Bicriteria robotic operation allocation in a flexible manufacturing cell

Consider a manufacturing cell of two identical CNC machines and a material handling robot. Identical parts requesting the completion of a number of operations are to be produced in a cyclic scheduling environment through a flow shop type setting. The existing studies in the literature overlook the flexibility of the CNC machines by assuming that both the allocation of the operations to the machines as well as their respective processing times are fixed. Consequently, the provided results may be either suboptimal or valid under unnecessarily limiting assumptions for a flexible manufacturing cell. The allocations of the operations to the two machines and the processing time of an operation on a machine can be changed by altering the machining conditions of that machine such as the speed and the feed rate in a CNC turning machine. Such flexibilities constitute the point of origin of the current study. The allocation of the operations to the machines and the machining conditions of the machines affect the processing times which, in turn, affect the cycle time. On the other hand, the machining conditions also affect the manufacturing cost. This study is the first to consider a bicriteria model which determines the allocation of the operations to the machines, the processing times of the operations on the machines, and the robot move sequence that jointly minimize the cycle time and the total manufacturing cost. We provide algorithms for the two 1-unit cycles and test their efficiency in terms of the solution quality and the computation time by a wide range of experiments on varying design parameters.     

Evaluation of natural gas hydrates as a future methane source

In recent years, attention has been given to obtaining methane gas from natural gas hydrates (NGHs) sediment; but its production, economics, and safety are still far away from being commercially viable for many years, and so more research is needed. NGHs are nonstoichiometric crystalline solid compounds that form from mixtures of water molecules and light weight natural gases such as methane, ethane, propane, and carbon dioxide. They are formed in specific thermodynamic conditions, low temperatures (5–15°C) and high pressures (2–3 MPa), and are found in (a) onshore polar regions beneath permafrost and (b) offshore deep-sea sediments. Methane, NG, is the cleanest fossil fuel and its huge amounts in NGHs have carbon quantities more than double of all fossil fuels. The methods that have been proposed for NG extraction from NGHs include: (a) depressurization, (b) thermal stimulation, and (c) chemical inhibitor injections. The authors review the potential of methane gas from NGHs as an unconventional source of future energy. The formation of NGHs as well as extraction of methane from NGHs coupled with technical and environmental challenges are also addressed.     

Reliability-Aware Heterogeneous 3D Chip Multiprocessor Design

Ability to stack separate chips in a single package enables three-dimensional integrated circuits (3D ICs). Heterogeneous 3D ICs provide even better opportunities to reduce the power and increase the performance per unit area. An important issue in designing a heterogeneous 3D IC is reliability. To achieve this, one needs to select the data mapping and processor layout carefully. This paper addresses this problem using an integer linear programming (ILP) approach. Specifically, on a heterogeneous 3D CMP, it explores how applications can be mapped onto 3D ICs to maximize reliability. Preliminary experiments indicate that the proposed technique generates promising results in both reliability and performance.     

Effect of austenitization heat treatment on the magnetic properties of Fe-40wt % Ni-2wt % Mn alloy

The effect of austenitization heat treatment on magnetic properties was examined by means of M6ssbauer spectroscopy on an Fe-40wt%Ni-2wt%Mn alloy. The morphology of the alloy was obtained by using scanning electron microscopy （SEM） under different heat treatment conditions. The magnetic behavior of the non heat-treated alloy is ferromagnetic. A mixed magnetic structure including both paramagnetic and ferromagnetic states was obtained at 800℃ after 6 and 12 h heat treatments. In addition, the magnetic structure of the heat-treated alloy at 1150~C for 12 h was ferromagnetic. With the volume fraction changing, the effective hyperfine field of the ferromagnetic austenite phase and isomery shift values were also determined by Mtssbauer spectroscopy.     

Effective harvesting,detection, and conversion of IR radiation due to quantum dots with built-in charge

We analyze the effect of doping on photoelectron kinetics in quantum dot [QD] structures and find two strong effects of the built-in-dot charge. First, the built-in-dot charge enhances the infrared [IR] transitions in QD structures. This effect significantly increases electron coupling to IR radiation and improves harvesting of the IR power in QD solar cells. Second, the built-in charge creates potential barriers around dots, and these barriers strongly suppress capture processes for photocarriers of the same sign as the built-in-dot charge. The second effect exponentially increases the photoelectron lifetime in unipolar devices, such as IR photodetectors. In bipolar devices, such as solar cells, the solar radiation creates the built-in-dot charge that equates the electron and hole capture rates. By providing additional charge to QDs, the appropriate doping can significantly suppress the capture and recombination processes via QDs. These improvements of IR absorption and photocarrier kinetics radically increase the responsivity of IR photodetectors and photovoltaic efficiency of QD solar cells.     

Self-consistent modeling of heating and MOSFET performance in 3-D integrated circuits

We present a new method for finding the temperature profile of vertically stacked three-dimensional (3-D) digital integrated circuits (ICs). Using our model, we achieve spatial thermal resolution at the desired circuit level, which can be as small as a single MOSFET. To resolve heating of 3-D ICs, we solve nonisothermal device equations self-consistently with lumped heat flow equations for the entire 3-D IC. Our methodology accounts for operational variations due to technology nodes (hardware: device), chip floor plans (hardware: layout), operating speed (hardware: clock frequency), and running applications (software). To model hardware, we first decide on an appropriate device configuration. We then calculate elements of the lumped thermal network using the 3-D IC layout. To include software, chip floor plan, and duty cycle-related performance variations, we employ a statistical Monte Carlo type algorithm. In this paper, we investigate performance of vertically stacked 3-D ICs, with each layer modeled after a Pentium III. Our calculated results show that layers within the stacked 3-D ICs, especially the ones in the middle, may greatly suffer from thermal heating.     

Compact modeling of 0.35 μm SOI CMOS technology node for 4 K DC operation using Verilog-A

Compact modeling of MOSFETs from a 0.35 μm SOI technology node operating at 4 K is presented. The Verilog-A language is used to modify device equations for BSIM models and more accurately reproduce measured DC behavior, which is not possible with the standard BSIM model set. The model presented exhibits convergent behavior and is shown to be experimentally accurate at 4 K. No design tool currently in place exhibits convergence and/or accuracy over this range. The Verilog-A approach also allows the embedding of nonlinear length, width and bias effects into BSIM calculated curves beyond those that can be achieved by the use of different BSIM parameter sets. Nonlinear dependences are necessary to capture effects particular to 4 K behavior, such as current kinks. The 4 K DC behavior is reproduced well by the compact model and the model seamlessly evolves during simulation of circuits and systems as the simulator encounters SOI MOSFETs with different lengths and widths. The incorporation of various length/width and bias dependent effects into one Verilog-A/BSIM4 library, therefore, produces one model for all sets of devices called up in a given product design kit (PDK) for this technology node.     

Forecasting electric energy consumption using neural networks

An artificial neural network model is developed to relate the electric energy consumption in the Eastern Province of Saudi Arabia to the weather data (temperature and humidity), global solar radiation and population. A two layered feedforward neural network is used for the modelling. The inputs to the neural network are the independent variables and the output is the electric energy consumption. Seven years' of data are used for model building and validation. Model adequacy is established by a visual inspection technique and the chi-square test. Model validation, which reflects the suitability of the model for future predictions is performed by comparing the predictions of the model with future data that was not used for model building. Comparison with a regression model shows that the neural network model performs better for predictions.     

Management of product variety in cellular manufacturing systems

In today’s markets, non-uniform, customized products complicate the manufacturing processes significantly. In this paper, we propose a cellular manufacturing system design model to manage product variety by integrating with the technology selection decision. The proposed model determines the product families and machine groups while deciding the technology of each cell individually. Hedging against changing market dynamics leads us to the use of flexible machining systems and dedicated manufacturing systems at the same facility. In order to integrate the market characteristics in our model, we proposed a new cost function. Further, we modified a well known similarity measure in order to handle the operational capability of the available technology. In the paper, our hybrid technology approach is presented via a multi-objective mathematical model. A filtered-beam based local search heuristic is proposed to solve the problem efficiently. We compare the proposed approach with a dedicated technology model and showed that the improvement with the proposed hybrid technology approach is greater than 100% in unstable markets requiring high product varieties, regardless of the volumes of the products.