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 an approach to using semantic web services in managing production processes. In particular, the devices in the production systems considered expose web service interfaces through which they can then be controlled, while semantic web service descriptions formulated in web ontology language for services (OWL-S) make it possible to determine the conditions and effects of invoking the web services. The approach involves three web services that cooperate to achieve production goals using the domain web services. In particular, one of the three services maintains a semantic model of the current state of the system, while another uses the model to compose the domain web services so that they jointly achieve the desired goals. The semantic model of the system is automatically updated based on event notifications sent by the domain services.

Increasing textile production is associated with an environmental burden which can be decreased with an improved recycling system by digitalization. The collection of textiles is done with so-called curbside bins. Sensor technologies support dynamic-informed decisions during route planning, helping predict waste accumulation in bins, which is often irregular and difficult to predict. Therefore, dynamic route-optimization decreases the costs of textile collection and its environmental load. The existing research on the optimization of waste collection is not based on real-world data and is not carried out in the context of textile waste. The lack of real-world data can be attributed to the limited availability of tools for long-term data collection. Consequently, a system for data collection with flexible, low-cost, and open-source tools is developed. The viability and reliability of such tools are tested in practice to collect real-world data. This research demonstrates how smart bins solution for textile waste collection can be linked to a dynamic route-optimization system to improve overall system performance. The developed Arduino-based low-cost sensors collected actual data in Finnish outdoor conditions for over twelve months. The viability of the smart waste collection system was complemented with a case study evaluating the collection cost of the conventional and dynamic scheme of discarded textiles. The results of this study show how a sensor-enhanced dynamic collection system reduced the cost 7.4% compared with the conventional one. We demonstrate a time efficiency of -7.3% and that a reduction of 10.2% in CO2 emissions is achievable only considering the presented case study.

Offering efficacious instruction for production based technical education to provide adequate hands-on training has always been a growing concern in technical universities. This paper proposes the use of Digital Twins as a tool for training students on production based systems and justifies the proposition by instantiating learning theories within a pedagogical digital twin framework. Further, the paper investigates how situational awareness can be beneficial in such an environment and its contribution to the overall learning experience.

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.