Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data streaming from high amount of sensors and other data sources. Cyber-physical systems play an important role as sources of information to achieve context sensitivity. Cyber-physical systems can be seen as complex intelligent sensors providing data needed to identify the current context under which the manufacturing system is operating. In this paper, it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of cyber-physical systems integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution encompasses run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes.

Purpose: The purpose of this paper is to present an ontology‐based approach of context‐sensitive computing for the optimization of flexible manufacturing systems (FMS).Design/methodology/approach: A context‐sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology‐based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA‐based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported.Findings: Continuous improvement of the factory can be enhanced utilizing context‐sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.Research limitations/implications: The performance of context‐sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade‐off should be in between the extent of context processing and the required outcome of the support applications.Practical implications: This paper includes the practical implications of context‐sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS.Originality/value: Reported results provide a modular approach of context‐sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

A new approach, based on self-learning embedded services, allowing for high adaptation and integration of complex, flexible production systems is presented. The context sensitive solution for adaptation of discrete, flexible assembly/manufacturing lines is proposed. The proposed solution includes context extractor, adapter and self-learning modules allowing for integration of the control and so-called secondary processes depending on the extracted context. Both context extraction and adapter are continuously learning and improving their performance. Service Oriented Architecture principles are used to implement these modules. Generic solution and specific applications in three various manufacturing environments are presented.

The work presented in this paper demonstrates how flexible manufacturing systems (FMS) combined with context awareness can be used to allow for an improved decision support in manufacturing industry. Thereby manufacturing companies shall be supported in a continuous process of increasing efficiency and availability of their production machines. Such optimization has to be embedded in the processes allowing for run time adaptation of the process to various dynamically changing external conditions. Context awareness, based on the information obtained from cyber physical systems, is a promising approach to allow for efficient building of such embedded optimization solutions. The objective of the research presented is to explore how context awareness, using the information from cyber physical systems integrated in the processes, can be applied to build a solution for self-optimization of discrete, flexible manufacturing processes.

In this paper, we present a generic solution for context based self-adaptation of production systems. We describe the overall architecture of the proposed system, as well as its components. Furthermore, we illustrate how this architecture is being used to extract the production context of machine tools and improve their energy efficiency.

AI and robotics aim to transform workplace landscapes in a several sectors such as manufacturing, logistics, healthcare, construction, agriculture, and education. Central to this evolution is the innovative use of Digital Twin technology, which creates real-time updated virtual replicas of physical systems and entities. This technology is especially transformative in healthcare and education, promising customized and efficient experiences for all involved. This paper outlines the AI4Work project’s approach to leveraging Digital Twin Technology to improve work environments in these sectors. The goal of AI4Work is to formulate a workplace where AI and robots seamlessly collaborate with humans, while explores how to best share tasks between humans and machines in six different domains. For healthcare, AI4Work will explore how Digital Twin technology can assist occupational doctors and psychologists in monitoring the physical and mental health of hospital personnel in order to predict burnout symptoms and to create a sustainable working environment. In education, AI4Work will investigate how to uphold the mental health of both educators and students while fostering a more supportive and enduring educational setting.