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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]

This paper introduces a new type of automated container terminal system, which utilizes multistory frame bridges and rail-mounted trolleys to transport containers between the quay and the yard. For this new design concept, this paper makes an explorative study to identify the challenges and opportunity for it to be applied in transshipment hubs. We compare the transport efficiency and stacking capacity between this new system and the widely used AGV-based system. Several analytical models and performance measures are proposed for the quantitative comparisons between these two ACT systems. [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]

Though the structured light measurement system has been successfully applied to the profile measurement of diffuse objects, it is still a challenge to measure shiny objects due to the mix of both specular and diffuse reflections. To this end, we propose a robust encoding and decoding method in this paper. First, the monochromatic stripe patterns are utilized to eliminate the effect of texture and color of objects. Second, an intensity mask, dynamically adjusting the intensity of a projected pattern, is applied to avoid overexposure without any pre-knowledge of the workpiece. Thus, it is more flexible and efficient, compared with the existing methods. Third, to solve the internal reflection of the shiny part, an extrapolation model, combined with the intensity mask, is developed to detect the stripe edge for pattern decoding, resulting in accurate and robust 3D reconstruction. Compared with traditional polarization based methods, it does not need to readjust for a new part. The experimental results show that the proposed method is capable of measuring various parts without surface pretreatment. [ABSTRACT FROM AUTHOR]

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]

Part feeders take an unsorted bulk of identical parts and output them in a uniform orientation. Common feeders sort out those items that settle initially on one specific face, and further reorient them as desired. Quick estimators of the probability of settling on a given face facilitate the design of parts for efficient feeding and of the feeding lines themselves. Nevertheless, the evaluation and the development of such estimators have been hindered by the lack of data. Here, I create and analyze a large, simulated dataset; evaluate estimators available in the literature by comparing their predictions to simulation results with the help of a custom-made metric; and propose new estimation algorithms. The new estimators offer viable alternative to the direct dynamic simulation of parts due to their low average errors. [ABSTRACT FROM AUTHOR]

Due to the complexity of modern manufacturing systems and the lack of optimal management of energy consumption, the energy efficiency of manufacturing systems in real industrial environment is much lower than designed level, which significantly increases operation cost, impedes company competitiveness in the global market, leads to high carbon dioxide emission, and results in destroyed environment and ecology. Compared with existing research efforts on energy management of single machine system in the literature, few works have been performed to study the opportunity for energy management of typical manufacturing systems with multiple machines and buffers. From the point-of-view of sustainability, considering stochastic factor and buffer utilization, this paper investigates the opportunity estimation for real-time energy control of typical multi-machine manufacturing systems without sacrificing system throughput. A numerical case study based on an automotive assembly line is used to illustrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a requirement-based bidding language for agent-based scheduling. The language allows agents to attach their valuations directly to scheduling performance requirements. Compared with general bidding languages, the proposed one reduces agents' valuation and system's communication complexities. In addition, it results in efficient winner determination problem models. Experimental results show that the requirement-based language exhibits superior winner determination performance in terms of problem-solving speed and scalability. [ABSTRACT FROM AUTHOR]

In this paper, we first describe a framework to model the sponsored search auction on the web as a mechanism design problem. Using this framework, we describe two well-known mechanisms for sponsored search auction-Generalized Second Price (GSP) and Vickrey-Clarke-Groves (VCG). We then derive a new mechanism for sponsored search auction which we call optimal (OPT) mechanism. The OPT mechanism maximizes the search engine's expected revenue, while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We then undertake a detailed comparative study of the mechanisms GSP, VCG, and OPT. We compute and compare the expected revenue earned by the search engine under the three mechanisms when the advertisers are symmetric and some special conditions are satisfied. We also compare the three mechanisms in terms of incentive compatibility, individual rationality, and computational complexity. [ABSTRACT FROM AUTHOR]

Fixtures are devices used in manufacturing systems to locate, immobilize, and hold workpieces. There are many different numerical measures proposed in the literature to quantify the locating accuracy of a fixture, and its capability in firmly holding the workpiece in the presence of external loads. These numerical measures are useful in fixture layout design and the related area of grasp synthesis. As a number of numerical measures are available, a user may face the problem that how to choose an appropriate one as the optimality criterion in practice for fixture design. In this paper, we present a comparative study on several widely used optimality criteria, based on both theoretical analysis and simulation studies. First, we establish a set of inequalities, which describe the relations between several widely used numerical measures. Second, comparisons between optimality criteria are demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Computer models are widely used to simulate real processes. Within the computer model, there always exist some parameters which are unobservable in the real process but need to be specified in the model. The procedure to adjust these unknown parameters in order to fit the model to observed data and improve predictive capability is known as calibration. Practically, calibration is typically done manually. In this paper, we propose an effective and efficient algorithm based on the stochastic approximation (SA) approach that can be easily automated. We first demonstrate the feasibility of applying stochastic approximation to stochastic computer model calibration and apply it to three stochastic simulation models. We compare our proposed SA approach with another direct calibration search method, the genetic algorithm. The results indicate that our proposed SA approach performs equally as well in terms of accuracy and significantly better in terms of computational search time. We further consider the calibration parameter uncertainty in the subsequent application of the calibrated model and propose an approach to quantify it using asymptotic approximations. [ABSTRACT FROM AUTHOR]

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]

The ability to detect changes is an essential competence that robots should possess for increased autonomy. In several applications, such as surveillance, a robot needs to detect relevant changes in the environment by comparing current sensory data with previously acquired information from the environment. We present an efficient method for point cloud comparison and change detection in 3D environments based on spatial density patterns. Our method automatically segments 3D data corrupted by noise and outliers into an implicit volume bounded by a surface, making it possible to efficiently apply Boolean operations in order to detect changes and to update existing maps. The method has been validated on several trials using mobile robots operating in real environments and its performance was compared to state-of-the-art algorithms. Our results demonstrate the performance of the proposed method, both in greater accuracy and reduced computational cost. [ABSTRACT FROM AUTHOR]

We present a study in human-centered automation that has potential to reduce patient side effects from high dose rate brachytherapy (HDR-BT). To efficiently deliver radiation to the prostate while minimizing trauma to sensitive structures such as the penile bulb, we modified the Acubot-RND 7-axis robot to guide insertion of diamond-tip needles into desired skew-line geometric arrangements. We extend and integrate two algorithms: Needle Planning with Integer Programming (NPIP) and Inverse Planning with Integer Programming (IPIP) to compute skew-line needle and dose plans. We performed three physical experiments with anatomically correct phantom models to study performance: two with the robot and one control experiment with an expert human physician (coauthor Hsu) without the robot. All were able to achieve needle arrangements that meet the RTOG-0321 clinical dose objectives with zero trauma to the penile bulb. We analyze systematic and random errors in needle placement; total RMS error for the robot system operating without feedback ranged from 2.6 to 4.3 mm, which is comparable to the RMS error of 2.7 mm obtained in an earlier study for PPI-BT treatment using a robot with 3D ultrasound feedback. [ABSTRACT FROM AUTHOR]