The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.

Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors.

Industry 4.0 stresses the importance of considering production automation as an integrated and collaborative teamwork process between human workers and intelligent machines and tools. Collaborative robotics is a recent field of study for industrial automation. Although fenceless robot systems are available, the actual implementation of collaborative schemes for the conduction of assembly jobs should be supported through dedicated procedures and guidelines. These procedures have yet to be found and defined in detail. In this work, the authors claim that it may be possible to approach the problem of collaborative cell design with the methods devised for lean thinking. In the paper, the most common lean strategies are listed and analysed from the viewpoint of setting up a collaborative work cell. The most suitable strategies and tools are then recommended in a methodology that has been proposed to redesign an industrial assembly cell. The methodology has then been adopted in the presented industrial use case which is focused on the steps of HRC design process such as tasks assignment and scheduling.

With the proliferation of systems generating huge amounts of product information and the increasing number of companies involved in the production processes, the efficient management of product lifecycle is becoming a challenging issue. The problem of maintaining a coherent structure to represent and link different pieces of information is crucial for companies aiming at improving their performances by reducing the time to search for particular information.The need of developing models for such a complex information structure can be found in many works, which define semantic models and ontologies to represent and integrate information along the product lifecycle. However, some works address only specific cases, thus resulting in very detailed ontologies that model few portions of the product lifecycle and are not easily generalizable or usable, while other works define more general models, but did not provide concrete examples of applications in industrial domains.The aim of our work is to propose a reference ontology to support PLM, general enough to represent the main concepts and relationships needed to manage the whole product lifecycle. Furthermore, the PLM reference ontology can be extended and specialized to manage specific product lifecycles by adding new entities, sub-entities and relationships to its structure. An example of a real application of the development and extension of the PLM reference ontology in an industrial case is reported. The work presented in this paper is supported by the EU-FP7 project amePLM