WO2019144436A1 - 一种基于加工特征的工艺知识推送方法 - Google Patents

一种基于加工特征的工艺知识推送方法 Download PDF

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WO2019144436A1
WO2019144436A1 PCT/CN2018/075382 CN2018075382W WO2019144436A1 WO 2019144436 A1 WO2019144436 A1 WO 2019144436A1 CN 2018075382 W CN2018075382 W CN 2018075382W WO 2019144436 A1 WO2019144436 A1 WO 2019144436A1
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feature
process knowledge
processing
knowledge
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French (fr)
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刘金锋
周宏根
李磊
李国超
何强
田桂中
景旭文
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江苏科技大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/18—Manufacturability analysis or optimisation for manufacturability
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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  • the invention belongs to the field of intelligent process design of machined parts, and particularly relates to a process knowledge pushing method based on processing features.
  • the feature-based CAPP system has been widely used by manufacturing companies to create MBD process models with a large number of processing features stored in the enterprise model database. These process models embed a large number of processing and manufacturing information, such as processing resources, processing requirements, etc. These process models have not been fully exploited and utilized, which will cause a lot of manpower and material waste.
  • the feature-based process knowledge push method can not only solve the reuse of existing process knowledge, but also realize intelligent process decision-making to promote the development of intelligent machine plus process design.
  • the process push method mostly concentrates on the form of process knowledge retrieval, mainly based on process semantic retrieval and shape retrieval methods. However, most of these methods are based on similar retrieval at the part level, and are not related to process processing features, thus leading to retrieval.
  • the process knowledge cannot be directly applied, and further interactive screening processing is required.
  • the reuse of process knowledge has been seen by manufacturing companies as an important factor in shortening the development cycle, reducing costs, and improving the competitiveness of enterprises.
  • the retrieval method based on process semantics can only be applied to the process of low-level process knowledge representation; the shape-based retrieval method can solve the matching of diverse process knowledge, but does not consider the specific process design requirements. Therefore, the process knowledge push method based on processing features can not only be fully embedded in the 3D machine plus process design system, but also can achieve efficient reuse of process knowledge.
  • the present invention provides a process knowledge pushing method based on processing features, based on the similarity calculation and process knowledge of the process intention model.
  • the evaluation method ensures the accurate pushing of the machining process knowledge, improves the efficiency of the process design, and provides technical support for the intelligent process design.
  • the present invention provides a process knowledge pushing method based on processing features, which is characterized in that it comprises the following steps:
  • the process intention model PPIM is constructed.
  • the similarity calculation method in the process intent model constructed in step (1) includes: part basis information similarity calculation, processing feature similarity calculation and quality information similarity calculation;
  • the hierarchical representation model of process knowledge includes three levels: basic process information layer, processing information layer and quality information layer;
  • the construction of the process intent model in the step (1) is constructed according to the part design information and the processing requirement, wherein the PPB is the process design background including the part type, the blank type, the material property and the processing type; the PPG is the process design target. It includes machining feature type, tool feed direction, feature surface matrix, adjacent surface matrix, topological relationship matrix and specific size information; API is process attachment information including geometric accuracy information and dimensional accuracy information.
  • the basic process information layer includes four process elements: part type, blank type, material attribute and processing type;
  • the processing information layer includes six processing elements: processing type, processing method, machine type, tool type, fixture type and cutting fluid;
  • the quality information layer includes specific inspection requirements.
  • the PPB is obtained by creating basic design information
  • the PPG is obtained by identifying a processing feature, specifically:
  • the processing feature type is determined by a predefined feature library, and mainly includes a hole feature, a groove feature, a planar feature, and a boss feature;
  • the tool feed direction is determined based on the normal vector of the machined surface
  • the feature surface matrix is formed by each feature surface type and its adjacent surface attributes.
  • the types of the feature surface include plane, cylindrical surface, chamfer surface, spherical surface and torus surface.
  • the adjacent surface attributes are based on the attributes of the intersecting edges of the feature surface. Determined to include concave, convex and tangent edges;
  • the topological relationship matrix is determined by the relationship between the feature faces, including parallel, vertical, oblique, and tangent;
  • the basic size information is determined by the minimum bounding box of the machined features, including length, width and height.
  • processing feature similarity calculation in the step (2) includes three types: vector matching degree calculation, matrix matching degree calculation, and attribute value matching degree calculation:
  • the vector matching degree calculation expression is:
  • Conine(p,q) represents the degree of matching between the vector p and the vector q, and i represents the i-th element in the vector, wherein the matrix matching degree value can be converted into a vector matching degree calculation;
  • the matricity calculation method of the matrix is to first convert the N-order matrix into an N-dimensional vector, and calculate the matching degree of the vector to realize the matrix matching degree calculation.
  • the attribute value matching degree calculation expression is:
  • S a (a 1 , a 2 ) represents the degree of matching of the attribute values a 1 and a 2 , n represents the number of elements contained in the attribute value, and j represents the j-th element in the attribute value.
  • the priority of each element of the process knowledge in the step (4.1) refers to a priority relationship of the elements contained in the processing information layer, and the priority order is a processing type, a processing method, a machine type, a tool type, a fixture type, and Cutting fluid type.
  • calculation formula of the confidence level of the adjacent process knowledge elements in the step (4.2) is as follows:
  • Preq(p 1 ) represents the total number of process knowledge elements p 1
  • Preq(p 1 ⁇ p 2 ) represents the process knowledge element The total number of times p 1 and p 2 are associated.
  • processing feature type is expressed by a string
  • tool feeding direction is expressed by a vector
  • feature quilt matrix and the topological relationship matrix are created by attribute type assignment.
  • step (5) based on the obtained part process intention model and the confidence value, the specific steps for accurately pushing the process knowledge are as follows:
  • step (5.1) judging whether the matching degree of the process background information in the retrieved process knowledge satisfies the requirement by the process intention model, if yes, proceeding to step (5.2), and if not, continuing to search until the requirement is met;
  • step (5.2) Determine whether the matching degree of the process target information in the process knowledge meets the requirements, and if yes, proceed to step (5.3), and if not, continue to search until the requirements are met;
  • step (5.3) Determine whether the matching degree of the process subsidiary information in the process knowledge meets the requirements, and if yes, proceed to step (5.4), and if not, continue to search until the requirement is met;
  • the invention utilizes the process knowledge representation of the processing feature as the carrier, and proposes a process knowledge pushing method, which effectively solves the fast and accurate pushing of the process information in the process of the three-dimensional machine adding process design, thereby improving the efficiency of the process design and also the intelligent process. Provide technical support for the development and application of the design.
  • Figure 1 is a flow chart of the present invention
  • FIG. 2 is a schematic structural view of a diesel engine connecting member in an embodiment
  • Figure 3 is a schematic diagram showing the results of the process design goals of the parts in the embodiment.
  • FIG. 4 is a schematic diagram of a process intent model in which the processes to be processed are matched in the embodiment.
  • the present invention provides a process knowledge pushing method based on processing features, which in turn includes the following steps:
  • PPIM Process planning intent model construction based on processing feature vector expression method.
  • Hierarchical expression of process knowledge The hierarchical representation model of process knowledge is divided into three levels: basic process information layer, processing information layer and quality information layer.
  • the similarity calculation method of the process intention model mainly includes the similarity calculation of the basic information of the parts, the similarity calculation of the processing features and the similarity calculation of the quality information.
  • the PPIM is created according to part design information and processing requirements, wherein the PPB includes part type, blank type, material property and processing type; PPG includes machining feature type, tool feed direction, feature surface matrix, adjacent surface matrix, topology Relationship matrix and specific size information; API includes geometric accuracy information and dimensional accuracy information.
  • the hierarchical knowledge representation model of the process knowledge is managed by creating a process knowledge base, wherein the process knowledge element included in the basic process information layer is the same as the process knowledge of the process design background; the processing information layer includes the processing type, the processing method, the machine tool type, Tool type, fixture type and cutting fluid six parts; quality information layer includes specific testing requirements.
  • the process intent model similarity calculation includes similarity calculation of basic process information, similarity calculation of machining features, and similarity calculation of quality information.
  • the flow of the process knowledge evaluation method based on the confidence calculation is: firstly, the priority of each element of the process knowledge is determined; then, the confidence of the adjacent process knowledge elements is calculated, and the process knowledge element with the highest confidence is output; finally, the calculation is performed. Confidence in the level of process knowledge elements until all process knowledge elements are obtained.
  • the background knowledge of the process design is obtained through the basic design information of the part.
  • the process design goal is obtained by identifying a machining feature, wherein the machining feature type is determined by a predefined feature library, which mainly includes a hole feature, a groove feature, a plane feature, and a boss feature; the tool feed direction is based on the machining
  • the normal surface of the surface is determined;
  • the feature surface matrix is formed by each feature surface type and its adjacent surface properties, and the types of the feature surface include plane, cylindrical surface, chamfer surface, spherical surface, and torus, adjacent surface
  • the attributes are determined based on the attributes of the intersecting edges of the feature faces, including concave, convex and tangent edges;
  • the topological relationship matrix is determined by the relationship between the feature faces, including parallel, vertical, oblique and tangent;
  • the basic size information is Determined by the minimum bounding box of the machined features, mainly including length, width and height.
  • the creation of the process knowledge base is organized based on association rules, wherein the association rules are divided into a single dimension association rule and a multi-dimensional association rule.
  • the similarity calculation of the processing feature includes a vector matching degree calculation, a matrix matching degree calculation, and an attribute value matching degree calculation, wherein the vector matching degree calculation expression is:
  • Conine(p,q) represents the degree of matching between the vector p and the vector q, and i represents the i-th element in the vector, wherein the matrix matching degree value can be converted into a vector matching degree calculation;
  • the attribute value matching degree calculation expression is :
  • S a (a 1 , a 2 ) represents the degree of matching of the attribute values a 1 and a 2 , n represents the number of elements contained in the attribute value, and j represents the j-th element in the attribute value.
  • the priority of each element of the process knowledge refers to the priority relationship of the elements contained in the processing information layer, and the priority order is the processing type, the processing method, the machine type, the tool type, the fixture type, and the cutting fluid type.
  • Preq(p 1 ) represents the total number of process knowledge elements p 1
  • Preq(p 1 ⁇ p 2 ) represents the process knowledge element The total number of times p 1 and p 2 are associated.
  • the processing type is expressed by a string; the tool feed direction is expressed by a vector; the feature quilt matrix and the topological relationship matrix are created based on the attribute type assignment.
  • the creation of a process intent model is the key to the knowledge of machining process knowledge.
  • the process intent model consists of three parts: the process design background, the process design goals, and the process ancillary information.
  • the process design background is composed of basic attribute information of machined parts, including product family, part type, blank type, material type and processing type.
  • the background design of the process design of the part is: diesel engine connecting parts, V12 connecting parts, cast blank, 45 # steel, NC machining.
  • the process design goal includes six parts: feature type, tool feed direction, feature quilt matrix, adjacent quilt matrix, topological relationship matrix, and size information.
  • each attribute element is managed in the form of a letter or binary number assignment.
  • the assignment of the processing feature type and the feature surface attribute is shown in Table 1 and Table 2; the parallel, vertical, non-vertical and tangent binary numbers in the topological relationship are assigned: 0001, 0010, 0011, and 0100; the intersecting edge attribute is concave.
  • the binary digits of the embossed and tangent edges are assigned: 0001, 0010, and 0011.
  • the binary digits of the line, arc, and spline in the intersecting edge type are assigned: 0001, 0010, and 0011, to accurately represent the intersecting edge information.
  • attribute + type such as "tangential edge - arc”
  • the faces F1 and F3 are planes
  • F2 and F4 are chamfered faces
  • the created feature group matrix, topological relationship matrix and phase are created based on the binary number assignment of each attribute element.
  • the adjacent group matrix is shown in Figure 3.
  • the process design intent model is the basis for acquiring the candidate process knowledge set.
  • the matching process is used to obtain the similar process design intent model in the existing process knowledge database, and then the associated process knowledge is acquired to form the candidate process knowledge set.
  • the process of matching similar design intent models is as follows: The similarity calculation of the process design background, according to the principle of "similar parts have similar processes", parts with similar design intent will necessarily have the same process design background, so the process design background contains the process Knowledge does not need to be similarly calculated; the similarity calculation of the process design goal, which includes six attribute elements, in which the feature type and the tool feed direction need to be completely matched, therefore, only the feature surface matrix, the adjacent surface matrix, and the topological relationship are needed. Matrix and size information are used for similarity calculations.

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Abstract

一种基于加工特征的工艺知识推送方法。首先，为表达零件加工工艺的需求与目的，提出基于加工特征向量表达技术的工艺意图模型创建方法；然后，提出工艺知识的层次化管理方法，将其分为基础信息层、加工信息层和附属信息层三个层次，并关联于工艺意图模型中的各组成元素；其次，提出工艺意图模型的相似性计算方法以提高工艺知识推送的效率；最后，基于工艺重用评价方法实现工艺知识的准确推送。提出的工艺意图模型创建方法，能够为工艺决策以及工艺信息重用提供技术支撑，而工艺知识推送方法为机加工工艺的智能设计奠定基础。

Description

一种基于加工特征的工艺知识推送方法 技术领域
本发明属于机加工零件的智能工艺设计领域，特别涉及一种基于加工特征的工艺知识推送方法。
背景技术
基于特征的CAPP系统已被制造企业广泛应用，创建了含有大量加工特征的MBD工艺模型存储于企业模型数据库中，这些工艺模型嵌入了大量的加工制造信息，如加工资源、加工需求等，然而如果这些工艺模型没有被充分挖掘与利用，将会造成企业大量人力与物力地浪费。基于特征的工艺知识推送方法，不仅能很好地解决对已有工艺知识的重用，而且能够实现智能化工艺决策，以促进智能化机加工艺设计的发展。
目前，工艺推送方法大都集中工艺知识检索的形式，主要以工艺语义检索和形状检索方法为主，然而这些方法大都以基于零件层面上的相似检索，并没有关联于工序加工特征，因此，导致检索的工艺知识不能直接应用，还需要进一步交互筛选处理。
在基于特征和情景方面的工艺推送研究中，有文献“李春磊，莫荣等.几何演变驱动的机加工艺知识表示与推送[J].计算机集成制造系统，2016,22(6):1434-1446”建立机加工艺知识复杂网络模型，以实现工艺知识的推送，但是该文献并没有考虑加工意图以及推送知识的评价，会导致不能准确推送所需工艺知识；文献“张发平,李丽.基于多维层次情景模型的业务过程知识推送方法研究[J].计算机辅助设计与图形学学报,2017,29(4):751-758.”提出了基于情境的知识匹配和推送的方法，但是文献并没有对工艺知识与情境模型的关联关系以及所推送工艺知识的评价进行阐述；文献“孙璞,候俊杰等.面向三维工艺设计的知识推送方法研究[J].制造业自动化,2016,38(9):96-104”提出了工艺设计意图与工艺知识间的匹配方法，通过获取的候选工艺知识集推送相应工艺知识，但是文献中工艺意图模型还是基于语义描述为主，并不能完全应用于三维机加工艺设计中。
综上所述，工艺知识重用已被制造企业看作为缩短研制周期、降低成本、提高企业竞争力的重要因素。然而，基于工艺语义的检索方法只能应用到低维度的工艺知识表达过程中；基于形状的检索方法能够解决多元化工艺知识的匹配，但是没有考虑具体工艺设计需求。因此，基于加工特征的工艺知识推送方法不仅能够完全嵌入到三维机加工艺设计系统中，而且能够实现工艺知识的高效重用。
发明内容
发明目的：针对现有技术中存在的问题，为加快机加工艺智能化设计的发展和应用，本发明提供一种基于加工特征的工艺知识推送方法，基于工艺意图模型的相似性计算和工艺知识的评价方法，确保了机加工艺知识的准确推送，提高了工艺设计的效率，为智能化工艺设计提供技术支持。
技术方案：为解决上述技术问题，本发明提供一种基于加工特征的工艺知识推送方法，其特征在于，包括如下步骤：
(1)基于加工特征向量表达方法构建工艺意图模型PPIM，PPIM的表达式为PPIM＝{PPB,PPG,API}，其中PPB为工艺设计背景，PPG为工艺设计目标，API为工艺附属信息；
(2)步骤(1)中构建的工艺意图模型中的相似性计算方法包括：零件基础信息相似性计算、加工特征相似性计算和质量信息相似性计算；
(3)构建工艺知识层次化表达模型，工艺知识层次化表达模型包括三个层次：基础工艺信息层、加工信息层和质量信息层；
(4)基于置信度计算的工艺知识评价方法：利用相邻工艺属性置信度计算值，获取最优的工艺知识元素；
(5)基于步骤(1)中得到的零件工艺意图模型和步骤(4)中的置信度值，实现工艺知识的准确推送。
进一步的，所述步骤(1)中工艺意图模型的构建是根据零件设计信息和加工要求构建的，其中PPB为工艺设计背景包括零件类型、毛坯类型、材料属性和加工类型；PPG为工艺设计目标包括加工特征类型、刀具进给方向、特征面矩阵、相邻面矩阵、拓扑关系矩阵和具体尺寸信息；API为工艺附属信息包括几何精度信息和尺寸精度信息。
进一步的，所述工艺知识层次化表达模型中三个层次具体为：
基础工艺信息层包括零件类型、毛坯类型、材料属性和加工类型四个工艺元素；
加工信息层包括加工类型、加工方法、机床类型、刀具类型、夹具类型和切削液六个工艺元素；
质量信息层包括具体的检测要求。
进一步的，所述基于置信度计算的工艺知识评价方法的具体步骤为：
(4.1)确定工艺知识各元素的优先级；
(4.2)计算相邻工艺知识元素的置信度，输出置信度最高的工艺知识元素；
(4.3)计算下一级工艺知识元素的置信度，直至获取所有工艺知识元素。
进一步的，所述PPB通过创建的基础设计信息获取，所述PPG通过识别加工特征获取，具体为：
加工特征类型是通过预定义特征库确定的，主要包括孔特征、槽特征、平面特征和凸台特征；
刀具进给方向是基于加工面的法向量确定的；
特征面矩阵是通过各特征面类型及其相邻面属性形成的，特征面的类型包括平面、圆柱面、倒角面、球面和圆环面，相邻面属性是基于特征面相交边的属性确定，包括凹边、凸边和相切边；
拓扑关系矩阵是通过特征面之间的相互关系确定，包括平行、垂直、倾斜和相切；
基本尺寸信息是通过加工特征的最小包围盒确定，主要包括长度、宽度和高度。
进一步的，所述步骤(2)中的加工特征相似性计算包括向量匹配度计算、矩阵匹配度计算和属性值匹配度计算三种类型：
其中向量匹配度计算表达式为：
Figure PCTCN2018075382-appb-000001
conine(p,q)表示向量p和向量q之间的匹配度，i表示向量中的第i个元素，其中矩阵匹配度值可以转化为向量的匹配度计算；
矩阵的匹配度计算方法是先将N阶矩阵转化为N维向量，通过计算向量的匹配度以实现矩阵匹配度计算。
属性值匹配度计算表达式为：
Figure PCTCN2018075382-appb-000002
S a(a 1,a 2)表示属性值a 1和a 2的匹配度，n表示属性值所含有的元素个数，j 表示属性值中的第j个元素。
进一步的，所述步骤(4.1)中所述工艺知识各元素的优先级是指加工信息层所含有元素的优先关系，其优先顺序为加工类型、加工方法、机床类型、刀具类型、夹具类型和切削液类型。
进一步的，所述步骤(4.2)中所述相邻工艺知识元素的置信度的计算公式如下：
Figure PCTCN2018075382-appb-000003
其中S con<p 1,p 2>表示工艺知识元素p 1和p 2的置信度，Preq(p 1)表示工艺知识元素p 1的总数量，Preq(p 1∩p 2)表示工艺知识元素p 1和p 2关联的总次数。
进一步的，所述加工特征类型通过字符串表达，刀具进给方向通过向量表达，特征面组矩阵和拓扑关系矩阵通过属性类型赋值创建。
进一步的，所述步骤(5)中基于得到的零件工艺意图模型和置信度值，实现工艺知识的准确推送的具体步骤如下：
(5.1)通过工艺意图模型判断检索到的工艺知识中的工艺背景信息匹配度是否满足要求，如果是则进入步骤(5.2)，如果不是则继续检索直至满足要求；
(5.2)判断工艺知识中的工艺目标信息匹配度是否满足要求，如果是则进入步骤(5.3)，如果不是则继续检索直至满足要求；
(5.3)判断工艺知识中的工艺附属信息匹配度是否满足要求，如果是则进入步骤(5.4)，如果不是则继续检索直至满足要求；
(5.4)输出满足要求的工艺信息列表，然后通过相邻工艺属性置信的度计算得到置信度值，最后输出并推送最优工艺知识元素。
与现有技术相比，本发明的优点在于：
本发明利用加工特征为载体的工艺知识表达，提出一种工艺知识推送方法，有效的解决了三维机加工艺设计过程中工艺信息的快速准确推送，进而提高工艺设计的效率，也为智能化工艺设计的发展和应用提供技术支持。
附图说明
图1为本发明的流程图；
图2为实施例中柴油机连接件的结构示意图；
图3为实施例中零件的工艺设计目标的结果示意图；
图4为实施例中待加工工序匹配的工艺意图模型的示意图。
具体实施方式
下面结合附图和具体实施方式，进一步阐明本发明。
如图1所示，本发明提供一种基于加工特征的工艺知识推送方法，依次包括以下步骤：
(1)基于加工特征向量表达方法的工艺意图模型(Process planning intent model,PPIM)构建。PPIM的表达式为PPI M＝{PPB,PPG,API}，其中PPB为工艺设计背景，PPG为工艺设计目标，API为工艺附属信息。
(2)工艺知识的层次化表达。工艺知识的层次化表达模型分为三个层次：基础工艺信息层、加工信息层以及质量信息层。
(3)工艺意图模型的相似性计算方法，主要包括零件基础信息相似性计算、加工特征相似性计算以及质量信息相似性计算。
(4)基于置信度计算的工艺知识评价方法。利用相邻工艺属性置信度的计算值，获取最优的工艺知识。
(5)工艺知识的层次推送。基于创建的工艺知识库和零件的工艺设计意图模型，实现工艺知识的层次化准确推送。
所述PPIM是根据零件设计信息及加工要求创建的，其中PPB包括零件类型、毛坯类型、材料属性以及加工类型；PPG包括加工特征类型、刀具进给方向、特征面矩阵、相邻面矩阵、拓扑关系矩阵以及具体尺寸信息；API包括几何精度信息、尺寸精度信息。
所述工艺知识层次化表达模型是通过创建工艺知识库进行管理的，其中基础工艺信息层包含的工艺知识元素与工艺设计背景的工艺知识相同；加工信息层包括加工类型、加工方法、机床类型、刀具类型、夹具类型以及切削液六部分内容；质量信息层包括具体的检测要求。
所述工艺意图模型相似性计算包括基本工艺信息的相似性计算、加工特征的相似性计算以及质量信息的相似性计算。
所述基于置信度计算的工艺知识评价方法流程为：首选要确定工艺知识各元素的优先级；然后，计算相邻工艺知识元素的置信度，输出置信度最高的工艺知识元素；最后，计算下一级工艺知识元素的置信度，直至获取所有工艺知识元素。
所述工艺设计背景知识是通过零件的基本设计信息获取。
所述工艺设计目标是通过识别加工特征而获取的，其中加工特征类型是通过预定义特征库而确定的，主要包括孔特征、槽特征、平面特征、凸台特征；刀具进给方向是基于加工面的法向量确定的；特征面矩阵是通过各特征面类型及其相邻面属性而形成的，特征面的类型包括平面、圆柱面、倒角面、球面、以及圆环面，相邻面属性是基于特征面相交边的属性确定，包括凹边、凸边和相切边；拓扑关系矩阵是通过特征面之间的相互关系确定，包括平行、垂直、倾斜和相切；基本尺寸信息是通过加工特征的最小包围盒确定，主要包括长度、宽度和高度。
所述工艺知识库的创建是基于关联规则进行组织的，其中关联规则分为单维度关联规则和多维度关联规则。
所述加工特征的相似性计算包括向量匹配度计算、矩阵匹配度计算和属性值匹配度计算，其中向量匹配度计算表达式为：
Figure PCTCN2018075382-appb-000004
conine(p,q)表示向量p和向量q之间的匹配度，i表示向量中的第i个元素，其中矩阵匹配度值可以转化为向量的匹配度计算；属性值匹配度计算表达式为：
Figure PCTCN2018075382-appb-000005
S a(a 1,a 2)表示属性值a 1和a 2的匹配度，n表示属性值所含有的元素个数，j表示属性值中的第j个元素。
所述工艺知识各元素的优先级是指加工信息层所含有元素的优先关系，其优先顺序为加工类型、加工方法、机床类型、刀具类型、夹具类型和切削液类型。
所述相邻工艺知识元素的置信度的计算公式如下：
Figure PCTCN2018075382-appb-000006
其中S con<p 1,p 2>表示工艺知识元素p 1和p 2的置信度，Preq(p 1)表示工艺知识元素p 1的总数量，Preq(p 1∩p 2)表示工艺知识元素p 1和p 2关联的总次数。
所述加工类型是用字符串表达；刀具进给方向是用向量表达；特征面组矩阵、拓扑关系矩阵是基于属性类型赋值所创建的。
工艺意图模型的创建是机加工工艺知识推送的关键。工艺意图模型有三部分构成：工艺设计背景、工艺设计目标和工艺附属信息。其中工艺设计背景是由机加工零件的基本属性信息构成的，主要包括产品族、零件类型、毛坯类型、材料类型和加工类型。如图2所示的柴油机连接件为例，该零件的工艺设计背景知识为：柴油机连接件族类零件、V12的连接件、铸造毛坯、45 #钢、NC加工。
工艺设计目标包括六部分内容：特征类型、刀具进给方向、特征面组矩阵、相邻面组矩阵、拓扑关系矩阵以及尺寸信息。为便于工艺设计目标的表达与相似性计算，各属性元素采用字母或二进制数字赋值的形式进行管理。其中加工特征类型和特征面属性的赋值如表1和表2所示；拓扑关系中平行、垂直、不垂直和相切的二进制数字赋值为：0001、0010、0011和0100；相交边属性凹边、凸边和相切边的二进制数字赋值为：0001、0010和0011，相交边类型中直线、圆弧和样条曲线的二进制数字赋值为：0001、0010和0011，为准确表达相交边信息，采用“属性+类型”的形式，如“相切边-圆弧”，其对应数值通过各元素数值相乘获得的，如“相切边-圆弧”的数值为0011×0010＝0110，若两个平面不相交则用0表示。以图2示例零件为例，基于特征识别技术获得面F1和F3为平面，F2和F4为倒角面，基于各属性元素的二进制数字赋值，所创建的特征面组矩阵、拓扑关系矩阵以及相邻面组矩阵如图3所示。
表1加工特征类型的字母赋值
Figure PCTCN2018075382-appb-000007
表2加工特征面的二进制数字赋值
Figure PCTCN2018075382-appb-000008
工艺设计意图模型是获取候选工艺知识集的基础，利用匹配方法在已有工艺知识数据库中获取相似工艺设计意图模型，随后获取所关联的工艺知识，形成候选工艺知识集。匹配相似设计意图模型的过程阐述如下：工艺设计背景的相似性计算，根据“相似零件具有相似工艺”原理，具有相似设计意图的零件必然会有相同的工艺设计背景，因此工艺设计背景包含的工艺知识无需进行相似性计算；工艺设计目标的相似性计算，它包括六项属性元素，其中特征类型和刀具进给方向需要完全匹配，因此，只需要对特征面矩阵、相邻面矩阵、拓扑关系矩阵以及尺寸信息进行相似性计算。基于企业已有的工艺模型数据库，与图2示例零件槽加工工艺相匹配的工艺模型及其相似度计算值如图4所示。根据所匹配的工艺模型，获取所关联的工艺知识，形成的候选工艺知识集如表3所示。
表3基于工艺意图模型获取的候选工艺知识集
Figure PCTCN2018075382-appb-000009
基于置信度计算值，以评价候选工艺知识集中的工艺条目，将置信度最高的 工艺知识进行推送，其置信度分别为S con<P type,P meth-turning>＝0.76、S con<P meth-turning,P mach_CK5120>＝0.75、S con<P mach,P clamp-platen>＝0.67,最终推送的工艺知识为：
roughing-turning-CK5120-platen-YT6/YG8-aqueous。

Claims (10)

  1. 一种基于加工特征的工艺知识推送方法，其特征在于，包括如下步骤：
    (1)基于加工特征向量表达方法构建工艺意图模型PPIM，PPIM的表达式为PPIM＝{PPB,PPG,API}，其中PPB为工艺设计背景，PPG为工艺设计目标，API为工艺附属信息；
    (2)步骤(1)中构建的工艺意图模型中的相似性计算方法包括：零件基础信息相似性计算、加工特征相似性计算和质量信息相似性计算；
    (3)构建工艺知识层次化表达模型，工艺知识层次化表达模型包括三个层次：基础工艺信息层、加工信息层和质量信息层；
    (4)基于置信度计算的工艺知识评价方法：利用相邻工艺属性置信度计算值，获取最优的工艺知识元素；
    (5)基于步骤(1)中得到的零件工艺意图模型和步骤(4)中的置信度值，实现工艺知识的准确推送。
  2. 根据权利要求1所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述步骤(1)中工艺意图模型的构建是根据零件设计信息和加工要求构建的，其中PPB为工艺设计背景包括零件类型、毛坯类型、材料属性和加工类型；PPG为工艺设计目标包括加工特征类型、刀具进给方向、特征面矩阵、相邻面矩阵、拓扑关系矩阵和具体尺寸信息；API为工艺附属信息包括几何精度信息和尺寸精度信息。
  3. 根据权利要求1所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述工艺知识层次化表达模型中三个层次具体为：
    基础工艺信息层包括零件类型、毛坯类型、材料属性和加工类型四个工艺元素；
    加工信息层包括加工类型、加工方法、机床类型、刀具类型、夹具类型和切削液六个工艺元素；
    质量信息层包括具体的检测要求。
  4. 根据权利要求1所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述基于置信度计算的工艺知识评价方法的具体步骤为：
    (4.1)确定工艺知识各元素的优先级；
    (4.2)计算相邻工艺知识元素的置信度，输出置信度最高的工艺知识元素；
    (4.3)计算下一级工艺知识元素的置信度，直至获取所有工艺知识元素。
  5. 根据权利要求1所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述PPB通过创建的基础设计信息获取，所述PPG通过识别加工特征获取，具体为：
    加工特征类型是通过预定义特征库确定的，主要包括孔特征、槽特征、平面特征和凸台特征；
    刀具进给方向是基于加工面的法向量确定的；
    特征面矩阵是通过各特征面类型及其相邻面属性形成的，特征面的类型包括平面、圆柱面、倒角面、球面和圆环面，相邻面属性是基于特征面相交边的属性确定，包括凹边、凸边和相切边；
    拓扑关系矩阵是通过特征面之间的相互关系确定，包括平行、垂直、倾斜和相切；
    基本尺寸信息是通过加工特征的最小包围盒确定，主要包括长度、宽度和高度。
  6. 根据权利要求1所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述步骤(2)中的加工特征相似性计算包括向量匹配度计算、矩阵匹配度计算和属性值匹配度计算三种类型：
    其中向量匹配度计算表达式为：
    Figure PCTCN2018075382-appb-100001
    conine(p,q)表示向量p和向量q之间的匹配度，i表示向量中的第i个元素，其中矩阵匹配度值可以转化为向量的匹配度计算；
    矩阵的匹配度计算方法是先将N阶矩阵转化为N维向量，通过计算向量的匹配度以实现矩阵匹配度计算。
    属性值匹配度计算表达式为：
    Figure PCTCN2018075382-appb-100002
    S a(a 1,a 2)表示属性值a 1和a 2的匹配度，n表示属性值所含有的元素个数，j 表示属性值中的第j个元素。
  7. 根据权利要求4所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述步骤(4.1)中所述工艺知识各元素的优先级是指加工信息层所含有元素的优先关系，其优先顺序为加工类型、加工方法、机床类型、刀具类型、夹具类型和切削液类型。
  8. 根据权利要求4所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述步骤(4.2)中所述相邻工艺知识元素的置信度的计算公式如下：
    Figure PCTCN2018075382-appb-100003
    其中S con<p 1,p 2>表示工艺知识元素p 1和p 2的置信度，Preq(p 1)表示工艺知识元素p 1的总数量，Preq(p 1∩p 2)表示工艺知识元素p 1和p 2关联的总次数。
  9. 根据权利要求2或5所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述加工特征类型通过字符串表达，刀具进给方向通过向量表达，特征面组矩阵和拓扑关系矩阵通过属性类型赋值创建。
  10. 根据权利要求1所述的一种基于加工特征的工艺知识推送方法，其特征在于，所述步骤(5)中基于得到的零件工艺意图模型和置信度值，实现工艺知识的准确推送的具体步骤如下：
    (5.1)通过工艺意图模型判断检索到的工艺知识中的工艺背景信息匹配度是否满足要求，如果是则进入步骤(5.2)，如果不是则继续检索直至满足要求；
    (5.2)判断工艺知识中的工艺目标信息匹配度是否满足要求，如果是则进入步骤(5.3)，如果不是则继续检索直至满足要求；
    (5.3)判断工艺知识中的工艺附属信息匹配度是否满足要求，如果是则进入步骤(5.4)，如果不是则继续检索直至满足要求；
    (5.4)输出满足要求的工艺信息列表，然后通过相邻工艺属性置信的度计算得到置信度值，最后输出并推送最优工艺知识元素。
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