Manufacturing execution systems play an important role of bridging high-level enterprise functions and production or manufacturing operations. The embedded systems are usually in charge of controlling execution of the operations. Modern embedded systems have become capable of simultaneous and deterministic execution of control algorithms and IP-based communication, making it possible to create complex cyber-physical systems (CPSs), where the computational and communication resources of a device can be used directly for various control, supervisory, or monitoring functions. The complexity for defining open-knowledge-driven manufacturing execution system (OKD-MES) is in maintaining awareness of overall system state to avoid disruptive actions as various functions may be requested from a system. The problem is that obtaining such information on system state may necessitate collecting data from a number of devices, as there may not be a single data point for state information. This paper describes and illustrates an approach for designing OKD-MES on top of CPSs that controls robot workstations and conveyor-based transportation system of a pallet-based production system.

This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU–China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.

The rapid development and reconfiguration of assembly automation systems to accommodate product changes in a short period of time is a challenging task due to the limitations of the contemporary automation systems engineering approach. To improve the engineering process, Product Process Resource (PPR)-based modeling, simulation and data integration systems emerged as a promising approach to compress the development time and engineering cost by enabling collaboration and realization of product and manufacturing resources in a virtual environment. However, due to ineffective coupling of PPR data, design and reconfiguration of assembly systems is still a challenging task and is highly dependent on the knowledge and experience of engineers. This paper presents a knowledge-driven engineering approach to manage the mapping of engineering data sets to interconnect product attributes to manufacturing process and resources. A case study based on a Festo test rig is presented for illustrating the implementation of the approach. The successful identification of the tasks required to manufacture a product proves the approach concept but only for the presented use case.

The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.

Web services are widely used for enterprise software development. Web service protocols simplify application integration thanks to interface description that can be processed at runtime and, in addition, due to mature and widely used standards for transportation and internetworking. Implementation of service-oriented architecture starts to get acceptance in the field of industrial automation ranging from international research project to the first implementations in industry including first commercial controller devices supporting web service communication protocols and executing deterministic control at the same time. The current research step is to allow knowledge-based integration of industrial automation systems and to exploit full potentials of run-time reconfiguration and adaptation of industrial systems. This paper demonstrates implementation of knowledge-based industrial system, the architecture and ontology model that can be generalized for implementation of other production systems.