against attacks, damage or unauthorized access. In an IT context, security requires coordinated efforts across the entire information system. The elements of information security are: application security, information security, network security, disaster recovery (business continuity planning) and training for end users. One of the most problematic elements of security lies in the rapid and unpredictable evolution of cyber threats. The traditional approach of concentrating most of the resources on the most critical system components to protect them against the major known threats is considered insufficient in the context of the factory of the future, as the threats advance faster than the reaction capabilities: approaches are therefore needed more proactive, adaptive and global.

Edge computing is a distributed model in which processing takes place close to the physical location where data is collected and analyzed, rather than on a centralized server or in the cloud. This new infrastructure includes sensors for data collection and perimeter servers for the secure processing of data in real time and locally, as well as the connection to the network of other devices such as laptops and smartphones.

An integrated system is an aggregate of often heterogeneous subsystems, cooperating with each other to offer the general functionalities foreseen by the project. The traditional discipline of Systems Integration also provides for adding value to the system to bring out new features made possible by virtue of the interactions between the subsystems. In an intelligent factory, integration aims to connect applications, hardware and software, in a single organization that works together to simplify and automate processes as much as possible, while avoiding making radical changes to existing applications or data structures that improving processes in business continuity (“on the fly”). According to Gartner, Systems Integration is the “sharing without restriction of business processes and data between any application and data source connected in the company”.

Artificial Intelligence (A.I.) is one of the technological solutions applied at the service of the industry that offers customized solutions with better quality, more flexible, rapid, efficient and economical. The A.I. collects and processes data through edge computing, offering better functionality with complete data control. Edge devices enable onboard data processing while maintaining cloud connectivity for centralized software updates and data analysis across multiple locations.
The application to robotics guarantees the robot to recognize objects and related classes of belonging, not known a priori, thanks to a prior training of the neural network, which collects and exploits all the related experience.

With the expression “Internet of objects” a scenario is envisaged in which in the Internet, in addition to programs and human beings, there is the interconnection of physical devices of any nature and size, both fixed and mobile, equipped with electronics, software , sensors, actuators and interfaces for network connection that allow the collection and distribution of data. According to the IoT paradigm, each physical object is uniquely identifiable, has its own “embedded” operating system and is able to interoperate within the existing Internet infrastructure. In the intelligent factory, IoT is important to enable the “product centrality” paradigm, understood as: a) role exchange between machine and product (each component of the product in the production process has its own identity and instructs the machines on this that must be done on the component itself – production – and on where it must be shipped – logistics); b) role exchange between process and product (the awareness and responsibility of production moves from the production process to the product itself).

Augmented reality is a live view, direct or indirect, of a real environment in the physical world whose elements are increased (or integrated) by sensory inputs generated by a computer, such as audio, video, graphics or data from positioning (GPS). “Augmentation” is conventionally understood in real time and in a semantic context, with environmental elements, such as sports results on TV during a football match. With the help of advanced AR technology (for example with the addition of object recognition through “computer vision”) the information relating to the real world surrounding the user becomes interactive and can be digitally manipulated. Information on the environment and its objects are superimposed on the real world without apparent suture.

TRL level indicates the technology readiness level of a research or solution.

These are the references based on the document Annex G of the General Annexes which is part of the European Commission’s 2018-2020 work program:

• TRL 1 – basic principles observed
• TRL 2 – technology concept formulated
• TRL 3 – experimental proof of concept
• TRL 4 – technology validated in lab
• TRL 5 – technology validated in relevant environment (industrially relevant environment in the case of key enabling technologies)
• TRL 6 – technology demonstrated in relevant environment (industrially relevant environment in the case of key enabling technologies)
• TRL 7 – system prototype demonstration in operational environment
• TRL 8 – system complete and qualified
• TRL 9 – effective system tested in the operating environment (competitive production in the case of enabling technologies, ready for marketing, or usable in space)

In the commonly understood meaning of the term, simulation is the reproduction and tracking over time of the functioning of a process or system present in the real world that requires the development of a mathematical model. In the vision of the CPPS systems, the simulation creates a digital image of all the processing phases of the production process to be transferred in a parallel virtual world; after executing the process, it moves back to the real world. This form of simulation is also called cyber-physical equivalence: the virtual world and the physical product production environment are synchronized, often with real-time requirements. A CPE (cyber physical equivalent) is a virtual replica of a CPPS synchronized with the real scene in every geometric, functional and behavioral aspect. Fast 3D acquisition devices are used to acquire moving objects; Fast processing of information in the virtual environment can facilitate planning operations. The resulting virtual 3D scene can be augmented with digital objects to check for possible collisions between existing parts and those to be added.

Artificial neural networks are mathematical models composed of artificial neurons that are inspired by the biological functioning of the human brain. They are indispensable for solving Artificial Intelligence engineering problems and require advanced supporting hardware chips.

An autonomous robot is a robotic system with a high degree of self-sufficiency; that is, it is a robot (or a fleet of robots) that has behaviors or performs tasks independently. At the current state of development of technologies the degree of autonomy of robots in the factory is confined by the characteristics of the direct environment, without the typical flexibility of human agents. Furthermore, the work environment in the factory is demanding and often chaotic and unpredictable conditions can emerge that put the robot with limited intelligence and autonomy out of play. A goal is therefore to allow the robot to cope with changes in the environment in which it operates, be it on the ground, in water, in the air or in space. A completely autonomous robot (or a fleet of robots) will have to assume real-time information on the environment without human intervention, will have to react to unexpected and sudden situations, avoid dangerous situations, perform missions even in the presence of unexpected variables, etc. An autonomous robot can learn and acquire new knowledge, adapting its behaviors and performing its tasks in a constantly evolving environment.

The ability to share spaces with humans is given to collaborative robots by the presence of sophisticated safety devices, visors and “sensitive” anti-collision systems, capable of constantly monitoring what is happening around them, coordinating their movements with those of humans and exerting a refined control over its actions (at the maximum speed of 1 m/sec) and over the applied forces. Normally cobots are anthropomorphic robots (not necessarily humanoid) equipped with articulated and flexible arms, vision systems and sensors, which make them capable of different degrees of autonomy and extremely refined manipulation and intervention functions.

Analyzes and defines the technological principles and product architecture to meet the required requirements. It also makes use of simulation tools and virtual modeling. It identifies materials and possible production technologies, also evaluating the opportunities offered by Additive Manufacturing. Collects and analyzes data during the prototype production phase, also through the enhancement of Internet of Things solutions and the use of Data Analytics. Collaborates in the development of Internet of Things solutions and Data Analytics systems functional to improve predictive analyzes and any adjustments to project specifications.

He knows the digital platforms for the management of logistic flows, the potentialities linked to the developments of the Internet of Things and Big Data, the management of fully automated warehouses and the solutions for tracking lots / products. Can use handling and storage techniques that make use of Automated Pickers and Vehicles, Advanced Human Machine Interface devices. He supervises the correct functioning and use of the Warehouse Management System for the registration and control of the movements of goods in the warehouse, ensuring consistency between physical and information flows. It manages the information systems of integrated interface with suppliers and customers.

Knows the architecture of the automation system to be implemented, the technologies, devices and control and regulation circuits that characterize it. Knows the technical characteristics and methods of use of Augmented Reality and the advanced solutions available in the field of industrial automation (for example collaborative robots). It is able to supervise operators in the equipment of machines or production plants that use advanced human-machine interface devices and / or collaborative robots. It provides information to operators to optimize the management of machines or systems, also through the use of advanced human machine interface (HMI) devices. It diagnoses and identifies the causes of failure, possibly also operating remotely through connection to digital platforms and the use of Augmented Reality technologies and devices for in-person and remote technical assistance. It is able to suggest possible innovations in the management of maintenance activities (predictive maintenance development), identifying any necessary technological updates. Contributes to the enhancement of Big Data generated by customer processes.

It determines the execution times of the various processing phases taking into account the characteristics of the materials and production technologies, including for example developments related to advanced robotics. He will be able to configure any Additive Manufacturing processes so that they ensure specific conditions of productivity, quality, reliability and safety. It governs and values the data obtained (in real time) from the lines. Monitors the progress of production by detecting any deviations, also through the use of Data Analytics. It is also able to manage the data flow along the entire production process chain.

He knows how to use diagnostic techniques for troubleshooting based on application software and knows the developments related to advanced Human Machine Interface and Augmented Reality devices. In addition to the database made available by the company maintenance information system, it is able to use any predictive Data Analytics to plan methods and times of interventions. It contributes to the development of a predictive maintenance system, providing useful information to rationalize inspection plans and optimize the activation of replacement and restoration interventions according to the probability of possible failures or malfunctions. It uses (supported by software for this purpose) the maintenance database to find and analyze information on the historical trend of the operating parameters of machines and systems and propose possible hypotheses of functional changes / adaptations, taking into account their life cycle, also with the goal of predictive maintenance.