This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing D...
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This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing Design of Experiments (DoE) with Taguchi and Response Surface analysis, we identify influential parameters affecting TS and EC. A Mixed-Integer nonlinearmultiobjectiveoptimization model is then utilized to balance TS and EC, resulting in optimal parameter values. Validation using fabricated specimens demonstrates less than 5 % error in Tensile Strength and less than 2 % error in Energy Consumption, confirming the efficacy of the proposed methodology.
In the manufacturing industry, laser welding is an important production process, including cutting and welding designs. Obtaining optimal designs is critical to improving the manufacturer's efficiency and competit...
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In the manufacturing industry, laser welding is an important production process, including cutting and welding designs. Obtaining optimal designs is critical to improving the manufacturer's efficiency and competitiveness, especially in their product performance. A multi-objectivenonlinear programming model is formulated for the laser welding design problem to minimize the length of the welds and the material consumption of the laser-welded blanks. An encoding mechanism is proposed to represent the decision variables. A non-dominated sorting genetic algorithm based on local search and incremental support vector regression (NSGA-II-ISVR), as a multi-objective evolutionary algorithm, is developed. Candidate sets are periodically selected for local search based on decomposition in order to locate promising areas of the feasible region for further exploitation. The trained SVRs are used as surrogate models to find approximate fitness values of the trial solutions in the offspring population and a selection rule is used to select the well performing solutions in the offspring population into the candidate set so as to reduce the function evaluations of the actual criterion vectors. The experimental results using four real-world instances of laser-welded blanks demonstrate that the developed NSGA-II-ISVR is very effective in finding good solutions as compared to four state-of-the-art baseline procedures.
A multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to quickly obtain a fixed size Pareto-front approximation. It adapts ideas ...
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A multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to quickly obtain a fixed size Pareto-front approximation. It adapts ideas from different multiobjective evolutionary algorithms, but also incorporates new devices. In particular, the search in the feasible region is carried out on promising areas (hyperspheres) determined by a radius value, which decreases as the optimization procedure evolves. This mechanism helps to maintain a balance between exploration and exploitation of the search space. Additionally, a new local search method which accelerates the convergence of the population towards the Pareto-front, has been incorporated. It is an extension of the local optimizer SASS and improves a given solution along a search direction (no gradient information is used). Finally, a termination criterion has also been proposed, which stops the algorithm if the distances between the Pareto-front approximations provided by the algorithm in three consecutive iterations are smaller than a given tolerance. To know how far two of those sets are from each other, a modification of the well-known Hausdorff distance is proposed. In order to analyze the algorithm performance, it has been compared to the reference algorithms NSGA-II and SPEA2 and the state-of-the-art algorithmsMOEA/DandSMS-EMOA. Several quality indicators have been considered, namely, hypervolume, average distance, additive epsilon indicator, spread and spacing. According to the computational tests performed, the new algorithm, named FEMOEA, outperforms the other algorithms.
Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly int...
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Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly intractable. In order to cope with those problems, several (meta) heuristic procedures have been developed during the last decade whose aim is to obtain a good discrete approximation of the Pareto-front. In this vein, a new multi-objective evolutionary algorithm, called FEMOEA, which can be applied to many nonlinear multi-objective optimization problems, has recently been proposed. Through a comparison with an exact interval branch-and-bound algorithm, it has been shown that FEMOEA provides very good approximations of the Pareto-front. Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D. Comprehensive computational studies have shown that, among the studied algorithms, FEMOEA was the one providing, on average, the best results for all the quality indicators analyzed. However, when the set approximating the Pareto-front must have many points (because a high precision is required), the computational time needed by FEMOEA may not be negligible at all. Furthermore, the memory requirements needed by the algorithm when solving those instances may be so high that the available memory may not be enough. In those cases, parallelizing the algorithm and running it in a parallel architecture may be the best way forward. In this work, a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. The results show that FEMOEA-Paral is able to maintain the effectiveness of the sequential version and this by reducing the computational costs. Furthermore, the parallel version shows good scalability. The efficiency results have been analyzed by means of a profiling and tracing toolkit for performance analysis. (C) 2014 Elsevier Inc. All
Six-port reflectometer is well-known for its ability to measure magnitude and phase-shift of microwave signal using four power detectors that perform magnitude-only measurements. This paper presents the development of...
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Six-port reflectometer is well-known for its ability to measure magnitude and phase-shift of microwave signal using four power detectors that perform magnitude-only measurements. This paper presents the development of an innovative symmetric ring junction as four-port reflectometer for complex reflection coefficient measurements. It reduces the number of required detectors to two. Design optimization, new calibration modeling and algorithm are discussed in details for this four-port reflectometer. The developed four-port reflectometer is compared to five-port reflectometer and vector network analyzer. It is found that the measured magnitude and phase-shift shows good performance in comparison with the commercial vector network analyzer and the five-port reflectometer.
作者：
Hu, WanqiuTian, JinpingChen, LujunTsinghua Univ
Sch Environm Beijing 100084 Peoples R China Tsinghua Univ
Ctr Ecol Civilizat Beijing 100084 Peoples R China Tsinghua Univ
Yangtze Delta Reg Inst Dept Environm Zhejiang Prov Key Lab Water Sci & Technol Jiaxing 314006 Zhejiang Peoples R China
The tradeoff between economic growth and environmental protection has been a critical issue in facilitating eco-industrial park development in China. As the principal contributors to China's industrial output, man...
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The tradeoff between economic growth and environmental protection has been a critical issue in facilitating eco-industrial park development in China. As the principal contributors to China's industrial output, many industrial parks have been addressing the issues of intensive resource consumption and pollutant generation, driven by much stricter regulations on the environment and resource management. Retuning the industrial structure is a substantial way to address the environmental issues while promoting economic development, which are the goals of eco-industrial development. This study proposes a multi-criteria industrial structure adjustment model by employing a generalized reduced gradient method to find the optimal structure of an industrial park. The model aims to increase the overall resource utilization efficiency and industrial output efficiency through a decoupling between the economic development and environmental burden of the park. A Chinese eco-industrial park located in the capital, the Beijing Economic-technological Development Area (BDA), is used as an example to uncover a transformation roadmap from a high-speed mode to a high-quality mode. The constraints of the multi-criteria decision-making model mainly focus on the limits of water consumption and pollutant emissions by targeting an appropriate economic development rate. The key findings are as follows. First, BDA could achieve 186% economic growth with 20% water consumption and 30% contaminant reduction in five years (2020-2025) by optimizing the industrial structure. Second, the advanced manufacturing industries play significant roles in stimulating the high-quality development. Third, ammonia nitrogen is a crucial factor restricting economic development under the requests of the "dual control" policy. Forth, the industry that can use reclaimed water in production will get more development opportunities and space, and vice versa. The model can be applied in diverse industrial parks by modifyin
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