支持多任务集成下料的优化下料技术研究及应用
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摘要
实施绿色制造与低碳制造是制造业减少资源消耗强度的重要途径，减少物料消耗和能源消耗是企业实施绿色制造的具体手段。优化下料技术是实现减少物料消耗的一项具体实用技术，作为控制原材料利用率的重点环节，通过采用合理的优化下料技术，对减少有限资源的浪费和碳排放起着积极而关键的作用。传统的优化下料注重于排料的数学意义上的最优，但难于解决实际工程应用中大规模多任务集成下料、特殊数据结构适应性、下料过程自动化、系统集成等一系列复杂应用需求问题。论文针对目前企业普遍采用的多工程、多任务集成下料方式，围绕支持多任务集成下料的优化下料技术及应用进行了深入研究。
首先，在分析多任务集成下料问题特点的基础上，提炼总结了支持多任务集成下料的优化下料需求，建立了多任务集成下料的优化决策过程模型；针对多任务集成下料优化决策过程中所涉及的重要支持技术，建立了支持多任务集成下料的优化下料技术体系，并以关键技术攻关作为研究重点给出了论文的解决思路。
然后，针对技术体系中的主要关键技术展开了研究，包括多任务大规模零件复杂轮廓信息的自动提取技术、多任务大规模零件的分组优化技术及基于网络的零件分组并行优化下料技术。
针对多任务集成下料中大规模零件轮廓信息提取过程复杂、提取工作量大等问题，提出了一种零件轮廓分层路由提取的MST生长算法，包括零件轮廓路由拓扑结构的构造和基于有权无向图MST的轮廓劈裂提取。其中，零件轮廓路由拓扑结构的构造包括基于拓扑投影的图纸平面化、节点路由状态定义与特征节点提取和轮廓线重组；基于有权无向图MST的轮廓劈裂提取包括无向图MST求解和轮廓线布尔劈裂。同时，为了实现下料生产对产品设计变更的快速响应，避免传统上更改传递的冗余过程，提出了基于产品模型重建的设计变更快速响应方法。
针对优化算法在处理多任务集成下料问题时易陷入时间效率和材料利用率矛盾的问题，提出了基于下料特征的多任务大规模零件分组优化策略，将零件的相似性特征和下料配合特征作为分组约束，并利用样本零件的导向作用加速零件分组过程，研究了具体的零件分组优化方法。一方面，为了克服传统无监督聚类算法中聚类中心需事前确定、聚类结果不稳定等缺点，提出了一种K-means与HCM算法相结合的大规模零件分组优化方法；另一方面，利用图论工具对零件的下料特征关联进行分析，提出了一种基于有权无向图MST的大规模零件自适应分组优化方法，并通过应用实例验证了两种方法的可行性和有效性。
针对目前不同优化方法及其优化软件对不同下料数据优化效果具有不确定性的问题，为了避免零件分组形成的下料子任务优化效果不理想而影响最终下料方案的形成，提出了一种基于网络的零件分组并行优化下料方法，通过集成多种计算资源、多种下料方法，利用开放的网络环境进行零件分组的并行优化求解，重点建立了基于网络的零件分组并行优化下料模型，并给出了零件分组并行优化下料的物理拓扑结构。
最后，以AutoCAD作为系统的图形支持平台，建立了支持多任务集成下料的优化下料系统的结构体系和功能体系，以国家863/CIMS目标产品“建筑金属结构计算机辅助设计与生产管理集成系统”中的条材/板材优化下料子系统为基础，基于上述研究结论开发了支持多任务集成下料的优化下料系统。
论文的研究内容是国家自然科学基金项目的重要组成部分，其研究结论和开发的产品是2011年度高等学校科学研究优秀成果奖科学技术进步二等奖获奖项目“支持复杂应用需求的优化下料关键技术及应用”的重要组成内容，软件系统已经在上百家企业中得到推广应用；在减少物料资源消耗、提高企业的生产制造水平方面，取得了较好的应用效果。
Implementation of green manufacturing and low-carbon manufacturing is theimportant way to reduce manufacturing resource consumption, and reducing materialconsumption and energy consumption are the specific means for enterprises toimplement green manufacturing. Cutting stock technology is a practical technology toreduce material consumption, which mainly controls the material utilization ratio.Therefore, by using rational cutting stock technology, the waste of limited resources andcarbon emissions will be greatly reduced. The traditional cutting stock technologyfocuses on the optimal material utilization ratio of pure mathematics, but it is difficult tosolve a series of complex application requirements in practical engineering application,such as integrated cutting stock, adaptability to special data structure, cutting processautomation, system integration, and so on. According to the integrated cutting stockmode commonly used by enterprises, cutting stock technologies for integrated cuttingstock and its application are studied in this thesis.
Firstly, based on the analysis of the integrated cutting stock problem(ICSP)’scharacteristics, cutting stock requirements for ICSP are summarized. Then, optimizationdecision model for ICSP is established. According to the important supportingtechnologies involved in the optimization decision process, technology system for ICSPis constructed. Solving thought for ICSP is put forward with key technologies asresearch emphasis.
Secondly, the key technologies in the technology system are studied in detail,including automatic extraction technology of large-scale parts’ complex outline,grouping optimization technology of large-scale parts and parallel optimizationtechnology of parts groups based on network.
On parts outline extraction, in order to resolve the complicated extraction processand heavy workload problems, MST growing algorithms for parts outline hierarchicalrouting extraction are proposed, which include the construction of outline routingtopology architecture and outline disassembling extraction based on MST of weightedundirected view. Construction of outline routing topology architecture involvesdrawings complanation based on topological projection, the definition of note routingstatus, the extraction of feature notes and outline restructuring. And outlinedisassembling extraction involves the solution of undirected view’s MST and outline boolean disassembling. Meanwhile, in order to realize quick response to design changein cutting stock and to avoid redundant process of traditional change transfer, quickresponse method for design change based on product model reconstruction is putforward.
On grouping optimization of large-scale parts, aimed at the contradiction of timeefficiency and material utilization ratio in ICSP, grouping optimization strategy oflarge-scale parts based on cutting stock characteristics is proposed. With the strategy,concrete parts grouping methods are further studied with parts’ similarity characteristicsand parts’ combination characteristics as grouping constraints, meanwhile, partssamples are utilized to accelerate grouping process. On the one hand, according to theshortcomings of unsupervised clustering algorithms that clustering centers should bedetermined beforehand and unstable clustering results likely occur, a parts groupingoptimization method based on K-means and HCM is put forward. On the other hand, byanalyzing the association of parts’ cutting stock characteristics with graph theory, a partsgrouping optimization method based on MST of weighted undirected view isestablished. At last, the application examples validate the feasibility and effectiveness ofthe proposed methods.
On parallel optimization of parts groups, in order to resolve unstable optimizationresults problems when different optimization methods and corresponding optimizationsystems process different data, and to avoid negative influence on final cutting solutioncaused by unsatisfactory optimization results of cutting stock subtasks, a paralleloptimization method for parts groups based on network is given. And to complete thismethod, the parallel optimization model for parts groups based on network and physicaltopological structure for parallel optimization are constructed. By integrating variouscomputing resources and cutting stock methods, the parallel optimization for partsgroups based on open network environment is realized.
Finally, by selecting AutoCAD as the system’s graphic support platform,architecture and function structure of the system supporting integrated cutting stock areconstructed. Based on cutting stock subsystem of bar and plate which belongs to863/CIMS software product called CAD and production management integrated systemfor architecture metal structure, the cutting stock system using the above researchresults is developed.
This thesis’s research contents are the important part of the project supported bythe National Natural Science Foundation, and the research results and developed system are also the important part of the winning project called cutting stock key technologiessupporting complex application requirements and its applications. This project has wonthe Second Award of the Scientific and Technological Progress of China's Ministry ofEducation. The system has been applied to hundreds of enterprises, and goodapplication effect is obtained for reducing material consumption and improvingenterprises’ manufacturing level.
首先，在分析多任务集成下料问题特点的基础上，提炼总结了支持多任务集成下料的优化下料需求，建立了多任务集成下料的优化决策过程模型；针对多任务集成下料优化决策过程中所涉及的重要支持技术，建立了支持多任务集成下料的优化下料技术体系，并以关键技术攻关作为研究重点给出了论文的解决思路。
然后，针对技术体系中的主要关键技术展开了研究，包括多任务大规模零件复杂轮廓信息的自动提取技术、多任务大规模零件的分组优化技术及基于网络的零件分组并行优化下料技术。
针对多任务集成下料中大规模零件轮廓信息提取过程复杂、提取工作量大等问题，提出了一种零件轮廓分层路由提取的MST生长算法，包括零件轮廓路由拓扑结构的构造和基于有权无向图MST的轮廓劈裂提取。其中，零件轮廓路由拓扑结构的构造包括基于拓扑投影的图纸平面化、节点路由状态定义与特征节点提取和轮廓线重组；基于有权无向图MST的轮廓劈裂提取包括无向图MST求解和轮廓线布尔劈裂。同时，为了实现下料生产对产品设计变更的快速响应，避免传统上更改传递的冗余过程，提出了基于产品模型重建的设计变更快速响应方法。
针对优化算法在处理多任务集成下料问题时易陷入时间效率和材料利用率矛盾的问题，提出了基于下料特征的多任务大规模零件分组优化策略，将零件的相似性特征和下料配合特征作为分组约束，并利用样本零件的导向作用加速零件分组过程，研究了具体的零件分组优化方法。一方面，为了克服传统无监督聚类算法中聚类中心需事前确定、聚类结果不稳定等缺点，提出了一种K-means与HCM算法相结合的大规模零件分组优化方法；另一方面，利用图论工具对零件的下料特征关联进行分析，提出了一种基于有权无向图MST的大规模零件自适应分组优化方法，并通过应用实例验证了两种方法的可行性和有效性。
针对目前不同优化方法及其优化软件对不同下料数据优化效果具有不确定性的问题，为了避免零件分组形成的下料子任务优化效果不理想而影响最终下料方案的形成，提出了一种基于网络的零件分组并行优化下料方法，通过集成多种计算资源、多种下料方法，利用开放的网络环境进行零件分组的并行优化求解，重点建立了基于网络的零件分组并行优化下料模型，并给出了零件分组并行优化下料的物理拓扑结构。
最后，以AutoCAD作为系统的图形支持平台，建立了支持多任务集成下料的优化下料系统的结构体系和功能体系，以国家863/CIMS目标产品“建筑金属结构计算机辅助设计与生产管理集成系统”中的条材/板材优化下料子系统为基础，基于上述研究结论开发了支持多任务集成下料的优化下料系统。
论文的研究内容是国家自然科学基金项目的重要组成部分，其研究结论和开发的产品是2011年度高等学校科学研究优秀成果奖科学技术进步二等奖获奖项目“支持复杂应用需求的优化下料关键技术及应用”的重要组成内容，软件系统已经在上百家企业中得到推广应用；在减少物料资源消耗、提高企业的生产制造水平方面，取得了较好的应用效果。
Implementation of green manufacturing and low-carbon manufacturing is theimportant way to reduce manufacturing resource consumption, and reducing materialconsumption and energy consumption are the specific means for enterprises toimplement green manufacturing. Cutting stock technology is a practical technology toreduce material consumption, which mainly controls the material utilization ratio.Therefore, by using rational cutting stock technology, the waste of limited resources andcarbon emissions will be greatly reduced. The traditional cutting stock technologyfocuses on the optimal material utilization ratio of pure mathematics, but it is difficult tosolve a series of complex application requirements in practical engineering application,such as integrated cutting stock, adaptability to special data structure, cutting processautomation, system integration, and so on. According to the integrated cutting stockmode commonly used by enterprises, cutting stock technologies for integrated cuttingstock and its application are studied in this thesis.
Firstly, based on the analysis of the integrated cutting stock problem(ICSP)’scharacteristics, cutting stock requirements for ICSP are summarized. Then, optimizationdecision model for ICSP is established. According to the important supportingtechnologies involved in the optimization decision process, technology system for ICSPis constructed. Solving thought for ICSP is put forward with key technologies asresearch emphasis.
Secondly, the key technologies in the technology system are studied in detail,including automatic extraction technology of large-scale parts’ complex outline,grouping optimization technology of large-scale parts and parallel optimizationtechnology of parts groups based on network.
On parts outline extraction, in order to resolve the complicated extraction processand heavy workload problems, MST growing algorithms for parts outline hierarchicalrouting extraction are proposed, which include the construction of outline routingtopology architecture and outline disassembling extraction based on MST of weightedundirected view. Construction of outline routing topology architecture involvesdrawings complanation based on topological projection, the definition of note routingstatus, the extraction of feature notes and outline restructuring. And outlinedisassembling extraction involves the solution of undirected view’s MST and outline boolean disassembling. Meanwhile, in order to realize quick response to design changein cutting stock and to avoid redundant process of traditional change transfer, quickresponse method for design change based on product model reconstruction is putforward.
On grouping optimization of large-scale parts, aimed at the contradiction of timeefficiency and material utilization ratio in ICSP, grouping optimization strategy oflarge-scale parts based on cutting stock characteristics is proposed. With the strategy,concrete parts grouping methods are further studied with parts’ similarity characteristicsand parts’ combination characteristics as grouping constraints, meanwhile, partssamples are utilized to accelerate grouping process. On the one hand, according to theshortcomings of unsupervised clustering algorithms that clustering centers should bedetermined beforehand and unstable clustering results likely occur, a parts groupingoptimization method based on K-means and HCM is put forward. On the other hand, byanalyzing the association of parts’ cutting stock characteristics with graph theory, a partsgrouping optimization method based on MST of weighted undirected view isestablished. At last, the application examples validate the feasibility and effectiveness ofthe proposed methods.
On parallel optimization of parts groups, in order to resolve unstable optimizationresults problems when different optimization methods and corresponding optimizationsystems process different data, and to avoid negative influence on final cutting solutioncaused by unsatisfactory optimization results of cutting stock subtasks, a paralleloptimization method for parts groups based on network is given. And to complete thismethod, the parallel optimization model for parts groups based on network and physicaltopological structure for parallel optimization are constructed. By integrating variouscomputing resources and cutting stock methods, the parallel optimization for partsgroups based on open network environment is realized.
Finally, by selecting AutoCAD as the system’s graphic support platform,architecture and function structure of the system supporting integrated cutting stock areconstructed. Based on cutting stock subsystem of bar and plate which belongs to863/CIMS software product called CAD and production management integrated systemfor architecture metal structure, the cutting stock system using the above researchresults is developed.
This thesis’s research contents are the important part of the project supported bythe National Natural Science Foundation, and the research results and developed system are also the important part of the winning project called cutting stock key technologiessupporting complex application requirements and its applications. This project has wonthe Second Award of the Scientific and Technological Progress of China's Ministry ofEducation. The system has been applied to hundreds of enterprises, and goodapplication effect is obtained for reducing material consumption and improvingenterprises’ manufacturing level.
引文
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