LAUSR

Machine state control is one of the most promising energy-efficient measures for machining processes. A proper control reduces the energy consumed during idle periods by switching off/on the machines. A critical barrier for practical implementation is related to the knowledge of part arrival process that is affected by uncertainty. The stochastic processes involved in the system are usually assumed to be known. However, real production environments are subject to several sources of randomness that are difficult to model a priori. This work provides an online time-based algorithm that is able to control the machine state. Through a method for the estimation of the stochastic process, the algorithm provides the optimal control parameters based on a collected set of observations. A new policy is formulated to manage the control over time such that changes in the control parameters are applied only under certain conditions. Potential benefits are discussed using realistic numerical cases. Note to Practitioners—This article analyzes the control problem of switching off/on a machine tool for energy saving during machine idle periods. A control policy based on time information is investigated when the machine requires a startup time to resume the service after being switched off. The proposed policy works online while acquiring information from the real system. An algorithm is described for identifying and applying 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 estimation of the power adsorbed by the machine in four different states and, therefore, it reduces the implementation effort for practitioners.