Meta-Modelling Techniques Towards Virtual Production Intelligence
Meta-Modelling Techniques Towards Virtual Production Intelligence
复制标题
虚拟生产智能的元建模技术
DOI：
10.1007/978-3-319-12304-2_6
复制
发表时间：
2015
影响因子：
4.2
通讯作者：
T. A. Khawli
中科院分区：
文献类型：
--
作者：
W. Schulz;T. A. Khawli
高工资国家的竞争性生产决策是一项日常挑战，需要使用理性和非理性的方法。决策过程的设计是一个有趣的，跨学科的科学。然而，在理解已知的数学和程序方法对理性选择理论的使用的影响方面存在差距。遵循本杰明富兰克林的决策规则制定在伦敦1772年，他称之为“审慎代数”的含义，审慎的原因，元建模的主要成分之一，可以确定最终导致一个代数值标记的结果（标准设置）的替代决策（参数设置）。这项工作描述了元建模技术的进展，应用于多维和多标准优化激光加工，例如金属板材切割，包括快速和节俭的元模型的生成与控制误差的基础上减少数学物理或数值模型减少。简化模型是为了避免任何不必要的复杂性。元建模技术的进步基于三个主要概念：（i）分类方法，其将过程参数的空间分解成便于优化的可行区域和非可行区域，或单调区域;（ii）智能采样方法，其用于元模型的更快生成;以及（iii）使用径向基函数网络的多维插值方法，其连续映射离散，多维采样集，包含过程参数以及质量标准。通过探索虚拟生产智能的先进“驾驶舱”内的数据映射，改进了多维参数空间上的模型简化和优化。
Decision making for competitive production in high-wage countries is a daily challenge where rational and irrational methods are used. The design of decision making processes is an intriguing, discipline spanning science. However, there are gaps in understanding the impact of the known mathematical and procedural methods on the usage of rational choice theory. Following Benjamin Franklin’s rule for decision making formulated in London 1772, he called “Prudential Algebra” with the meaning of prudential reasons, one of the major ingredients of Meta-Modelling can be identified finally leading to one algebraic value labelling the results (criteria settings) of alternative decisions (parameter settings). This work describes the advances in Meta-Modelling techniques applied to multi-dimensional and multi-criterial optimization in laser processing, e.g. sheet metal cutting, including the generation of fast and frugal Meta-Models with controlled error based on model reduction in mathematical physical or numerical model reduction. Reduced Models are derived to avoid any unnecessary complexity. The advances of the Meta-Modelling technique are based on three main concepts: (i) classification methods that decomposes the space of process parameters into feasible and non-feasible regions facilitating optimization, or monotone regions (ii) smart sampling methods for faster generation of a Meta-Model, and (iii) a method for multi-dimensional interpolation using a radial basis function network continuously mapping the discrete, multi-dimensional sampling set that contains the process parameters as well as the quality criteria. Both, model reduction and optimization on a multi-dimensional parameter space are improved by exploring the data mapping within an advancing “Cockpit” for Virtual Production Intelligence.
DOI：
10.1007/978-3-319-42620-4_70
发表时间：
2016
期刊：
--
影响因子：
--
作者：
R. Reinhard;U. Eppelt;Toufik Al-Khawly;Tobias Meisen;Daniel Schilberg;W. Schulz;S. Jeschke
通讯作者：
R. Reinhard;U. Eppelt;Toufik Al-Khawly;Tobias Meisen;Daniel Schilberg;W. Schulz;S. Jeschke