# An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
## 3 Industry 4.0 Safety and Exposure Assessment Tools  
### Industry 4.0 Overview  
The introduction of automation and digitisation in the manufacturing environment and the inter-connection of sensors and machines with the physical world in often described as the Fourth Industrial Revolution. The term \"Industry 4.0\" was first introduced by the German government as the \"Industrie 4.0\" initiative, signifying a new era with the evolution of \"smart factories\" and the establishment of digital technologies in the manufacturing sector [33]. The First Industrial Revolution involved the use of water and steam power in the manufacturing sector in the late 18 th and mid-19 th century. The Second Industrial Revolution in the late 19 th century brought the evolution of electricity and electrical power to the assembly lines, while the Third Industrial Revolution began in the mid-20 th century with the introduction of electronic devices (such as computers) and information technologies [11; 33; 34].  
The concept of Industry 4.0 is based on the establishment of data generation and exchange between digital and physical systems using innovative technologies (such as cloud computing and data analytics) and advanced manufacturing platforms (such as autonomous robots and 3D printing). Figure 2 depicts the technologies and manufacturing systems developed as part of the Industry 4.0 and \"smart manufacturing\" concept. Internet of Things (IoT) refers to the connection of different devices and the data exchange between sensors and electronic devices [35], allowing for the control of dynamic systems and real-time optimisation, benefiting economic and industrial growth. At the same time, cloud computing architecture provides a platform for data storage and sharing, as well as resources for data analytics and on-demand services, supporting data management within the scope of IoT [36]. Smart factories require the use of descriptive and predictive data analytics to analyse past historic patterns as well as investigate though AI and ML techniques future trends and possibilities [37]. Based on the data structure and the complexity of the real-world problems, different machine learning techniques are used either individually or connected by employing multiple machine learning approaches for data analysis and the development of decision-making processes [38; 39].  
Digital twin (DT) technology describes the connection between the physical and digital world by developing a digital replica of the physical system, allowing the simulation, prediction, control, and optimisation of the system [40; 41].  
Real-time sensor data and AI algorithms have been utilised to explore the development of dynamic systems and predictive DT platforms [42]. Additionally, virtual reality (VR) platforms have become widespread as training and simulation tools for education, qualification, and product design and development [43]. However, recent advances in the industrial sector have shifted towards the research and development of augmented reality (AR) tools that project a \"hologram\" of virtual elements onto a device. Such solutions allow for the simultaneous observation of the real and virtual world and support the user in a variety of services, such as repair, maintenance, and training, maximising employees' efficiency and minimising risk [44; 45]. Wearable devices have also been developed to support time-consuming processes or the optimisation of manufacturing organisation via the use of data exchange through Cloud, IoT, and sensors, as well as a real-time connection between employees and desired industrial sectors [46]. Finally, the development of autonomous robots and 3D printing as part of an intelligent production network--part of the IoT--minimises risk by transforming the manufacturing landscape to operate without human presence and control, while offering a low-waste economic solution [47; 48].