基于知识的大型机电产品制造能耗模型及预测的研究
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摘要
产品制造过程中的节能潜力很大,在大力加强技术节能、依靠技术进步降低单位产品能耗的同时,还需要对能源消耗进行精细管理,以保证节能效果发挥到最大程度。目前,离散制造业的能源管理方式还很不完善,需要由经验管理向现代化管理转变。通过建立产品制造过程的能源消耗模型,可以理清能耗的来源及组成,在此基础上减少或消除不必要的能源消耗;还可以指导能耗数据与加工过程相对应,从而为基于知识的能耗预测打下基础;在能耗模型指导下开发能源管理系统,可以使能源管理工作更具有科学性、合理性和实用性。另一方面,对于难以用数学方法精确估算产品制造过程中能耗值的问题,随着人工智能及计算机技术的发展,用基于知识的方法来解决成为可能。能耗估算结果是能源管理的基础,同时可以为工艺方案评价及调度等决策活动提供支持,也是绿色制造等研究中能源消耗研究的基础,因此,本文进行了产品制造过程能耗模型及基于知识的能耗预测的研究。全文的研究内容主要包括以下几个部分:
建立了产品制造过程的能耗模型。通过对制造过程中几个概念的分析,明确本文面向能耗的研究体系。接着分析了产品制造过程中的能源消耗,并建立了产品制造过程中能耗的数据模型,包括提出能耗模型要解决的问题;对能源消耗方面的一些术语进行定义;对术语及能耗控制的规则进行形式化等内容。其中术语定义包括机械加工工艺过程中工序能耗的各个定义,零件机械加工过程中的能耗及产品制造过程中的能耗等的定义。分析了产品制造过程能耗模型的作用,它可以为能耗的精细管理提供支持。
提出了产品制造过程能耗知识的语义模型及其表示方法。能耗信息采集后,与工艺实例及切削参数等结合后形成为能耗知识,通过明确与能源消耗有关的各种概念及其关系,建立了产品制造过程和机械加工过程能耗知识的语义模型。通过对几种承载语义模型的语义表示语言的比较之后,以OWL语言为基础来表示与能耗相关的概念实体集、概念实体属性集及关联集。对能耗相关概念体系中的公理和约束,以制造对象为例,进行了一阶逻辑表示的研究。接着提出了机械加工工艺过程中能耗相关信息的层次结构。
提出了基于实例推理的工艺过程能耗预测方法。首先建立了基于CBR的零件机械加工工艺过程能耗预测的过程模型。接着针对能耗预测的特点,提出了基于所属产品的检索、面向零件的检索和基于机床设备的匹配检索三个主要步骤的实例检索过程,并采用应用事务特性表技术的利用相似零件加工能耗结果的估算方法。以汽轮机转子加工为例,验证了上述方法的可行性及有效性。
提出了工序层次上基于神经网络的能耗预测方法。首先,通过对单位体积金属去除量的能耗公式的分析,知道能源消耗与很多具体条件相关,很难精确计算出,而且机床和刀具等一经确定,切削用量的选择就成为影响能源消耗的关键。在分析了BP神经网络的本质后,建立了能耗预测的神经网络模型,说明了输入变量及输出变量的选取及其归一化处理,并进行了隐含层节点数和传递函数的选取。以各切削用量组合及其对应能源消耗的历史数据作为神经网络训练的样本集,建立切削用量组合方案输入和能源消耗输出间的非线性关系,从而对新的切削用量参数组合进行能耗值的预测。以导叶片的粗铣加工为例,验证了该能耗预测方法的有效性。
在能耗模型和能耗知识语义模型的指导下进行了离散制造业能耗管理原型系统的开发。在分析了离散制造企业在能源管理方面的特点后,根据功能的独立化、模块化的设计思想,提出了包括数据采集层、数据管理层、用户服务层三个层次的系统总体层次结构。指明了软件系统的工作流程,并从产品能耗管理、设备能耗管理、能耗信息管理、能源消耗的预测和系统管理五个方面进行系统的实现。该系统有利于理清能源消耗的脉络,找出降低能源消耗的方向,并促进能耗数据的积累。最后,对本文提出的理论方法在某企业的应用进行了分析。
There are great potentialities of energy saving in product manufacturing process, for one hand, energy saving by technology improvement should be strengthened to reduce energy consumption of per unit product, for the other hand, fine energy management is also an urgent need to make energy saving effect come to a great extent. Nowadays, energy management method in discrete manufactures is not perfect and should be transformed from experience management to modern management. After energy consumption modeling in product manufacturing process, the source and component of energy consumption may be explicit to reduce or eliminate energy waste in the enterprise, the established model could serve as the guidance for relating energy consumption data with the mechanical process to be the basis for energy consumption prediction based on knowledge, and the model also could serve as the guidance for the development of energy management system to make the energy management work scientific, rational and practicable. With the development of artificial intelligence and computer technology, for the problem that the energy consumption result could not be calculate accurately by mathematical method, it is possible to solve it by the knowledge-based method to estimate the energy consumption in product manufacturing process. The result of calculation and prediction of energy consumption could be used to aid process planning evaluation and workshop scheduling, and it is the basis for fine energy management and the research of green manufacturing. Research on energy consumption model and research on estimation of energy consumption in manufacturing process based on knowledge were made in this dissertation and the research works include:
An energy consumption model in product manufacturing process is built up. After some concepts in manufacturing process analyzed, the research system faced energy consumption of the dissertation is presented. Then, energy consumption in product manufacturing process is analyzed, and the energy consumption data model is constructed, the procedure includes: the questions that energy consumption model must be able to answer are presented; the terms of energy consumption are defined, the terms include definitions of energy consumption in machining procedure, definitions of energy consumption in part mechanical manufacturing process and product manufacturing process etc; the definitions and constraints on the terms and energy consumption control are specified in a formal way etc. The effect of the energy consumption model is analyzed, and the model could support fine energy management effectively.
A semantic model of energy consumption knowledge and its semantic representation are proposed. Energy consumption knowledge comes from energy consumption information collected, which is associated with process plan and operating parameters afterwards. By explicating the concepts in energy consumption and their relationship, the semantic models of energy consumption knowledge in product manufacturing process and part machining process are presented. After comparing several semantic representation languages, OWL is used to describe entities, properties and relationships of concepts which are related with energy consumption. Axioms and constraints for system of energy consumption concepts are represented in First Order Logic by example of manufacture object. Then, information hierarchy of mechanical machining process is presented.
The estimation method of energy consumption in mechanical process layer based on CBR is presented. First, the CBR-based process model of estimation of energy consumption in technical process is presented. Then, a case retrieval process is presented in view of the characteristics of energy consumption, and the retrieval process includes three main steps: retrieval based on affiliation product, part-oriented retrieval, matching and retrieval based on machine tools. The energy consumption results of retrieval similar cases are used to estimate energy consumption of new part by using layouts of article characteristics technology. The feasibility and effectiveness of the method is finally verified by the machining process of turbine rotor.
The estimation method of energy consumption in machining procedure layer based on neural network is presented. First, after analyzing the expression of energy consumed per unit volume of metal removed, we know that energy consumption is related with real-life condition and is difficult to calculate accurately, and the choice of cutting parameter is the key factor of energy consumption for certain machine and tool. After analyzing the essence of neural network, neural network model of energy consumption prediction is built up, the choice and unitary of input variables and output variables are illustrated, then, the node number of latent layer and transfer function are selected. The combination of cutting parameter and the corresponding history data of energy consumption are served as training data set, and the nonlinear mapping relation of the combination of cutting parameter with corresponding energy consumption is setup, thus, energy consumption of the new combination of cutting parameter is estimated. The effectiveness of the prediction method is verified by the example of rough machining process of a guide blade.
A prototype system of energy management in discrete manufacture is developed based on the guidance of energy consumption model and semantic model of energy consumption knowledge. After analyzing the characteristic of energy management in discrete manufacture, overall structure of system is presented which is constructed of three layers including data collection layer, data management layer and user service layer based on the design idea of function independence and modularization. The work procedure of prototype system is illustrated, and the system is realized from five aspects including product energy consumption management, machine tool energy consumption management, energy consumption information management, energy consumption estimation and system administration. By using the prototype system, it is benefit to make energy consumption clear, to find the way to reduce energy consumption, to promote the collection of energy consumption data. Lastly, the application of the method proposed in this paper in an enterprise is analyzed.
建立了产品制造过程的能耗模型。通过对制造过程中几个概念的分析,明确本文面向能耗的研究体系。接着分析了产品制造过程中的能源消耗,并建立了产品制造过程中能耗的数据模型,包括提出能耗模型要解决的问题;对能源消耗方面的一些术语进行定义;对术语及能耗控制的规则进行形式化等内容。其中术语定义包括机械加工工艺过程中工序能耗的各个定义,零件机械加工过程中的能耗及产品制造过程中的能耗等的定义。分析了产品制造过程能耗模型的作用,它可以为能耗的精细管理提供支持。
提出了产品制造过程能耗知识的语义模型及其表示方法。能耗信息采集后,与工艺实例及切削参数等结合后形成为能耗知识,通过明确与能源消耗有关的各种概念及其关系,建立了产品制造过程和机械加工过程能耗知识的语义模型。通过对几种承载语义模型的语义表示语言的比较之后,以OWL语言为基础来表示与能耗相关的概念实体集、概念实体属性集及关联集。对能耗相关概念体系中的公理和约束,以制造对象为例,进行了一阶逻辑表示的研究。接着提出了机械加工工艺过程中能耗相关信息的层次结构。
提出了基于实例推理的工艺过程能耗预测方法。首先建立了基于CBR的零件机械加工工艺过程能耗预测的过程模型。接着针对能耗预测的特点,提出了基于所属产品的检索、面向零件的检索和基于机床设备的匹配检索三个主要步骤的实例检索过程,并采用应用事务特性表技术的利用相似零件加工能耗结果的估算方法。以汽轮机转子加工为例,验证了上述方法的可行性及有效性。
提出了工序层次上基于神经网络的能耗预测方法。首先,通过对单位体积金属去除量的能耗公式的分析,知道能源消耗与很多具体条件相关,很难精确计算出,而且机床和刀具等一经确定,切削用量的选择就成为影响能源消耗的关键。在分析了BP神经网络的本质后,建立了能耗预测的神经网络模型,说明了输入变量及输出变量的选取及其归一化处理,并进行了隐含层节点数和传递函数的选取。以各切削用量组合及其对应能源消耗的历史数据作为神经网络训练的样本集,建立切削用量组合方案输入和能源消耗输出间的非线性关系,从而对新的切削用量参数组合进行能耗值的预测。以导叶片的粗铣加工为例,验证了该能耗预测方法的有效性。
在能耗模型和能耗知识语义模型的指导下进行了离散制造业能耗管理原型系统的开发。在分析了离散制造企业在能源管理方面的特点后,根据功能的独立化、模块化的设计思想,提出了包括数据采集层、数据管理层、用户服务层三个层次的系统总体层次结构。指明了软件系统的工作流程,并从产品能耗管理、设备能耗管理、能耗信息管理、能源消耗的预测和系统管理五个方面进行系统的实现。该系统有利于理清能源消耗的脉络,找出降低能源消耗的方向,并促进能耗数据的积累。最后,对本文提出的理论方法在某企业的应用进行了分析。
There are great potentialities of energy saving in product manufacturing process, for one hand, energy saving by technology improvement should be strengthened to reduce energy consumption of per unit product, for the other hand, fine energy management is also an urgent need to make energy saving effect come to a great extent. Nowadays, energy management method in discrete manufactures is not perfect and should be transformed from experience management to modern management. After energy consumption modeling in product manufacturing process, the source and component of energy consumption may be explicit to reduce or eliminate energy waste in the enterprise, the established model could serve as the guidance for relating energy consumption data with the mechanical process to be the basis for energy consumption prediction based on knowledge, and the model also could serve as the guidance for the development of energy management system to make the energy management work scientific, rational and practicable. With the development of artificial intelligence and computer technology, for the problem that the energy consumption result could not be calculate accurately by mathematical method, it is possible to solve it by the knowledge-based method to estimate the energy consumption in product manufacturing process. The result of calculation and prediction of energy consumption could be used to aid process planning evaluation and workshop scheduling, and it is the basis for fine energy management and the research of green manufacturing. Research on energy consumption model and research on estimation of energy consumption in manufacturing process based on knowledge were made in this dissertation and the research works include:
An energy consumption model in product manufacturing process is built up. After some concepts in manufacturing process analyzed, the research system faced energy consumption of the dissertation is presented. Then, energy consumption in product manufacturing process is analyzed, and the energy consumption data model is constructed, the procedure includes: the questions that energy consumption model must be able to answer are presented; the terms of energy consumption are defined, the terms include definitions of energy consumption in machining procedure, definitions of energy consumption in part mechanical manufacturing process and product manufacturing process etc; the definitions and constraints on the terms and energy consumption control are specified in a formal way etc. The effect of the energy consumption model is analyzed, and the model could support fine energy management effectively.
A semantic model of energy consumption knowledge and its semantic representation are proposed. Energy consumption knowledge comes from energy consumption information collected, which is associated with process plan and operating parameters afterwards. By explicating the concepts in energy consumption and their relationship, the semantic models of energy consumption knowledge in product manufacturing process and part machining process are presented. After comparing several semantic representation languages, OWL is used to describe entities, properties and relationships of concepts which are related with energy consumption. Axioms and constraints for system of energy consumption concepts are represented in First Order Logic by example of manufacture object. Then, information hierarchy of mechanical machining process is presented.
The estimation method of energy consumption in mechanical process layer based on CBR is presented. First, the CBR-based process model of estimation of energy consumption in technical process is presented. Then, a case retrieval process is presented in view of the characteristics of energy consumption, and the retrieval process includes three main steps: retrieval based on affiliation product, part-oriented retrieval, matching and retrieval based on machine tools. The energy consumption results of retrieval similar cases are used to estimate energy consumption of new part by using layouts of article characteristics technology. The feasibility and effectiveness of the method is finally verified by the machining process of turbine rotor.
The estimation method of energy consumption in machining procedure layer based on neural network is presented. First, after analyzing the expression of energy consumed per unit volume of metal removed, we know that energy consumption is related with real-life condition and is difficult to calculate accurately, and the choice of cutting parameter is the key factor of energy consumption for certain machine and tool. After analyzing the essence of neural network, neural network model of energy consumption prediction is built up, the choice and unitary of input variables and output variables are illustrated, then, the node number of latent layer and transfer function are selected. The combination of cutting parameter and the corresponding history data of energy consumption are served as training data set, and the nonlinear mapping relation of the combination of cutting parameter with corresponding energy consumption is setup, thus, energy consumption of the new combination of cutting parameter is estimated. The effectiveness of the prediction method is verified by the example of rough machining process of a guide blade.
A prototype system of energy management in discrete manufacture is developed based on the guidance of energy consumption model and semantic model of energy consumption knowledge. After analyzing the characteristic of energy management in discrete manufacture, overall structure of system is presented which is constructed of three layers including data collection layer, data management layer and user service layer based on the design idea of function independence and modularization. The work procedure of prototype system is illustrated, and the system is realized from five aspects including product energy consumption management, machine tool energy consumption management, energy consumption information management, energy consumption estimation and system administration. By using the prototype system, it is benefit to make energy consumption clear, to find the way to reduce energy consumption, to promote the collection of energy consumption data. Lastly, the application of the method proposed in this paper in an enterprise is analyzed.
引文
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53 Gruninger, M., Fox, M.S. The Logic of Enterprise Modeling, Modeling and Methodologies for Enterprise Integration. Cornwall, Great Britain: Chapman and Hall, 1996
54 Michael Gruninger, Mark S. Fox. An Activity Ontology for Enterprise Modeling. AAAI-94 June 1, 1994:1-13
55 Gruninger, M., Fox, M.S. Methodology for the Design and Evaluation of Ontologies. Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95, Montreal, 1995
56 Mike Uschold, Martin King, Stuart Moralee, Yannis Zorgios.The Enterprise Ontology. AIAI, the University of Edinburgh, 1997:1-69
57邓志鸿,唐世渭,杨冬青.面向语义集成—本体在Web信息集成中的研究进展.计算机应用. 2002, 22 (1):15-17
58金芝,陆汝铃,陈钢.基于领域知识重用的虚拟领域本体构造.软件学报. 2003, 14 (3):350-355
59武成岗,焦文品,史忠植.基于本体论和多主体的信息检索服务器.计算机研究与发展. 2001, 38 (6) : 662-667
60 Costas Tsatsoul, R. L. Kashyap. Case-Based Reasoning and Learning in Manufacturing with the TOLTEC Planner. TEES TRANSACTION ON SMC,1993, 23 (4):1010-1023
61 Yang H, Lu WF, Lin AC. Process:A Case-Based Progress Planning System for Machining of Rotational Part. Journal of Intelligent Manufacturing. 1994,(5):411-430
62 Olga A. Nikolaychuk, Alexander Y. Yurin. Computer-aided Identification of Mechanical System’s Technical State with the Aid of Case-based Reasoning. Expert Systems with Applications. 2008,34(1):635-642
63 Sycara. K. CADET: A Case-based Synthesis Tool for Engineering Design. International Journal for Expert System. 1992,4 (2): 67-88
64 C.K.Kwong, S.M.Tam. Case-based Reasoning Approach to Concurrent Design of Low Power Transformers. Journal of Materials Processing Technology. 2002(128): 136-141
65 C.B.Cheng. A Fuzzy Inference System for Similarity Assessment in Case-based Reasoning Systems: An application to Product Design. Mathematical and Computer Modeling. 2003 (38): 358-394
66 Hui Wang, Yiming (Kevin) Rong. Case-based Reasoning Method for Computer Aided Welding Fixture Design. Computer-Aided Design. 2008,40(12):1121- 1132
67 Negny Stéphane, Le Lann Jean Marc. Case-based Reasoning for Chemical Engineering Design. Chemical Engineering Research and Design. 2008,86(6) : 648-658
68 Pei-Chann Chang, Chen-Hao Liu, Robert K. Lai. A Fuzzy Case-based Reasoning Model for Sales Forecasting in Print Circuit Board Industries. Expert Systems with Applications.2008,34(3): 2049-2058
69 Muh-Cherng Wu, Ying-Fu Lo, Shang-Hwa Hsu. A fuzzy CBR Technique for Generating Product Ideas. Expert Systems with Applications. 2008,34(1):530- 540
70 Sung-Hoon An, Gwang-Hee Kim, Kyung-In Kang. A Case-based Reasoning Cost Estimating Model Using Experience by Analytic Hierarchy Process. Building and Environment. 2007,42(7): 2573-2579
71 Y.F. Li, M. Xie, T.N. Goh. A Study of Mutual Information based Feature Selection for Case-based Reasoning in Software Cost Estimation. Expert Systems with Applications.2009,36(3), Part 2:5921-5931
72 Huey-Jiun Wang, Chien-Wei Chiou, Yi-Kai Juan. Decision Support Model based on Case-based Reasoning Approach for Estimating the Restoration Budget of Historical Buildings. Expert Systems with Applications. 2008, 35(4):1601- 1610
73 Irem Dikmen, M. Talat Birgonul, A. Kemal Gur. A Case-based Decision Support Tool for Bid Mark-up Estimation of International Construction Projects. Automation in Construction.2007,17(1): 30-44
74蔡文沁,彭培林,姜寿山.航空产品设计知识的表示与重用技术研究.计算机集成制造系统. 2004, 10 (1):55-58
75侯俊杰,莫蓉等.基于实例设计的夹具CAD研究.计算机工程与应用. 2002, (6):43-44
76刘晓冰,刘彩燕,马跃,蒙秋男.基于分层实例推理的混合型行业工艺设计系统研究.计算机集成制造系统. 2005, 11(7):941- 946,1013
77黄进,江平宇.工艺设计推理的神经网络索引模型的研究.西安交通大学学报.1995, 30 (7): 12-18
78谭建荣,刑建国.实例检索策略及其在减速器零件CAPP中的应用.中国机械工程. 2000, 11(9): 1064-1066
79杨志刚,马自勤,刘成颖,王先逵.基于零件特性表和关键机床的工艺实例检索.计算机集成制造系统. 2003, 9(7): 556-560
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86 Abdullah Kurt. Modelling of the Cutting Tool Stresses in Machining of Inconel 718 Using Artificial Neural Networks. Expert Systems with Applications. 2009, 36(6):9645-9657
87 Chang-Chun Zhou, Guo-Fu Yin, Xiao-Bing Hu. Multi-objective Optimization of Material Selection for Sustainable Products: Artificial Neural Networks and Genetic Algorithm Approach. Materials & Design. 2009,30(4):1209-1215
88包洋,李蓓智,杨建国.基于数据挖掘的工艺参数优化研究.微计算机信息. 2006,22(9):245-247
89 A.R. Shahani, S. Setayeshi, S.A. Nodamaie, M.A. Asadi, S. Rezaie. Prediction of Influence Parameters on the Hot Rolling Process Using Finite Element Method and Neural Network. Journal of Materials Processing Technology. 2009, 209(4): 1920-1935
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91 Atakan Kurt, Betul Gulbagci, Ferhat Karaca, Omar Alagha. An Online Air Pollution Forecasting System Using Neural Networks. Environment International. 2008,34(5):592-598
92 A. Azadeh, S.F. Ghaderi, S. Sohrabkhani. A Simulated-based Neural Network Algorithm for Forecasting Electrical Energy Consumption in Iran. Energy Policy. 2008,36(7): 2637-2644
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52 Fox, M.S., Chionglo, J., Fadel, F. A Common-Sense Model of the Enterprise. Proceedings of the Industrial Engineering Research Conference, 1993:425-429
53 Gruninger, M., Fox, M.S. The Logic of Enterprise Modeling, Modeling and Methodologies for Enterprise Integration. Cornwall, Great Britain: Chapman and Hall, 1996
54 Michael Gruninger, Mark S. Fox. An Activity Ontology for Enterprise Modeling. AAAI-94 June 1, 1994:1-13
55 Gruninger, M., Fox, M.S. Methodology for the Design and Evaluation of Ontologies. Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95, Montreal, 1995
56 Mike Uschold, Martin King, Stuart Moralee, Yannis Zorgios.The Enterprise Ontology. AIAI, the University of Edinburgh, 1997:1-69
57邓志鸿,唐世渭,杨冬青.面向语义集成—本体在Web信息集成中的研究进展.计算机应用. 2002, 22 (1):15-17
58金芝,陆汝铃,陈钢.基于领域知识重用的虚拟领域本体构造.软件学报. 2003, 14 (3):350-355
59武成岗,焦文品,史忠植.基于本体论和多主体的信息检索服务器.计算机研究与发展. 2001, 38 (6) : 662-667
60 Costas Tsatsoul, R. L. Kashyap. Case-Based Reasoning and Learning in Manufacturing with the TOLTEC Planner. TEES TRANSACTION ON SMC,1993, 23 (4):1010-1023
61 Yang H, Lu WF, Lin AC. Process:A Case-Based Progress Planning System for Machining of Rotational Part. Journal of Intelligent Manufacturing. 1994,(5):411-430
62 Olga A. Nikolaychuk, Alexander Y. Yurin. Computer-aided Identification of Mechanical System’s Technical State with the Aid of Case-based Reasoning. Expert Systems with Applications. 2008,34(1):635-642
63 Sycara. K. CADET: A Case-based Synthesis Tool for Engineering Design. International Journal for Expert System. 1992,4 (2): 67-88
64 C.K.Kwong, S.M.Tam. Case-based Reasoning Approach to Concurrent Design of Low Power Transformers. Journal of Materials Processing Technology. 2002(128): 136-141
65 C.B.Cheng. A Fuzzy Inference System for Similarity Assessment in Case-based Reasoning Systems: An application to Product Design. Mathematical and Computer Modeling. 2003 (38): 358-394
66 Hui Wang, Yiming (Kevin) Rong. Case-based Reasoning Method for Computer Aided Welding Fixture Design. Computer-Aided Design. 2008,40(12):1121- 1132
67 Negny Stéphane, Le Lann Jean Marc. Case-based Reasoning for Chemical Engineering Design. Chemical Engineering Research and Design. 2008,86(6) : 648-658
68 Pei-Chann Chang, Chen-Hao Liu, Robert K. Lai. A Fuzzy Case-based Reasoning Model for Sales Forecasting in Print Circuit Board Industries. Expert Systems with Applications.2008,34(3): 2049-2058
69 Muh-Cherng Wu, Ying-Fu Lo, Shang-Hwa Hsu. A fuzzy CBR Technique for Generating Product Ideas. Expert Systems with Applications. 2008,34(1):530- 540
70 Sung-Hoon An, Gwang-Hee Kim, Kyung-In Kang. A Case-based Reasoning Cost Estimating Model Using Experience by Analytic Hierarchy Process. Building and Environment. 2007,42(7): 2573-2579
71 Y.F. Li, M. Xie, T.N. Goh. A Study of Mutual Information based Feature Selection for Case-based Reasoning in Software Cost Estimation. Expert Systems with Applications.2009,36(3), Part 2:5921-5931
72 Huey-Jiun Wang, Chien-Wei Chiou, Yi-Kai Juan. Decision Support Model based on Case-based Reasoning Approach for Estimating the Restoration Budget of Historical Buildings. Expert Systems with Applications. 2008, 35(4):1601- 1610
73 Irem Dikmen, M. Talat Birgonul, A. Kemal Gur. A Case-based Decision Support Tool for Bid Mark-up Estimation of International Construction Projects. Automation in Construction.2007,17(1): 30-44
74蔡文沁,彭培林,姜寿山.航空产品设计知识的表示与重用技术研究.计算机集成制造系统. 2004, 10 (1):55-58
75侯俊杰,莫蓉等.基于实例设计的夹具CAD研究.计算机工程与应用. 2002, (6):43-44
76刘晓冰,刘彩燕,马跃,蒙秋男.基于分层实例推理的混合型行业工艺设计系统研究.计算机集成制造系统. 2005, 11(7):941- 946,1013
77黄进,江平宇.工艺设计推理的神经网络索引模型的研究.西安交通大学学报.1995, 30 (7): 12-18
78谭建荣,刑建国.实例检索策略及其在减速器零件CAPP中的应用.中国机械工程. 2000, 11(9): 1064-1066
79杨志刚,马自勤,刘成颖,王先逵.基于零件特性表和关键机床的工艺实例检索.计算机集成制造系统. 2003, 9(7): 556-560
80王俊彪,盛威.基于实例的飞机饭金工艺设计.机械科学与技术. 2004, 23(6):675-678
81许之伟,刘文剑.工艺设计中支持事例推理的扩展对象模型.计算机集成制造系统. 2002, 8(2): 115-121
82许之伟,刘永贤,刘文剑.工艺设计中的事例表达与相似计算.中国制造业信息化.2004, 33 (1): 102-104
83 R .Agrawal,T.Imielinski, A. S. Mining Association Rules between Sets of Items in Large Databases. ACM SIGMOD International Conf. of Management of Data, Washington,D.C. 1993: 207-216
84 Myon-Woong Park, Hyung-Min Rho, Ryoung-Tae Park. Generation of Modified Cutting Using Neural Network for an Operation Planning System. Annals of the CIRP,1996,45(1):5-8
85陈杰,罗红波,赵武.基于人工神经网络方法的切削用量智能选择系统.工具技术.2003,37(10):17-19
86 Abdullah Kurt. Modelling of the Cutting Tool Stresses in Machining of Inconel 718 Using Artificial Neural Networks. Expert Systems with Applications. 2009, 36(6):9645-9657
87 Chang-Chun Zhou, Guo-Fu Yin, Xiao-Bing Hu. Multi-objective Optimization of Material Selection for Sustainable Products: Artificial Neural Networks and Genetic Algorithm Approach. Materials & Design. 2009,30(4):1209-1215
88包洋,李蓓智,杨建国.基于数据挖掘的工艺参数优化研究.微计算机信息. 2006,22(9):245-247
89 A.R. Shahani, S. Setayeshi, S.A. Nodamaie, M.A. Asadi, S. Rezaie. Prediction of Influence Parameters on the Hot Rolling Process Using Finite Element Method and Neural Network. Journal of Materials Processing Technology. 2009, 209(4): 1920-1935
90 Chang Y.W. Wang C.Y. Tsai. Evolving Neural Network for Printed Circuit Board Sales Forecasting. Expert Systems with Applications. 2005,29: 83-92
91 Atakan Kurt, Betul Gulbagci, Ferhat Karaca, Omar Alagha. An Online Air Pollution Forecasting System Using Neural Networks. Environment International. 2008,34(5):592-598
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