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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. [ABSTRACT FROM PUBLISHER]

Automated manipulation of nanowires and nanotubes would enable the scalable manufacturing of nanodevices for a variety of applications, including nanoelectronics and biological applications. In this paper, we present an electric-field-based method for motion control, planning, and manipulation of nanowires in liquid suspension with a simple, generic set of electrodes. We first present a dynamic model and a vision-based motion control of the nanowire motion in dilute suspension with a set of N\times N controllable electrodes. Since the motion planning of a nanowire from one position to the target location is NP-hard, two heuristic algorithms are presented to generate near-optimal motion trajectories. We compare the heuristic motion planning algorithms with other existing algorithms such as the rapidly exploring random tree (RRT) and A^\ast algorithms. The comparisons show that the proposed heuristic algorithms obtain near-optimal minimum time trajectories. Finally, we demonstrate a single, integrated process to position, orient, and deposit multiple nanowires onto the substrate. Extensive experimental and numerical results are presented to confirm the motion control and planning algorithms. [ABSTRACT FROM PUBLISHER]

Semiconductor cluster tools are the integrated equipment to process a variety of silicon wafers for the fabrication of microelectronic components. The cluster tool system consists of several loadlock modules, processing chambers, and material handling armed robots for transferring wafers between them. Most scheduling problems for dual-armed cluster tools adopt cyclic scheduling with the assumption of swap sequence of the robot arm. A deadlock-free and non-cyclic scheduling is highly required to improve total throughput of cluster tools for various types of wafer flow patterns and various changes of equipment configurations. In this paper, we propose a Petri net decomposition approach to derive a near-optimal solution of deadlock-free and non-cyclic scheduling of dual-armed cluster tools to reduce the computational complexity. A timed Petri net model is introduced to represent a non-cyclic scheduling model for dual-armed cluster tools. In order to obtain a deadlock-free and non-cyclic schedule efficiently, we propose a deadlock avoidance control policy that restricts the markings to prevent unmarked siphons. Deadlock condition for the dual-armed cluster tools is characterized by the structure of Petri net model. The performance of the non-cyclic scheduling model by the proposed method is compared with that of the cyclic scheduling model. Computational results show the effectiveness of the non-cyclic scheduling model compared with the cyclic scheduling model for multiple wafer flow patterns. [ABSTRACT FROM PUBLISHER]

In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms. [ABSTRACT FROM PUBLISHER]

In scaling-up Carbon Nanotubes (CNTs) array manufacturing, the uniformity of CNTs' height, or flatness of array, is critical for the yield of nanodevices fabricated from CNTs, and thus needs to be properly controlled. However, since the flatness of the CNTs array is better characterized by a profile, the conventional run-to-run (R2R) controllers that are designed for a single or multiple quality indicators are not effective in controlling the CNTs array manufacturing process. Therefore, in this work, we first develop a statistical model to characterize the variation of the flatness profile, and then derive a novel R2R profile controller based on a state-space model and Kalman filter to improve the flatness of CNTs array. The performance of the proposed R2R control algorithm is studied and compared with existing controller via simulation studies. [ABSTRACT FROM PUBLISHER]