Many advanced product manufacturing approaches have been introduced in the market in recent years. Thus, it is critical to develop modern techniques which can effectively familiarize budding minds with the latest manufacturing procedures. In fact, the contemporary training methods and advanced education practices are crucial to uphold the interest of the new generation as well as to equip them with state-of the art systems. There is a need for innovative ideas and effective methodologies to inculcate the desired competency and prepare students for prospective manufacturing set ups. In the latest Industry 4.0 paradigm, visualization technologies, especially virtual reality, have been emphasized to sustainably train and educate young students. This work presents a technique for utilizing the leading visualization method based on virtual reality in product manufacturing. It aims to acquaint students with the prominent concept of Industry 4.0, the reconfigurable manufacturing system (RMS). The RMS has been a demanding topic for the novice and, most often, amateurs are not able to grasp and interpret it. Therefore, this paper outlines the various steps that can be useful for students in order to anticipate the RMS design, interact with it, understand its operation, and evaluate its performance.
This paper proposes a reliability-oriented stochastic aggregated integer linear framework for full observability of the automated distributed systems based on the µ-synchrophasor units. The µ-synchrophasor unit as a newly introduced high-tech device makes it possible for an accurate and highspeed measurement of the voltage and current waveforms in the distribution systems. This paper proposes a multi-stage strategy for the µ-synchrophasor unit placement together with the communication system requirements in the reconfigurable distribution systems, considering the zero-injection constraints in the model. To determine the optimal topology at the end of each phase, a reliability-based cost function is developed to optimize the customer interruption costs and power losses simultaneously. In order to model the uncertainties of forecast error in the active and reactive load demands as well as the failure rate and repair rate parameters, a stochastic framework based on the fuzzy cloud theory is employed. The proposed bi-level mixed integer linear programing approach is used to co-optimize the network switching scheme as well as the optimal µ-synchrophasor positions and communication infrastructure costs in the same framework. The simulation results on a practical test system verify the observability of the automated reconfigurable distribution system during the reconfiguration process.
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