...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computing and Information Science in Engineering >A Simulation Data-Driven Design Approach for Rapid Product Optimization
【24h】

A Simulation Data-Driven Design Approach for Rapid Product Optimization

机译：用于快速产品优化的仿真数据驱动设计方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and continually based on the simulation results until a satisfactory design is obtained. However, the expensive computational costs and large resource consumption of complex products hinder the wide application of simulation in industry. It is not an easy task to search the optimal design solution intelligently and efficiently. Therefore, a simulation data-driven design approach which combines dynamic simulation data mining and design optimization is proposed to achieve this purpose in this study. The dynamic simulation data mining algorithm—on-line sequential extreme learning machine with adaptive weights (W_(adaptive)OS-ELM)—is adopted to train the dynamic prediction model to effectively evaluate the merits of new design solutions in the optimization process. Meanwhile, the prediction model is updated incrementally by combining new "good" data set to reduce the modeling cost and improve the prediction accuracy. Furthermore, the improved heuristic optimization algorithm—adaptive and weighted center particle swarm optimization (AWCPSO)—is introduced to guide the design change direction intelligently to improve the search efficiency. In this way, the optimal design solution can be searched automatically with less actual simulation iterations and higher optimization efficiency, and thus supporting the rapid product optimization effectively. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.
机译：传统的设计优化是设计，仿真和重新设计的迭代过程，需要大量的计算和分析。设计人员需要根据仿真结果手动并连续地调整和评估设计参数，直到获得满意的设计为止。但是，复杂产品的昂贵的计算成本和大量的资源消耗阻碍了仿真在工业中的广泛应用。智能，高效地搜索最佳设计解决方案并非易事。因此，本研究提出了一种将动态仿真数据挖掘与设计优化相结合的仿真数据驱动设计方法。动态仿真数据挖掘算法（具有自适应权重的在线顺序极限学习机（W_（adaptive）OS-ELM））用于训练动态预测模型，以有效地评估优化过程中新设计解决方案的优点。同时，通过组合新的“良好”数据集来逐步更新预测模型，以减少建模成本并提高预测精度。此外，引入了改进的启发式优化算法（自适应和加权中心粒子群优化（AWCPSO）），以智能地指导设计更改方向，以提高搜索效率。这样，可以以较少的实际仿真迭代和较高的优化效率自动搜索最佳设计解决方案，从而有效地支持了快速的产品优化。实验结果证明了该方法的可行性和有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号