Twenty years ago, the field of Robotics was defined and galvanized by the creation of an IEEE Journal devoted to the topic. We now have the opportunity to do the same for the field of Automation. The IEEE voted in February 2003 to bifurcate the IEEE Transactions on Robotics and Automation, the top-cited archival publication on robotics. The renamed Transactions on Robotics will gain a sibling: the Transactions on Automation Science and Engineering.T-ASE will publish foundational research on Automation: scientific methods and technologies that improve efficiency, productivity, quality, and reliability, specifically for methods, machines, and systems operating in structured environments over long periods, and the explicit structuring of environments. Its coverage will go beyond Automation's roots in mass production and include many new applications areas, such as Biotechnology, pharmaceutical, and health care; Home, service, and retail; Construction, transportation, and security; Manufacturing, maintenance, and supply chains; and Food handling and processing. Research includes topics related to robots and intelligent machines/systems in structured environments and the explicit structuring of environments, and topics at the Operational/Enterprise levels such as System Modeling, Analysis, Performance Evaluation; Planning, Scheduling, Coordination; Risk Management; and Supply Chain Management. T-ASE will integrate knowledge across disciplines and industries.Details on IEEE's motivation, distinctions between Robotics and Automation, scope statements, and sample application areas are available at: http://www.ieee.org/t-ase. The first issue will appear in July 2004.

Energy saving in production plants is becoming more and more relevant due to the pressure from governments to contain the environmental impact of manufacturing, and from companies to reduce costs. One of the measures for saving energy is the implementation of control strategies that reduce energy consumption during the machine idle periods. This paper proposes a framework that integrates different control policies for switching the machine off when production is not critical, and on when the part flow has to be resumed. A general policy is formalized by modelling explicitly the energy consumed at each machine state. The policy parameters that minimize the requested machine expected energy are provided analytically for general distributions. Numerical results are based on data acquired with dedicated experimental measurements on a real machining centre, and a comparison with the most common practices in manufacturing is also reported.Note to Practitioners -The paper analyzes the control problem of switching off-on a machine tool for energy saving during the idle times. Different control policies are investigated pointing out the most significant factors. A mapping of the optimal control policy as a function of warm-up time and mean part arrival time is also provided. An algorithm is described for identifying the optimal control parameters. The results of this research will be useful for a practical implementation of a switching policy for energy saving. This implementation requires the fitting of the distribution modelling the part arrival times, the estimation of the warm-up time, and the estimation of the power adsorbed by the machine in four different states.

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The growing amount of knowledge on machine energy consumption underlines the increasing interest in the sustainable aspect of manufacturing. The heterogeneity of production equipment and processes makes the machine hard to modeling. A method able to build an energy state-based model of a complex machine is presented. First the machine is divided into functional modules, afterword each module is modeled in terms of states and events with automata theory. A specific logic automaton represents the relationships existing among modules. Then, the machine state model is automatically obtained using a synchronization algorithm. The method has been validated on real machining centers

Simulation classes have the main advantage of deeply involving and stimulating students through intensive work in computer laboratories and projects. The counterpart is often the lack of the real system that is subject to simulation modeling. Creating, building and validating a simulation model of a system that cannot be observed represent a real obstacle for student learning. In this paper, we describe the experience from an educational project launched in a course of manufacturing systems for mechanical engineering students in which discrete event simulation plays a fundamental role in performance evaluation. The project has been designed to exploit student interaction with a LEGO R -based physical system. Students have the possibility to learn from the physical system and making experiments together with the simulation model built during project activities. The project details are also described with the hope that the project becomes a simulation case study and be replicated in other courses.

A general framework for switching the machine off/on has been recently proposed in literature for single machines. However, the amount of information available along the production system is often limited and it might be not trivial to understand which information provides more benefits. This paper studies the performance of a production line when several control policies are applied at machine level. The amount of information at machine level varies and the trade-off with energy reduction is investigated. The considered performance measures are the energy consumed per part and the system throughput. Numerical results are based on discrete event simulation

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