船舶焊接智能系统知识建模与推理方法研究
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摘要
焊接智能化是船舶焊接技术发展的一个趋势。领域知识获取是智能系统构建中的一个瓶颈问题，基于粗糙集(Rough Set, RS)知识获取的建模方法为船舶焊接设计智能系统的研究与开发提供了一种新的思路。电弧焊接过程是一个典型的复杂过程，基于RS建模作为一种较新的方法已在焊接中得到应用，但在不确定信息处理方面，经典的RS理论和方法仍有一些未能很好解决的问题。如何扩展RS理论和方法以适应模糊值、连续值信息的处理是RS理论一个重要研究方向。
合理的推理策略对于提高智能系统求解问题的能力具有重要作用。作为近似推理领域较具代表性的推理算法，合成规则推理(Compositional Rule of Inference, CRI)和相似性推理(Similarity-basedApproximate Reasoning, SAR)在一些领域均有成功应用，但仍存在一些不足。进一步完善近似推理机制，并将扩展的模糊集理论应用于近似推理领域，已成为近年来模糊推理研究的一个热点。
本文首先分析了专家系统及智能方法建模在焊接领域的应用现状和存在问题，然后，以智能系统在知识获取和知识推理方面存在的问题为主线，研究了相应的改进和扩充方法，并将其应用于船舶焊接生产设计、船体焊接变形预报、焊接规范参数设计及焊缝成形质量预测等方面。本文的主要研究工作及创新内容有以下几个方面：
(1)应用粗糙集建模与推理的船舶焊接生产设计
将粗糙集理论和方法应用于焊接生产设计系统知识建模方面，给出基于RS获取焊接设计知识模型的方法和步骤，并提出一种基于属性重要度的推理算法。
(2)基于vague粗糙集的船体焊接变形过程建模
将经典RS与vague集相结合，提出一种基于vague粗糙集理论来获取复杂过程知识模型的方法，以船舶高强钢焊接变形过程知识模型的获取为例，介绍vague粗糙集建模方法在焊接过程知识建模中的应用。
(3)基于vague集的近似推理方法及其在焊接领域中的应用
针对现有的SAR算法中存在的问题，提出一种新的基于vague集间相似性的推理方法，以船舶高强钢焊接变形预测为例，介绍该算法在焊接变形预报领域中的应用。合成推理是近似推理领域主要方法之一，本文研究了vague环境下扩展的CRI算法，并以CO_2保护焊焊接规范参数设计为例，介绍该算法在焊接工艺设计中的应用。
(4)焊缝成形质量预测系统知识建模方法
焊缝成形过程中的系统参量大多是基于连续值属性，扩展经典RS理论处理连续值属性决策系统(Continuous-valued AttributesDecision System, CADS)中的知识获取问题是RS理论研究的一个热点。本文对这一问题进行研究，提出一种CADS知识建模方法，以焊CO_2缝成形质量预测系统建模为例，介绍该方法在焊接过程建模中的应用。
Welding intelligentization is a tendency of ship welding technology.Domain knowledge acquisition is a bottleneck problem in building anintelligence system. Knowledge modeling based on the Rough Set theory(RS) provides a new approach to the development of welding designintelligence system for shipbuilding. Arc welding is a classical complexprocess. As a new method applied in welding engineering area, modelingbased on RS has been proved applicable to welding process. However, interms of handling uncertain information, there are still some issues in thetraditional RS to be addressed. Thus, it is becoming an important researchtopic to extend the existing theories and methods of rough set to deal withfuzzy-valued or continuous-valued data.
An appropriate reasoning strategy plays an important role inimproving ability to solve problems of intelligence system. As twoinfluential inference methods in approximate reasoning field, theCompositional Rule of Inference (CRI) and the Similarity-basedApproximate Reasoning (SAR) have successful applications in somefields, but there are still some deficiencies that need to be addressed.Further improving inference mechanism, as well as applying theextensional fuzzy set theory to approximate reasoning field, has become aresearch focus of fuzzy inference in recent years.
In this dissertation, we first summarize the application status ofexpert system and intelligent modeling in welding field. Then, throughanalyzing the problems on knowledge acquisition and knowledge inference in intelligence system, we propose the related improvement andextension methods, which are applicable to welding production design ofshipbuilding, welding deformation prediction of shiphull, weldingoperational parameters design and weld formation prediction, etc. Themajor research results achieved in this dissertation are listed as follows：
(1) RS modeling and inference with application to weldingproduction planning for shipbuilding.
The Rough Set theory is introduced into knowledge modeling ofwelding production design. The procedures on obtaining knowledgemodel of welding design based on the RS method are given, and aninference algorithm based on attribute significance was provided in thisdissertation,
(2) Process modeling of welding deformation of shiphull based onthe vague rough set theory.
Combining rough set with vague set, we present an approach toobtaining knowledge model of complex process based on the vague roughset theory. Using a study case on welding deformation prediction ofmarine high tensile steel, we introduce the application of modelingmethod based on vague rough set in knowledge modeling of weldingprocess.
(3) Approximate reasoning based on vague set with application towelding field.
In view of problems in the existing SAR algorithms, we propose anovel inference method based on similarity degree of vague sets. A studycase on welding deformation prediction of marine high tensile steel isused to illustrate application of the proposed method in weldingdeformation prediction field. Compositional Rule of Inference is one ofthe popular algorithms in approximate reasoning field. We give anextensional CRI algorithm based on vague set. A study case concerningwelding procedure parameters design for carbon-dioxide arc welding isused to illustrate application of the proposed algorithm to welding processdesign.
(4) Knowledge modeling in weld formation prediction system.
The systematic parameters in weld formation process are largelybased on continuous-valued attributes. Extending the RS to handleContinuous-valued Attributes Decision System (CADS) is a current research focus in the RS research. We present a CADS modeling methodin this dissertation. A study case concerning weld formation prediction forcarbon-dioxide welding is used to illustrate application of the proposedmethod in welding process modeling.
合理的推理策略对于提高智能系统求解问题的能力具有重要作用。作为近似推理领域较具代表性的推理算法，合成规则推理(Compositional Rule of Inference, CRI)和相似性推理(Similarity-basedApproximate Reasoning, SAR)在一些领域均有成功应用，但仍存在一些不足。进一步完善近似推理机制，并将扩展的模糊集理论应用于近似推理领域，已成为近年来模糊推理研究的一个热点。
本文首先分析了专家系统及智能方法建模在焊接领域的应用现状和存在问题，然后，以智能系统在知识获取和知识推理方面存在的问题为主线，研究了相应的改进和扩充方法，并将其应用于船舶焊接生产设计、船体焊接变形预报、焊接规范参数设计及焊缝成形质量预测等方面。本文的主要研究工作及创新内容有以下几个方面：
(1)应用粗糙集建模与推理的船舶焊接生产设计
将粗糙集理论和方法应用于焊接生产设计系统知识建模方面，给出基于RS获取焊接设计知识模型的方法和步骤，并提出一种基于属性重要度的推理算法。
(2)基于vague粗糙集的船体焊接变形过程建模
将经典RS与vague集相结合，提出一种基于vague粗糙集理论来获取复杂过程知识模型的方法，以船舶高强钢焊接变形过程知识模型的获取为例，介绍vague粗糙集建模方法在焊接过程知识建模中的应用。
(3)基于vague集的近似推理方法及其在焊接领域中的应用
针对现有的SAR算法中存在的问题，提出一种新的基于vague集间相似性的推理方法，以船舶高强钢焊接变形预测为例，介绍该算法在焊接变形预报领域中的应用。合成推理是近似推理领域主要方法之一，本文研究了vague环境下扩展的CRI算法，并以CO_2保护焊焊接规范参数设计为例，介绍该算法在焊接工艺设计中的应用。
(4)焊缝成形质量预测系统知识建模方法
焊缝成形过程中的系统参量大多是基于连续值属性，扩展经典RS理论处理连续值属性决策系统(Continuous-valued AttributesDecision System, CADS)中的知识获取问题是RS理论研究的一个热点。本文对这一问题进行研究，提出一种CADS知识建模方法，以焊CO_2缝成形质量预测系统建模为例，介绍该方法在焊接过程建模中的应用。
Welding intelligentization is a tendency of ship welding technology.Domain knowledge acquisition is a bottleneck problem in building anintelligence system. Knowledge modeling based on the Rough Set theory(RS) provides a new approach to the development of welding designintelligence system for shipbuilding. Arc welding is a classical complexprocess. As a new method applied in welding engineering area, modelingbased on RS has been proved applicable to welding process. However, interms of handling uncertain information, there are still some issues in thetraditional RS to be addressed. Thus, it is becoming an important researchtopic to extend the existing theories and methods of rough set to deal withfuzzy-valued or continuous-valued data.
An appropriate reasoning strategy plays an important role inimproving ability to solve problems of intelligence system. As twoinfluential inference methods in approximate reasoning field, theCompositional Rule of Inference (CRI) and the Similarity-basedApproximate Reasoning (SAR) have successful applications in somefields, but there are still some deficiencies that need to be addressed.Further improving inference mechanism, as well as applying theextensional fuzzy set theory to approximate reasoning field, has become aresearch focus of fuzzy inference in recent years.
In this dissertation, we first summarize the application status ofexpert system and intelligent modeling in welding field. Then, throughanalyzing the problems on knowledge acquisition and knowledge inference in intelligence system, we propose the related improvement andextension methods, which are applicable to welding production design ofshipbuilding, welding deformation prediction of shiphull, weldingoperational parameters design and weld formation prediction, etc. Themajor research results achieved in this dissertation are listed as follows：
(1) RS modeling and inference with application to weldingproduction planning for shipbuilding.
The Rough Set theory is introduced into knowledge modeling ofwelding production design. The procedures on obtaining knowledgemodel of welding design based on the RS method are given, and aninference algorithm based on attribute significance was provided in thisdissertation,
(2) Process modeling of welding deformation of shiphull based onthe vague rough set theory.
Combining rough set with vague set, we present an approach toobtaining knowledge model of complex process based on the vague roughset theory. Using a study case on welding deformation prediction ofmarine high tensile steel, we introduce the application of modelingmethod based on vague rough set in knowledge modeling of weldingprocess.
(3) Approximate reasoning based on vague set with application towelding field.
In view of problems in the existing SAR algorithms, we propose anovel inference method based on similarity degree of vague sets. A studycase on welding deformation prediction of marine high tensile steel isused to illustrate application of the proposed method in weldingdeformation prediction field. Compositional Rule of Inference is one ofthe popular algorithms in approximate reasoning field. We give anextensional CRI algorithm based on vague set. A study case concerningwelding procedure parameters design for carbon-dioxide arc welding isused to illustrate application of the proposed algorithm to welding processdesign.
(4) Knowledge modeling in weld formation prediction system.
The systematic parameters in weld formation process are largelybased on continuous-valued attributes. Extending the RS to handleContinuous-valued Attributes Decision System (CADS) is a current research focus in the RS research. We present a CADS modeling methodin this dissertation. A study case concerning weld formation prediction forcarbon-dioxide welding is used to illustrate application of the proposedmethod in welding process modeling.
引文
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