基于语义表示法的智能语义特征建模研究
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摘要
在语义特征建模系统中,特征不仅封装了大量的几何信息和拓扑信息,还保存了从产品设计、分析到制造等各个阶段的所有工程信息和功能信息。因此原有的特征表示方法不仅难以满足高效管理和组织模型数据以及特征元素的需要,还增加了智能化设计的难度。
本文在研究语义特征建模系统、细胞元模型以及国内外相关理论成果的基础上,提出了一种用于语义特征建模系统的模型和特征数据表示方法。并通过该方法研究了语义特征的管理、模型知识表示、特征参数优化以及概念设计等方面的问题。主要内容包括以下几个方面：
1.为了提高语义特征建模系统保持和管理特征语义的能力,本文提出了一种基于细胞元模型和特征语义的模型表示方法。该方法通过参数集合、面创建方法、属性集合和约束集合来表示模型中的特征语义,通过使用语义面将特征的所有几何面划分成若干个具有相同或相似约束关系的集合来简化特征交互操作和有效性维护的计算模型,通过细胞元模型和语义所属列表简化了特征操作(插入、删除和修改)和布尔运算等操作,通过重新构建特征语义范围及其语义面内的所属几何面实现了特征曲面的精确细分,通过使用细胞元模型判断特征语义面的完整性来提高验证模型有效性的效率。
2.为了提高智能语义特征建模系统中模型知识积累、更新和复用的效率,提出了一种基于语义表示法的模型知识表示方法。该方法通过语义表示法和模型中特征间的约束关系建立模型的语义依赖图,通过从语义依赖图中抽取独立语义结点集合和强联系语义结点集合来生成模型的语义知识,通过动态调整强联系语义结点集合阈值来自动调整知识库中各种知识的组成比例。通过语义依赖图中当前被修改特征与行为所对应特征之间的约束关系来确定行为的类型,并根据行为类型和语义依赖图的结构生成相应的行为知识,然后通过行为转换的结果评价所使用的知识。
3.为了提高语义特征建模系统的设计效率,提出了一种基于语义表示法的特征参数优化的方法。该方法通过语义表示法来表示特征模型,通过特征的语义和模型的有效性来推理特征参数的有效范围,通过特征的子语义来评价和选择模型,通过特征子语义的交叉以及变异生成新的模型实体,并根据特征的语义来对无效模型进行过滤。
4.为了提高语义特征建模系统概念设计的效率和水平,提出了一种基于语义表示法的概念设计方法。该方法通过语义表示法和细胞元模型将特征表示成为具有n个输入和m个输出的“黑盒”,通过待求解模型输入端和输出端的关系来描述概念设计的方案,并通过“力传递”原理采用联接系统“黑盒”的方法自动生成概念设计的方案。
最后,在HUST-CAID系统上验证了论文中提出的所有算法,实验表明这些算法可以满足当前语义特征建模系统特征交互检测、有效性维护、特征参数优化以及概念设计等方面的需要。
In the Semantic Feature Modeling system, the feature not only encapsulates a great deal of geometric and topological information, but also maintains all engineering and functional information in different stages including design, analysis, manufacture and other processes of products. Thus old feature representations are hard to meet the need of high efficient management and organization of model data, and increase the difficulty of intelligent design.
Based on the study of semantic feature modeling system, cellular model and other related research achievements, this paper presents a data representation of models and features for semantic feature modeling system. And it studies the use of data representation in management of semantic features, representations of model knowledge, optimization of feature parameters and conceptual design. The main contents are as follows:
Firstly, to improve the ability of maintaining and managing the feature semanteme in semantic feature modeling system, a new model representation is presented, which is based on the cellular model and feature semanteme. It represents the feature semantemes in the model with parameter set, the surface creation function, attribute set and constraint set, simplifies the computation model of the feature interactive operations and validity maintenance via using semantic surface to divide all geometric faces in features into several sets in which faces have the same or similar constraint relations, simplifies the Boolean and features operations including insertion, deletion and modification by cellular model and semantic owner list, subdivides the feature surfaces accurately by rebuilding the scope of the feature semanteme and all geometrical faces in it, increases the efficiency of testing the model's validity by judging whether the semantic surfaces are intact with cellular model.
Secondly, to improve the efficiency of accumulation, update and reuse of model knowledge in intelligent semantic feature modeling system, an expression method of model knowledge based on semantic representation is presented. It creates the semantic dependence graph with the semantic representation and the constraint relations among features in the model, generates the model's semantic knowledge via extracting the independent semantic node set and the strong relation set from model's semantic dependence graph, adjusts the component ratio of all kinds of knowledge in knowledge base automatically via regulating the threshold of the strong relation semantic node set dynamically. And it determines the behavior type by the constraint relations between the feature modified currently and the one corresponding to behaviors, generates the behavior knowledge with the behavior type and the structures of the semantic dependence graph and then evaluates the used knowledge with results of behavior transition.
Thirdly, to improve the designing efficiency of the semantic feature modeling system, a method of feature parameter optimization based on semantic representation is presented. It represents the feature model with semantic representation, reasons the value scope of feature parameters with the features'semanteme and the model's validity, evaluates and selects models by the sub-semanteme of features, generates new model entities via swapping and mutating the features'sub-semanteme and filters the invalid models with features'semanteme.
Fourthly, to improve the efficiency and level of conceptual design in semantic feature modeling system, a semantic representation based method is presented. It represents the feature as "black box" with n input and m output, describes the plan of conceptual design with the input and output terminal of the model to be solved and generates the plan of conceptual design via connecting "black boxes" with the theory of power transfer.
At last, all algorithms have been verified on HUST-CAID system. And experiments show that these algorithms can satisfy the need of modeling with semantic feature, detecting of feature interaction, maintaining of model's validity, optimizing of feature parameters and conceptual designing very well.
本文在研究语义特征建模系统、细胞元模型以及国内外相关理论成果的基础上,提出了一种用于语义特征建模系统的模型和特征数据表示方法。并通过该方法研究了语义特征的管理、模型知识表示、特征参数优化以及概念设计等方面的问题。主要内容包括以下几个方面：
1.为了提高语义特征建模系统保持和管理特征语义的能力,本文提出了一种基于细胞元模型和特征语义的模型表示方法。该方法通过参数集合、面创建方法、属性集合和约束集合来表示模型中的特征语义,通过使用语义面将特征的所有几何面划分成若干个具有相同或相似约束关系的集合来简化特征交互操作和有效性维护的计算模型,通过细胞元模型和语义所属列表简化了特征操作(插入、删除和修改)和布尔运算等操作,通过重新构建特征语义范围及其语义面内的所属几何面实现了特征曲面的精确细分,通过使用细胞元模型判断特征语义面的完整性来提高验证模型有效性的效率。
2.为了提高智能语义特征建模系统中模型知识积累、更新和复用的效率,提出了一种基于语义表示法的模型知识表示方法。该方法通过语义表示法和模型中特征间的约束关系建立模型的语义依赖图,通过从语义依赖图中抽取独立语义结点集合和强联系语义结点集合来生成模型的语义知识,通过动态调整强联系语义结点集合阈值来自动调整知识库中各种知识的组成比例。通过语义依赖图中当前被修改特征与行为所对应特征之间的约束关系来确定行为的类型,并根据行为类型和语义依赖图的结构生成相应的行为知识,然后通过行为转换的结果评价所使用的知识。
3.为了提高语义特征建模系统的设计效率,提出了一种基于语义表示法的特征参数优化的方法。该方法通过语义表示法来表示特征模型,通过特征的语义和模型的有效性来推理特征参数的有效范围,通过特征的子语义来评价和选择模型,通过特征子语义的交叉以及变异生成新的模型实体,并根据特征的语义来对无效模型进行过滤。
4.为了提高语义特征建模系统概念设计的效率和水平,提出了一种基于语义表示法的概念设计方法。该方法通过语义表示法和细胞元模型将特征表示成为具有n个输入和m个输出的“黑盒”,通过待求解模型输入端和输出端的关系来描述概念设计的方案,并通过“力传递”原理采用联接系统“黑盒”的方法自动生成概念设计的方案。
最后,在HUST-CAID系统上验证了论文中提出的所有算法,实验表明这些算法可以满足当前语义特征建模系统特征交互检测、有效性维护、特征参数优化以及概念设计等方面的需要。
In the Semantic Feature Modeling system, the feature not only encapsulates a great deal of geometric and topological information, but also maintains all engineering and functional information in different stages including design, analysis, manufacture and other processes of products. Thus old feature representations are hard to meet the need of high efficient management and organization of model data, and increase the difficulty of intelligent design.
Based on the study of semantic feature modeling system, cellular model and other related research achievements, this paper presents a data representation of models and features for semantic feature modeling system. And it studies the use of data representation in management of semantic features, representations of model knowledge, optimization of feature parameters and conceptual design. The main contents are as follows:
Firstly, to improve the ability of maintaining and managing the feature semanteme in semantic feature modeling system, a new model representation is presented, which is based on the cellular model and feature semanteme. It represents the feature semantemes in the model with parameter set, the surface creation function, attribute set and constraint set, simplifies the computation model of the feature interactive operations and validity maintenance via using semantic surface to divide all geometric faces in features into several sets in which faces have the same or similar constraint relations, simplifies the Boolean and features operations including insertion, deletion and modification by cellular model and semantic owner list, subdivides the feature surfaces accurately by rebuilding the scope of the feature semanteme and all geometrical faces in it, increases the efficiency of testing the model's validity by judging whether the semantic surfaces are intact with cellular model.
Secondly, to improve the efficiency of accumulation, update and reuse of model knowledge in intelligent semantic feature modeling system, an expression method of model knowledge based on semantic representation is presented. It creates the semantic dependence graph with the semantic representation and the constraint relations among features in the model, generates the model's semantic knowledge via extracting the independent semantic node set and the strong relation set from model's semantic dependence graph, adjusts the component ratio of all kinds of knowledge in knowledge base automatically via regulating the threshold of the strong relation semantic node set dynamically. And it determines the behavior type by the constraint relations between the feature modified currently and the one corresponding to behaviors, generates the behavior knowledge with the behavior type and the structures of the semantic dependence graph and then evaluates the used knowledge with results of behavior transition.
Thirdly, to improve the designing efficiency of the semantic feature modeling system, a method of feature parameter optimization based on semantic representation is presented. It represents the feature model with semantic representation, reasons the value scope of feature parameters with the features'semanteme and the model's validity, evaluates and selects models by the sub-semanteme of features, generates new model entities via swapping and mutating the features'sub-semanteme and filters the invalid models with features'semanteme.
Fourthly, to improve the efficiency and level of conceptual design in semantic feature modeling system, a semantic representation based method is presented. It represents the feature as "black box" with n input and m output, describes the plan of conceptual design with the input and output terminal of the model to be solved and generates the plan of conceptual design via connecting "black boxes" with the theory of power transfer.
At last, all algorithms have been verified on HUST-CAID system. And experiments show that these algorithms can satisfy the need of modeling with semantic feature, detecting of feature interaction, maintaining of model's validity, optimizing of feature parameters and conceptual designing very well.
引文
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