Digital Twin (DT) implementations can contribute to smart manufacturing by integrating the physical and the cyber space. Artificial Intelligence (AI) applications based on Machine Learning (ML) are widely accepted as promising technologies in manufacturing. However, ML methods require large volumes of quality training datasets and in the case of supervised ML, manual input is usually required for labelling those datasets. Such an approach is expensive, prone to errors and labour as well as time-intensive, especially in a highly complex and dynamic production environment. DT models can be utilized for accelerating the training phase in ML by creating suitable training datasets as well as by automatic labelling via the simulation tools chain and thus alleviating user’s involvement during training. These synthetic datasets can be enhanced and cross-validated with real-world information which is not required to be extensive. A framework for implementing the proposed DT-driven approach for developing ML models is presented. The proposed framework has been implemented in an industrially relevant use case. The use case has provided evidence that the proposed concept can be used for training vision-based recognition of parts’ orientation using simulation of DT models, which in turn can be used for adaptively controlling the production process.

Production systems empowered by digital simulation tools can be improved in a time and cost-effective approach. The enrichment of digital simulations with sensor data, can enhance their realism and improve the accuracy of their results. Hence, this study proposes an implementation of the digital twin approach as part of a wider cyber-physical system (CPS) to enable the optimisation of the planning and commissioning of human-based production processes using simulation-based approaches. This is achieved through a) sensor data fusion and motion recognition of human activities in a factory floor and b) knowledge management mechanism for capturing the implicit knowledge of the task execution. A case study from the intra-factory logistics operations in the white goods industry, demonstrates the feasibility of the proposed approach, by enriching the simulation of manual assembly operations with the operator’s knowledge in the form of spatiotemporal constraints.

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

A key characteristic for industrial production in the Industry 4.0 paradigm is the connection of physical items like sensors, devices and enterprise assets, connected both to each other and to the Internet. In this Internet of Things environment, things will sense more data, become context-aware and provide added-value information to assist people take more relevant and valuable decisions. Context-aware information distribution may offer substantial value to manufacturing. It provides task-relevant information or services to users in a manufacturing shop-floor improving decision making through context-driven recommendations. This paper presents a context-aware information distribution system to support users in an industrial environment. The system aims at utilising data collected from sensors located at a shop-floor in order to increase the visibility of shop-floor processes by providing the right information, to the right people, at the right time. The system’s architecture and key elements have been developed for a pilot implementation in the white goods industry and are presented in this study.

Digital manufacturing technologies have been considered an essential part of the continuous effort towards the reduction in a product’s development time and cost, as well as towards the expansion in customisation options. The simulation-based technologies constitute a focal point of digital manufacturing solutions, since they allow for the experimentation and validation of different product, process and manufacturing system configurations. This article investigates simulation-based applications in a series of different technological and manufacturing domains. First, this article discusses the current industrial practice, focusing on the use of information technology. Next, a series of simulation-based solutions are explored in the domains of product and production process design, as well as in the area of enterprise resource planning. The current technologies and research trends are discussed in the context of the new landscape of computing hardware technologies and the emerging computing services, including the initiatives comprising both the Internet cloud and the Internet of things.

In a manufacturing shop floor, context-aware intelligent service systems can be used to provide information services to shop floor personnel, according to their situation. This article presents an industrial Internet of things (IIoT) context-aware information system that provides decision support for mobile or static operators and supervisors. The system uniquely combines key IIoT concepts such as multi-layered, service-oriented architecture, which integrates several subsystems, such as sensor data acquisition together with concepts for developing context-aware systems such as context modelling and contextual data information provision. The proposed solution is relevant for several manufacturing environments. However, in dynamic, semi-structured manufacturing environments that the workstations are not fixed but dynamically change over time and in which the workers are mobile and not static to some predefined workstations, the utilisation of context-aware information delivery is considered as even more relevant. For that reason, a case study with a medium-sized shipyard has been selected and conducted. Industrial requirements were gathered and led to use cases for a customised implementation of the proposed solution into a shipyard. The proposed solution includes a number of context-aware apps that support the users in carrying out production tasks. The study provided insights into the suitability of the solution presented in the shipyard environment and brought to light further aspects that would improve the proposed context-aware system.