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金沢大学融合研究域融合科学系, The purpose of this paper is to present a new predictive control utilizing online data and stored data of input/output of the controlled system. The conventional predictive control methods utilize the mathematical model of the control system to predict an optimal future input to control the system. The model is usually obtained by a standard system identification method from the measured input/output data. The proposed method does not require the mathematical model to predict the optimal future control input to achieve the desired output. This control strategy, called just-in-time, was originally proposed by Inoue and Yamamoto in 2004. In this paper, we proposed a simplified version of the original just-in-time predictive control method. © (2014) Trans Tech Publications, Switzerland., 研究者情報DB

The diffusion coefficient in the metallic glass-forming systems such as Pd-Cu-Ni-P exhibits a marked deviation from the Stokes-Einstein (SE) relation in the proximity of the glass transition temperature. Such a deviation is characterized by the fractional exponent p of the modified SE expression. For the material Pd43Cu27Ni10P20, it has been reported that it takes the value p = 0.75. In this work, it is shown that the value of p is highly correlated with the ratio ED / ENB, where ED and ENB are the activation energies for diffusion coefficient D and cooperativity NB defined by the Bond Strength-Coordination Number Fluctuation (BSCNF) model. The present paper reports that for the metallic glass-forming melt Pd43Cu27Ni10P20, the fractional exponent p can be calculated accurately within the framework of the BSCNF model

International audience; The ACCENT Project (FP7- AAT- 2007- RTD-1) will allow the European Aero Engine manufacturers to improve their competitiveness by applying adaptive control techniques to the manufacturing of their components. For the critical rotating parts of aircraft engines, the surface integrity generated after machining is a key factor on the life cycle. In this context, one particular attention has to be carried out on tool condition. The aim of this paper is to define a monitoring approach able to detect the tool condition and machining disturbances. The main failure modes characterizing this particular Nickel base drilling and the apparition of embedded chips over the machined surface were identified. By experimental techniques, cartography of failure modes was performed. The results show that flank wear and notch are the main failure modes limiting the tool life. For some cutting conditions, the tool failure occurs after the first hole due to the important cutting forces. Some interesting combinations are made between the spindle current/accelerometers/ thrust force and flank wear, tool breakage and notch. Before these correlations, a detailed signal analysis was performed, considering different disturbing phenomena, such as chips evacuation problem. Finally, a "synopsis" for process monitoring is proposed, considering the analyzed phenomena.