Article(id=1149768939552555110, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768937925165147, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2404142, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717430400000, receivedDateStr=2024-06-04, revisedDate=1728835200000, revisedDateStr=2024-10-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1752055876863, onlineDateStr=2025-07-09, pubDate=1748361600000, pubDateStr=2025-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752055876863, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752055876863, creator=13701087609, updateTime=1752055876863, updator=13701087609, issue=Issue{id=1149768937925165147, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='15', pageStart='6155', pageEnd='6586', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752055876475, creator=13701087609, updateTime=1768456822194, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559490207699090, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768937925165147, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559490211893395, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768937925165147, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=6538, endPage=6547, ext={EN=ArticleExt(id=1149768939934236778, articleId=1149768939552555110, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Automatic Compilation Method of Aeronautical Typical Parts Inspection Plan Based on MBD, columnId=1156262731079607234, journalTitle=Science Technology and Engineering, columnName=Papers·Aeronautics and Astronautics, runingTitle=null, highlight=null, articleAbstract=

Aiming at the current aviation typical parts with complex structure and high precision requirements, increased difficulty in quality inspection but backward inspection methods, an inspection plan automatic preparation method was proposed. Firstly, from the information reuse and information integration, the inspection number model was constructed on the basis of the design model, which layed a foundation of data source for the generation of the inspection plan. Secondly, the production method of the inspection plan was formulated, and the rules of the inspection sequence generation were designed, to achieve the automatic generation of inspection sequence number by using the improved Dynamic planning algorithm. Finally, combined with the CAA(component application architecture) secondary development interface and Excel component development, the corresponding function module was developed under the CATIA environment, to achieve the output of the inspection element information and process view, to complete the automatic preparation and export of the inspection plan. Through examples, it is proved that the method can reduce the labour intensity of inspectors and improve the efficiency of the preparation of inspection plans.

, correspAuthors=Fa-lin WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Chao-yang GUO, Ying-ji JIANG, Hua-long CHENG, Zhi-bin YU, Fa-lin WANG), CN=ArticleExt(id=1149768962394735151, articleId=1149768939552555110, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于MBD的航空典型零件检验计划自动编制方法, columnId=1156262731373208516, journalTitle=科学技术与工程, columnName=论文·航空、航天, runingTitle=null, highlight=null, articleAbstract=

针对当前航空典型零件结构复杂,精度要求高,质量检测难度加大但检验方法落后的现状,提出了一种检验计划自动编制方法。首先,从信息重用和信息集成出发,以设计模型为基础构建检验数模,为检验计划的生成奠定数据源基础,其次,制定检验计划生成方法,设计检测顺序生成规则,采用改进的动态规划算法实现检测顺序编号的自动生成;最后,结合组件应用架构(component application architecture,CAA)二次开发接口与Excel组件开发,在CATIA环境下开发了相应的功能模块,实现了检测要素信息与工艺视图输出,完成检验计划的自动编制与导出。通过实例证明,该方法可以降低检测人员的劳动强度,提高检验计划的编制效率。

, correspAuthors=王发麟, authorNote=null, correspAuthorsNote=
* 王发麟(1986—),男,汉族,江西吉安人,博士,讲师。研究方向:数字化设计与制造。E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=8s4AAlaeOMUHldhrba2+LA==, magXml=lBLXU83tkBary5GjVvCyQg==, pdfUrl=null, pdf=8kftUqvf5N7arA8Mr7yArQ==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=UTF17yH+PLkWl7ZWR1KxTA==, mapNumber=null, authorCompany=null, fund=null, authors=

郭朝阳(1985—),男,汉族,河南信阳人,高级工程师。研究方向:航空产品检测及质量控制。E-mail:

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郭朝阳(1985—),男,汉族,河南信阳人,高级工程师。研究方向:航空产品检测及质量控制。E-mail:

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郭朝阳(1985—),男,汉族,河南信阳人,高级工程师。研究方向:航空产品检测及质量控制。E-mail:

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基于MBD的航空典型零件检验计划自动编制方法
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郭朝阳 1 , 蒋英吉 2 , 程华龙 1 , 余志斌 1 , 王发麟 2, *
科学技术与工程 | 论文·航空、航天 2025,25(15): 6538-6547
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科学技术与工程 | 论文·航空、航天 2025, 25(15): 6538-6547
基于MBD的航空典型零件检验计划自动编制方法
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郭朝阳1 , 蒋英吉2, 程华龙1, 余志斌1, 王发麟2, *
作者信息
  • 1 航空工业江西洪都航空工业集团有限责任公司检验中心/无损检测中心, 南昌 330024
  • 2 南昌航空大学航空制造工程学院, 南昌 330063
  • 郭朝阳(1985—),男,汉族,河南信阳人,高级工程师。研究方向:航空产品检测及质量控制。E-mail:

通讯作者:

* 王发麟(1986—),男,汉族,江西吉安人,博士,讲师。研究方向:数字化设计与制造。E-mail:
Automatic Compilation Method of Aeronautical Typical Parts Inspection Plan Based on MBD
Chao-yang GUO1 , Ying-ji JIANG2, Hua-long CHENG1, Zhi-bin YU1, Fa-lin WANG2, *
Affiliations
  • 1 Center of Inspection & Nondestructive Testing, Jiangxi Hongdu Aviation Industry Group Limited Liability Company, Nanchang 330024, China
  • 2 School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063, China
出版时间: 2025-05-28 doi: 10.12404/j.issn.1671-1815.2404142
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针对当前航空典型零件结构复杂,精度要求高,质量检测难度加大但检验方法落后的现状,提出了一种检验计划自动编制方法。首先,从信息重用和信息集成出发,以设计模型为基础构建检验数模,为检验计划的生成奠定数据源基础,其次,制定检验计划生成方法,设计检测顺序生成规则,采用改进的动态规划算法实现检测顺序编号的自动生成;最后,结合组件应用架构(component application architecture,CAA)二次开发接口与Excel组件开发,在CATIA环境下开发了相应的功能模块,实现了检测要素信息与工艺视图输出,完成检验计划的自动编制与导出。通过实例证明,该方法可以降低检测人员的劳动强度,提高检验计划的编制效率。

检验数模  /  改进的动态规划算法  /  检测顺序生成  /  CAA二次开发  /  检验计划自动编制

Aiming at the current aviation typical parts with complex structure and high precision requirements, increased difficulty in quality inspection but backward inspection methods, an inspection plan automatic preparation method was proposed. Firstly, from the information reuse and information integration, the inspection number model was constructed on the basis of the design model, which layed a foundation of data source for the generation of the inspection plan. Secondly, the production method of the inspection plan was formulated, and the rules of the inspection sequence generation were designed, to achieve the automatic generation of inspection sequence number by using the improved Dynamic planning algorithm. Finally, combined with the CAA(component application architecture) secondary development interface and Excel component development, the corresponding function module was developed under the CATIA environment, to achieve the output of the inspection element information and process view, to complete the automatic preparation and export of the inspection plan. Through examples, it is proved that the method can reduce the labour intensity of inspectors and improve the efficiency of the preparation of inspection plans.

inspection modelling  /  improved dynamic planning algorithms  /  inspection sequence generation  /  CAA secondary development  /  automatic preparation of inspection plans
郭朝阳, 蒋英吉, 程华龙, 余志斌, 王发麟. 基于MBD的航空典型零件检验计划自动编制方法. 科学技术与工程, 2025 , 25 (15) : 6538 -6547 . DOI: 10.12404/j.issn.1671-1815.2404142
Chao-yang GUO, Ying-ji JIANG, Hua-long CHENG, Zhi-bin YU, Fa-lin WANG. Automatic Compilation Method of Aeronautical Typical Parts Inspection Plan Based on MBD[J]. Science Technology and Engineering, 2025 , 25 (15) : 6538 -6547 . DOI: 10.12404/j.issn.1671-1815.2404142
在航空制造业,质量检验是一项庞大复杂的系统工程,从材料验收、零件生产、部件装配到总装试飞,都伴随各种不同的检验活动,零件检验是其中重要的组成部分[1]。目前,中国飞机制造厂商在零件生产验收中仍依赖二维图纸表达产品检验过程信息,将纸质工程图纸和检验计划作为指导生产的检验依据[2]。传统检验方法是通过三维数模的二维转换绘制工程图来表达零件几何特征和工艺要求,并采用Excel编制检验计划表格来规划检验顺序和检验特性方法,最终以“二维图纸+检验表格”作为指导零件检测的规范性技术文件。其中,检验表格中检验信息全手动输入,自动化程度差,编制工作量大,耗时费力,完全没有发挥出三维模型的优势[3]。随着飞机数字化设计与制造技术的发展,产品的制造精度要求更高,产品检测难度加大,传统的检测技术难以满足实际生产需求,解决检验环节智能化的问题已成为航空制造业发展的关键技术。
基于模型定义(model based definition, MBD)技术的高速发展为上述问题提供了解决方案[4]。MBD三维数字模型贯穿运用在产品的全生命周期中,是连接设计与制造信息之间的桥梁,有效解决了信息脱节的问题。MBD技术的应用从根本上改变了产品的研制模式,其全数字化表达方式使零件检验信息具有完备的基础数据源,便于直观、便捷地提取有效信息,更能提高零件生产和检验过程中的效率,缩短零件的研制周期。
目前,国外一些大型飞机制造企业已经将基于MBD的全三维制造技术应用于生产实践中,取得了显著的效益,中国基于MBD的技术体系还在建设和探索阶段,尤其在产品质量检测与控制层面,还需要更深入细致地发掘和探索[5]。21世纪以来,波音、空客、罗·罗、Messier-Dowty、Ford等公司已经大量运用三维数字化检测和质量控制方法,建立了较完善的数字化检测技术体系,开发了相应的计算机辅助三维检测规划工具系统,显著提高了检测效率和质量[6]。Zhao等[7]通过三维MBD模型对复杂产品的整个生命周期数据进行规范化定义与关联,开发了一个总体概念框架,设计一套映射模型和映射规则,从而为检测规划与检验规程生成提供一致可靠的数据支持。Thomas等[8]从产品制造信息出发,提取CAD(computer aided design)模型的STEP AP242文件中的几何信息,能有效提高夹具设计质量,从而实现设计与制造的一体化,并采用C#编程语言开发了自动特征提取软件FiXplan,用于提取零件的特征、尺寸和几何信息。Liu等[9]提出了基于模型的集成检测概念,并构建了一个由设备层、数据层应用层和操作层的组成的框架,将面向过程的检测和面向质量验证的检测结合在一起。Rezaei-Malek等[10]认为零件质量检测规划是一个重要的决策问题,合理的零件质量检验计划不仅能避免缺陷零件的交付,还能减少时间和材料的浪费,从而提高制造管理系统的效率。Abdulhameed等[11]提出一种基于人工智能技术(即人工神经网络和遗传算法)的方法,用于优化检测步骤的输出,使整个检测过程自动化,以实现高效的检测计划。党文浩[12]运用MBD技术完成对三维模型的信息标注以及标注信息管理,基于NX软件平台开发了产品制造信息(product & manufacturing information,PMI)模块,实现了制造与检测特征信息提取,将三维模型的所以信息以数模形式展示,并能以PDF文件格式发布。王筠翔[13]针对轴类零件共性特征,提出设计加工检测一体化策略,建立了变型关联规则,并设计了基于共性特征的加工检测集成工艺规划机制,开发了轴类零件设计-加工-检测系统软件。张贺等[14]提出一种基于模型的三维索引编号自动标注技术,用算法模拟人工操作,通过索引自动排序、指引点确定及索引智能排布等算法,实现了三维索引图自动标注功能,能够提高设计人员检测规划的效率。赵霞等[15]针对汽轮机叶片检测过程自动化程度低、检测规划周期长等问题,提出了基于MBD的叶片检测路径规划方法,设计了基于邻接矩阵的匹配算法,实现了检测路径自动生成与检测顺序的优化。徐龙等[16]提出了检验模型的构建方法,说明了检验模型的构成内容,检验模型的应用以及在检测过程中的作用。程瑜等[17]介绍了检验计划的应用,说明了检验计划的编制内容和原则,推动检验计划在零件检查验收的检验模式的进一步发展。
中国针对CATIA等三维设计软件的二次开发也取得了一些进展。大众汽车公司针对传统工艺校核方法效率低下,基于CATIA开发设计相应的主体功能来提高工艺校核的效率和准确率[18]。武晓娟等[19]提出了针对钣金零件的检验计划生成,提取了零件模型的检验数据信息,在CATIA三维软件上生成XML格式的检验计划。莫杰等[20]基于模块模板化的思想,利用组件应用架构(component application architecture,CAA)二次开发工具开发了工程图框、标题栏,明细表和更改标记的快速生成工具,但仅限于在二维图纸的应用。
为解决实际生产中检测方法技术落后的不足,基于三维环境下的MBD零件模型,提出了一种检验计划自动编制方法,首先通过信息重用构建全信息检验数模,为检测工作提供数据与技术支持,其次,针对检验顺序手动编制的缺陷,提出一种基于改进的动态规划算法的检测顺序自动生成方法,再设计相应的信息提取方案,创建特征信息结构体,最后并通过CAA二次开发技术来实现检测数据信息的获取与存储,完成信息输出与检验计划的自动生成。
基于模型定义技术明确了数字化环境下产品定义的内容,MBD三维数字化全信息模型并不是简单的对三维模型进行三维标注。检验数模作为实际生产、检验的唯一信息载体,以设计模型或工艺模型为基础,除了包含比二维工程图更为详细直观的几何信息外,还需要包含尺寸公差、形位公差、材料信息等非几何信息。同时,为保证数据源的唯一性,检测工序、检测方法、检测工具、工艺要求和设计更改等检测规划信息也应该融入到三维模型中,成为检验数模的组成部分。具体体现为检验数模包含了产品定义信息以及检验计划生成的全部内容,所有信息共同存储在同一个MBD模型中,可以直接从检验数模上提取并导出,区别于传统的工艺文件与几何信息脱离保存的方式。如图1所示为检验数模的结构组成。
零件检验模型的几何信息来源于零件的设计模型,几何信息描述零件的尺寸、位置和形状,由建模最基本的几何定义元素点、线、面构成,是检验模型的基础。检测特征被认为是某种结合形状和功能的两种属性特征得到信息集合。零件的几何检测特征包括轮廓外形、孔、轴、倒角、槽口、下陷等。
三维标注信息是几何模型设计与制造之间信息传递的桥梁,将尺寸公差,几何公差、粗糙度、基准及注解等特殊的非几何制造信息与几何拓扑结构相关联。而且,作为MBD检测模型的重要组成部分,三维标注可以反映更为详细的设计以及加工要求,也能完善表达零部件的检测需求。
零件检验模型的属性信息一般以文本形式存储在结构树中,包括零件工艺要求、设计更改内容、材料信息、工序步骤、检测工量具、检测方法要求及标准信息等,为三维检验计划编制提供了操作流程及文本类信息源。
基于检验数模信息的复杂性与CATIA的信息管理方式,在数模信息管理时,可以采用两种管理方法:采用特征树方式对文本类检测信息进行规划管理,按照合理的逻辑顺序制定统一的存储规则,固定存储路径,保证信息数据源的精确性;使用层级结构管理图层信息,在构建检测模型时将不同图层的几何信息分类处理,合理分布各个图层的三维标注信息,建立不同的空间布局,实现三维标注信息分层表达,再通过图层关联来管理产品定义信息,方便检测人员快速查找与浏览。
检测报表作为指导检测工作的指令性文件,需要制定规范的检测步骤。对于检验数模中的文本类属性信息,通过固定的存储路径和提取方案,可以实现精确提取与导出并生成标准格式文件。但由于三维标注信息与几何检测特征处于立体空间内,检索查阅过程中存在一定难度,故需对其进行一个顺序规划,采用编号“①②③……”标识检测特性特征(即检验报表中的检测步骤编号),以此实现模型信息的精确访问,检测人员根据编号顺序便可快速完成检测信息的查找。
目前,检验工作中的检测顺序仍然完全依靠手工编制,耗时费力,效率较低。检测顺序的规范表达形式如图2所示,要实现检测顺序自动生成,必须制定合理的逻辑规则,设置特定的限制条件,使得系统自动生成的检测顺序能够达到实际生产时的标准要求。结合零件三维标注信息的分布特点,制定了三条生成气泡编号时遵循的逻辑规则。
(1)同方向原则。图2中的11个气泡顺序编号是根据检测信息的坐标位置逆时针排序的。在规定检测顺序编号的生成方法时,要坚持同方向原则,不能错乱或随意添加,保证从各个标注视图正面看,检测信息所携带气泡顺序编号一定根据顺时针或者逆时针方向进行编制的,不能随意插入或添加。
(2)连续性原则。由于MBD模型包含完整的产品零件定义信息,其中三维标注信息在表达时存在多个标注依附面(即标注视图)。针对各个视图包含的标注信息,其携带的气泡顺序一定是连续的,不能有跨越或重复,而且每个视图的气泡不能都从“①”开始编号,后一视图的首个气泡顺序应该排在前一视图所有信息之后,例如,图2中零件的下一标注视图内的标注信息的检测顺序应从“⑫”开始。
(3)自动更新原则。鉴于实际检验工作中存在零件模型设计更改等特殊情况,需要增删或更改标注信息。为避免排序混乱,在自动生成检验特性编号时,系统应该根据实际标注信息自动完成气泡序号的增删,确保气泡要具有自动更新排序的功能。
图3所示为检测顺序自动生成逻辑流程图。 在满足上述生成规则的同时,判断并规划生成标注信息的检测顺序属于组合优化问题。而且,针对单一标注平面的标注信息进行顺序规划,从初始点M到终点N,所有元素都必须遍历访问,通过群体优化寻找规划空间的最优解,从众多的求解路径中寻找一条最短路径[21],即为所求的检测路径最优解,与经典的旅行商问题相似。故本文中决定对传统的动态规划算法加以改进,来求解检测顺序的生成问题。动态规划(dynamic programming)是解决多阶段决策过程最优化的一种数学方法,通常用于求解具有某种最优性质的问题[22]。结合动态规划算法特点,在规划检测和顺序时按以下几个步骤进行。
(1)刻画一个最优解的结构特征。假设过滤得到的待输出检测特征元素集合Qi包含n个检测要素信息,记为s0s1、…、sn-1,那么从任一顶点si出发,经过所有其他顶点有且仅有一次,最后回到起始点si,即sisi+1sn(其中si=sn)是一条从si出发的最短简单回路。
(2)递归地定义最优值的求解公式。令V表示所有标注元素的顶点集合,V={s0s1s2、…、sn-1},计算所有标注顶点两两坐标之间的距离c,将其保存在二维动态数组double **D中,构建位置距离邻接矩阵C
C= 0 c 01 c 02 c 0 ( n - 1 ) c 10 0 c 20 c 1 ( n - 1 ) c 20 c 21 0 c 2 ( n - 1 ) c ( n - 1 ) 0 c ( n - 1 ) 1 c ( n - 1 ) 2 0
式(1)中:cij为标注顶点i和标注顶点j的距离,且cij=cji
假设从起始顶点s出发,状态变量d(i,V)表示从顶点i出发经过V(剩余未访问的标注元素顶点集合)中各个顶点一次且仅有一次,最后回到出发顶点s的最短路径长度。
①如果V为空集,则表示所有顶点均被访问过,直接回到起始顶点s,那么d(i,V)表示直接从i回到s了,此时d(i,V)=cis,且(is)。
②如果V不是空集,那么就是在V这个集合中,求解子问题的最优解。表达式为
d(i,V)=min[cik+d(k,V-{k})]
式(2)中:cik为已选择的标注顶点k和标注顶点i的距离;d(k,V-{k})为一个子问题。
综上所述,求解检测顺序最优解的动态规划方程为
d(i,V)= c i k , V = , i s m i n [ c i k + ( k , V - { k } ) ] , k V , V
式(3)中:s为起点。
(3)以自底向上的方式计算最优值。按照上述思路,当访问到最后一层,V是空集,直接从该顶点返回起始顶点,恰好访问了所有标注元素顶点一次,生成最优解路径。从底层子问题求解最优路径,逐层向上循环,自底向上的方式逐步求取子阶段的最优解,通过多阶段决策过程共同构成一条全局最优路径,并将其结果保存在二维动态数组double **dp中。
(4)根据计算结果路径回推检测顺序。在各个阶段的决策过程中,需要将计算结果进行比较,保存长度较短的检测路径,反映到程序中,这需要一个包含循环的迭代函数来实现。最后,利用数组dp的保存结果,回推最优路径中各个顶点的顺序编号,将检测顺序结果保存在数组path[]中,并按顺序依次插入检测特性编号,生成气泡。
在MBD技术环境下,基于检测模型的检验报表自动生成是在传统检验模式基础上的技术创新。检验计划是现场工作人员开展产品检测工作的指导依据,是根据三维数模、技术标准、工艺要求、信息交接清单、质量文件等验收依据编制的技术文件。目前企业编制的主流检验计划内容主要包括检验计划草图和检验计划表格,涵盖了零件检测的全部相关信息。
其中检验计划表格包含所有检验特性信息,如尺寸、公差、工艺要求、制造标准、工程注释以及执行的设计更改和指令性交接等。而且,依据数模检测要求与技术条件,检验报表还应该自动匹配相应的检测方法和工具,并提供工具精度和备注要求等。最后,产品零件的相关属性信息、文本属性注释
以及特殊过程检验方法等内容也应包含其中。检验计划草图,也称工艺视图,用于描述检验特性特征,类似于以往产品研制模式中的设计蓝图。在工艺视图中对特征元素编制气泡编号,并以此作为检测顺序依据规划检测路径,编制检验计划表格,做到工艺视图内容与检测计划表格信息一一映射。
Excel具有强大的报表输出功能,CAA二次开发也提供了直接读写外部文件的类和接口函数,通过设计表(CATIDesignTable)可以对外部Excel文件进行读写,故采用Excel输出形式能够实现检验计划的快速生成。但是,由于检验计划不同于普通的表格文件,为方便检测人员查阅检测要求,检验计划表格必须具备标准的规范格式,不同类型信息具有固定的输出位置和格式要求。因此,在向Excel输入数据前,需要做好文件模板,并将文件另存为.xls模板格式。检验计划在输出时会调用模板,复制文件并以指定格式命名后存储到设定路径,最后依据指定的输出方案,按照模板格式输出检验信息内容。
在检验数模中,制造特征类型检测要素集中分布在三维标注信息内,但检验计划内容不包含所有制造信息,故需要对三维标注信息进行判断过滤,去除一些与检验计划编制无关的信息(如基准等),得到三维标注信息输出集合。检验计划包含的三维标注信息主要分为三类:尺寸公差、几何公差、表面粗糙度信息,具体结构信息如图4所示。最后遍历该集合内的所有元素,获取其有效信息载体及分布位置。
作为唯一数据源的检验数模,其包含的信息内容种类繁多,分布散乱,信息载体也大不相同,很难统一提取并输出,因此,决定构建一个信息输出链表全局变量(CATITPSList* piAlltpslist),用于组织和储存所有输出内容。
首先,梳理检验计划输出的所有内容,进行分类和定义处理,建立清晰的信息框架组织结构,设计特定的输出方案,并按顺序提取并另存于信息输出链表piAlltpslist,整体的保存顺序如图5所示。其次,对于结构树上生成的文本类信息,由于在构建检验数模时已经设置了固定的存储路径和载体,故针对不同类型的信息设计相应的访问输出方案即可实现精确提取与保存。然而,对于与几何结构关联的三维标注信息,基于检测顺序的生成,即可按顺序依次提取并保存。最后基于总体的输出框架以及模板文件的规范形式,完成信息输出链表的构建,以此确保检验计划格式的一致性。
需要注意的是,在设计信息提取的API(application programming interface)函数时,每种类型的特征信息都应创建相应的结构体,通过API函数可直接得到部分参数的信息,但有些需做特殊处理才能获取,比如数据类型不匹配时需对数据做类型转换;只需获取字符串前后缀时需对字符串做截断处理等等。该步骤存储的数据会在后续的输出算法中使用,所以结构体参数的定义必须完整。
在CATIA中,通过标注平面(视图)来解决三维标注信息的组织与管理问题。作为检测特征的三维标注信息数量众多,很难在同一个标注平面上完整表达。因此,对于不同位置与方向的标注信息,需要有不同的标注依附面作为信息载体。然而,众多的标注平面之间存在的干涉重叠问题也难以避免,全部显示势必会导致整个三维模型信息混乱、表达不直观,不方便生产下游检测部门使用与浏览。为了实现在三维模型中快速查找与显示相关的检测特征,采用捕获视图的形式来实现工艺视图的管理与输出。
工艺视图作为检测特征的信息载体,是检验计划的重要组成部分。首先,截图输出前需要在模板文件中预先设定草图页的保存位置,并设置截图大小与储存单元格相适应。其次,需要对标注视图进行预处理,创建捕获视图。遍历标注集内的所有标注视图,依次让其正向显示并对三维标注信息进行显隐处理,只保留该标注视图的相关标注信息完全显示,与视图自身无关的无效信息自动隐藏,按顺序依次创建捕获视图。三维标注信息的显隐控制,对三维模型的信息集成的规范化和实用性具有重要作用。然后,结合二次开发的截图功能,将捕获视图依次自动截取图片并另存为.jpg格式,完成工艺视图创建。最后,通过添加的Excel输出函数库,调用图片输出函数将保存的截图按顺序输出到检验计划草图页的指定位置,完成工艺视图的输出。
图6所示为基于检验数模的检验计划自动生成总流程图。
为验证本文方法的有效性,对多个航空零件进行试验测试,鉴于航空结构件检测特性繁多、复杂的特点,现选取某航空典型结构件的典型检测特征作为研究重点,以该典型零件的设计模型为基础,遵循检测要求规范形式构建了检验模型,对零件的检测路径进行规划,自动生成检测顺序,并完成检验计划表格的自动输出。系统对话框功能界面如图7所示。如图8所示为检测顺序自动生成的效果图。图8(a)即为带有三维标注信息的三维检验模型,显然,基于不同标注视图的检测信息错综复杂的分布在零件模型上,形成一个“刺猬模型”,导致信息表达不清晰直观。但通过已开发的功能模块对检验数模所包含的所有检测信息进行自动管理规划,首先,对其进行一个顺序规划,基于相应算法实现检验顺序编号的生成;其次,将标注视图与其映射的检测信息相关联,并进行相应的保存与隐藏操作,即可得到如图8(b)~图8(d)所示的捕获视图;最后,通过输出功能按钮实现检验计划表格的自动编制与输出。具体操作步骤如下:
(1)点击“检测顺序生成”按钮,系统依据标注视图顺序,对三维标注中的检测要素信息进行分类过滤,编制检验顺序编号并生成气泡,实现检测顺序自动生成。
(2)先点击“获取视图”按钮,提取标注视图列表,再依次选择列表中的标注视图,点击创建“捕获视图”按钮,系统完成捕获视图集的创建,同时完成捕获视图中无关信息的隐藏。
(3)点击“报告输出”按钮,系统自动对MBD模型信息进行提取、过滤并分类,构建信息输出链表保存相关信息,然后将检测要素信息导出到检验特性要求页的指定位置,生成Excel数据文件。同时对相应的捕获视图进行自动截图并保存为.jpg格式,生成工艺草图,最后输出到按顺序输出到草图页,完成检验计划的自动编制。
检验计划自动编制效果如图9所示。
(1)基于CATIA平台开发了零件检验计划自动编制原型系统,实现了检测路径自动规划,检测顺序自动生成,检验计划自动编制并导出保存,降低了劳动强度,改善了人工编制检验计划效率低的问题。
(2)实例验证结果表明:采用动态规划算法可以自动规划检测路径,快速生成检测顺序和气泡编号,解决了以往人工手动增加与删除检验编号的缺陷,大幅节约了时间和人力成本,缩短了产品研发周期。
(3)系统软件自动编制检验计划时,直接从三维数模上提取检测信息,不需要借助二维工程图作为中间载体,避免了数据源信息的二义性。而且根据程序设计的提取规则,自动提取有效信息并分类,同时对工艺视图进行预处理并自动截图,最终一同导出到Excel文件里,自动生成检验计划,消除了人工手动编制时的错填、漏填问题,保证检验计划信息的精确性。
(4)本文开发的功能模块对长期依赖传统的检验方法进行了优化升级,以控件功能响应替代人工操作,解决了简单重复性工作的缺陷,通过设计简单的操作系统减少检测工作量,提高了检验人员的工作效率。同时,MBD技术的实际应用对传统的检验计划生成方法进行了优化,在二维检验计划和三维检验模型两个方向上都做了有益的探索,推动了检验部门数字化检测的进程。
  • 江西省自然科学基金重点项目(20212ACB202005)
  • 国家重点实验室(工程中心、基地)开放基金(GT202108060)
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doi: 10.12404/j.issn.1671-1815.2404142
  • 接收时间:2024-06-04
  • 首发时间:2025-07-09
  • 出版时间:2025-05-28
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  • 收稿日期:2024-06-04
  • 修回日期:2024-10-14
基金
江西省自然科学基金重点项目(20212ACB202005)
国家重点实验室(工程中心、基地)开放基金(GT202108060)
作者信息
    1 航空工业江西洪都航空工业集团有限责任公司检验中心/无损检测中心, 南昌 330024
    2 南昌航空大学航空制造工程学院, 南昌 330063

通讯作者:

* 王发麟(1986—),男,汉族,江西吉安人,博士,讲师。研究方向:数字化设计与制造。E-mail:
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2种不同金属材料的力学参数

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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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