This article discusses service-based manufacturing operations support in manufacturing networks. The concept of a manufacturing-oriented service network (MOSeN) is introduced. A MOSeN consists of service units and autonomous work systems; the service units provide manufacturing-oriented services and the autonomous work systems consume manufacturing-oriented services. This approach aims to improve the performance of manufacturing systems by increasing their productivity, availability, responsiveness and agility, while maintaining product quality, all of which increase competitiveness. The MOSeN concept is adopted in a framework for manufacturing operations support (FMOS), which includes all of the elements necessary for its appropriate development and implementation. The use of the approach is illustrated in a case study of service-based quality monitoring. It is implemented as a web service, and its use in two different manufacturing plants is examined. The case study clearly demonstrates how manufacturing-oriented services can be effectively employed and integrated into manufacturing operations within a networked manufacturing environment.
COBISS.SI-ID: 12233499
Modern theories propose autonomous structures as building blocks of next-generation manufacturing systems. However, their size and scope are not agreed upon and remain a subject of research. The paper presents a method for discovering autonomous structures within existing manufacturing systems. Firstly, it is shown how a complex network model of a manufacturing system can be obtained. Then, a method for discovering structure in complex networks is applied in order to find cohesive subnetworks - candidates for the formation of autonomous work systems. The approach is illustrated in a case study of engineer-to-order production.
COBISS.SI-ID: 12273179
The aim of this paper is to contribute to complex systems thinking in manufacturing organisations through the development of a metric for operational complexity. Operational complexity is concerned with the temporal aspects of coordination and control in manufacturing systems. Statistical complexity from computational mechanics theory is proposed as the metric. The metric can potentially be used to support decision making by objective assessment of complexity. The properties of the metric are explored through simulation studies. The simulation results confirm that the proposed metric captures the intuitive notion of complexity. It is shown that operational complexity is influenced by internal factors such as system structure, as well as external ones such as demand, and that complexity can be managed through the application of appropriate control methods. A case study is presented that applies the metric to real production data. The case study shows that the global recession had resulted in a decreased operational complexity of outputs.
COBISS.SI-ID: 11924251
To survive in the highly competitive global economy, manufacturing systems must be able to adapt to new circumstances. An important prerequisite for adaptation is the ability to learn, a process based on knowledge discovery and growth. The aim of this research is to uncover knowledge by examining a large volume of real-time manufacturing data collected during manufacturing operations and to use the insights gained to support decision-making and adaptive process control. The paper presents the concept of a self-learning autonomous work system. This concept introduces a learning loop into a manufacturing system composed of data acquisition, data mining (DM), and knowledge-building models. Two methods for DM are applied. A descriptive DM method enables discovery of patterns in data that may contribute to a better understanding of the manufacturing processes. A predictive process provides knowledge in the form of rules, which can then be used for enhanced decision-making. To illustrate the utility of the knowledge models, the concept of adaptive process control is introduced and implemented in a high pressure die-casting domain. A case study based on industrial data collected during die-casting operations provides a demonstration of the concept.
COBISS.SI-ID: 12421403
In certain domains of production engineering we are faced with very small batch production as it is the case in the production of heavy hydro energy equipment. In this domain manual welding is one of the most time consuming operations. Monitoring of the welding process is essential from the point of work organization as well as from the point of process control. In this paper a novel concept of data acquisition and recording of welding parameters to the welding diary is presented. Several considerations on signal acquisition, sampling rate, processing, data aggregation, wireless information transfer, and presentation are discussed. Implementation of the concept is discussed on laboratory and industrial examples. Keywords: arc welding, monitoring, wireless sensor networks, ZigBee communication
COBISS.SI-ID: 12360731