365,409 research outputs found

    The Eco-design and Green Manufacturing of a Refrigerator

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    AbstractThe paper introduces the Energy-related Products(ErP)directive and its late evolution. Both the general and the particular eco-design requirements of a refrigerator are presented. The criteria of standby/off are listed as well. The assumptions made for the modeling of the product and source of the database are put forward. There are 11 factors of environmental impacts used in the evaluation software EIME. The environmental impact of manufacturing, distribution, use and the end of life is analyzed according to a certain refrigerator. The results show that three factors are significant, which are electricity consumed by the refrigerator in the use stage, the raw materials of metal and plastics in the manufacturing. The solution to these problems is provided. The introduction of eco-design to development phase of a product is urgent nowadays

    Computer-Aided Conceptual Design Through TRIZ-based Manipulation of Topological Optimizations

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    Organised by: Cranfield UniversityIn a recent project the authors proposed the adoption of Optimization Systems [1] as a bridging element between Computer-Aided Innovation (CAI) and PLM to identify geometrical contradictions [2], a particular case of the TRIZ physical contradiction [3]. A further development of the research has revealed that the solutions obtained from several topological optimizations can be considered as elementary customized modeling features for a specific design task. The topology overcoming the arising geometrical contradiction can be obtained through a manipulation of the density distributions constituting the conflicting pair. Already two strategies of density combination have been identified as capable to solve geometrical contradictions.Mori Seiki – The Machine Tool Compan

    Requirements for building information modeling based lean production management systems for construction

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    Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face

    Applicability valuation for evaluation of surface deflection in automotive outer panels

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    Upon unloading in a forming process there is elastic recovery, which is the release of the elastic strains and the redistribution of the residual stresses through the thickness direction, thus producing surface deflection. It causes changes in shape and dimensions that can create major problem in the external appearance of outer panels. Thus surface deflection prediction is an important issue in sheet metal forming industry. Many factors could affect surface deflection in the process, such as material variations in mechanical properties, sheet thickness, tool geometry, processing parameters and lubricant condition. The shape and dimension problem in press forming is defined as a trouble mainly caused by the elastic recovery of materials during the forming. The use of high strength steel sheets in the manufacturing of automobile outer panels has increased in the automotive industry over the years because of its lightweight and fuel-efficient improvement. But one of the major concerns of stamping is surface deflection in the formed outer panels. Hence, to be cost effective, accurate prediction must be made of its formability. The automotive industry places rigi

    Efficient Robust Optimization of Metal Forming Processes using a Sequential Metamodel Based Strategy

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    The coupling of Finite Element (FE) simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a new and generally applicable structured methodology for modeling and solving robust optimization problems. Stochastic design variables or noise variables are taken into account explicitly in the optimization procedure. The metamodel-based strategy is combined with a sequential improvement algorithm to efficiently increase the accuracy of the objective function prediction. This is only done at regions of interest containing the optimal robust design. Application of the methodology to an industrial V-bending process resulted in valuable process insights and an improved robust process design. Moreover, a significant improvement of the robustness (> 2s ) was obtained by minimizing the deteriorating effects of several noise variables. The robust optimization results demonstrate the general applicability of the robust optimization strategy and underline the importance of including uncertainty and robustness explicitly in the numerical optimization procedure

    Sensitivity analysis of expensive black-box systems using metamodeling

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    Simulations are becoming ever more common as a tool for designing complex products. Sensitivity analysis techniques can be applied to these simulations to gain insight, or to reduce the complexity of the problem at hand. However, these simulators are often expensive to evaluate and sensitivity analysis typically requires a large amount of evaluations. Metamodeling has been successfully applied in the past to reduce the amount of required evaluations for design tasks such as optimization and design space exploration. In this paper, we propose a novel sensitivity analysis algorithm for variance and derivative based indices using sequential sampling and metamodeling. Several stopping criteria are proposed and investigated to keep the total number of evaluations minimal. The results show that both variance and derivative based techniques can be accurately computed with a minimal amount of evaluations using fast metamodels and FLOLA-Voronoi or density sequential sampling algorithms.Comment: proceedings of winter simulation conference 201

    A Quantum Many-body Wave Function Inspired Language Modeling Approach

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    The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory. The latest development for a more effective QLM has adopted word embeddings as a kind of global dependency information and integrated the quantum-inspired idea in a neural network architecture. While these quantum-inspired LMs are theoretically more general and also practically effective, they have two major limitations. First, they have not taken into account the interaction among words with multiple meanings, which is common and important in understanding natural language text. Second, the integration of the quantum-inspired LM with the neural network was mainly for effective training of parameters, yet lacking a theoretical foundation accounting for such integration. To address these two issues, in this paper, we propose a Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The QMWF inspired LM can adopt the tensor product to model the aforesaid interaction among words. It also enables us to reveal the inherent necessity of using Convolutional Neural Network (CNN) in QMWF language modeling. Furthermore, our approach delivers a simple algorithm to represent and match text/sentence pairs. Systematic evaluation shows the effectiveness of the proposed QMWF-LM algorithm, in comparison with the state of the art quantum-inspired LMs and a couple of CNN-based methods, on three typical Question Answering (QA) datasets.Comment: 10 pages,4 figures,CIK
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