CN113868860A - Part self-adaptive cost estimation method based on process knowledge - Google Patents

Part self-adaptive cost estimation method based on process knowledge Download PDF

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CN113868860A
CN113868860A CN202111137599.XA CN202111137599A CN113868860A CN 113868860 A CN113868860 A CN 113868860A CN 202111137599 A CN202111137599 A CN 202111137599A CN 113868860 A CN113868860 A CN 113868860A
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彭义兵
吴竟宁
雷若山
杜莹莹
汤旭
吴泓晋
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Huazhong University of Science and Technology
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Abstract

The invention belongs to the technical field related to digital design and manufacture, and discloses a part self-adaptive cost estimation method based on process knowledge. The method comprises the following steps: s1, identifying manufacturing characteristics in the three-dimensional structure model of the part to be processed so as to obtain all manufacturing characteristics included in the part to be processed and characteristic information corresponding to the manufacturing characteristics; s2, for each manufacturing characteristic, determining a process route corresponding to the manufacturing characteristic, and for each processing technology in the process route, selecting an optimal tool resource combination according to the tool resources required for completing the processing technology by adopting a minimum cost principle; s3, according to the process route and the tool resource combination determined in the step S2, a production total cost calculation model is constructed, and the total cost of the parts to be processed is calculated by using the cost calculation model. According to the invention, the cost estimation of the part in the design stage is realized, and unnecessary cost in product research and development is reduced.

Description

Part self-adaptive cost estimation method based on process knowledge
Technical Field
The invention belongs to the technical field related to digital design and manufacture, and particularly relates to a part self-adaptive cost estimation method based on process knowledge.
Background
Under the current situation that the industrial software and chip industry is monopolized abroad at present, the industrial 4.0 era needs to break through in the aspects of processing, assembly process, manufacturing, information management and the like. Under the support of information technologies such as big data, internet of things, cloud computing and the like, various industrial manufacturing software still have a space for improving the response capability of the design of the full life cycle dynamic demand of industrial products.
Specifically, the design for manufacturability (DFM) software and the three-dimensional process design software (3DCAPP) are included, and one of the necessary conditions for realizing the industrial software is supported by a process knowledge base. The process knowledge base provides process knowledge service, and the process knowledge is the basis of the development and innovation of the manufacturing industry. The process knowledge management can efficiently collect, arrange and utilize the process knowledge resources of enterprises. The basic process data in the process of process design and manufacture are stored in an information system in a standard and unified mode, and process knowledge with diversity and complexity is collected through effective management, so that a process knowledge base can be converted into effective productivity.
In the life cycle of the product, the input cost in the design stage accounts for only 5% of the total cost, but 70% -80% of the total cost of the product development is influenced, so that the simulation of the product cost in the design stage has an important significance for reducing the product cost. The design process of a product determines the implementation of the key functions of the product, the manufacturing costs are determined at the design stage, and their definition often affects the choice of materials, machines and human resources used in the production process. In this case, the calculation of the manufacturing cost at the design stage becomes an important task. Some minor modifications by designers in the design often result in increased costs that are only discovered later and by cost accounting personnel due to process variations caused by such modifications.
Therefore, the real-time product cost estimation technology can specifically calculate the cost of the design part in the design stage without cost accounting of process personnel and cost management personnel, the design personnel can reduce the cost by modifying the geometric characteristics of the part, the unnecessary cost in the research and development of new products is avoided, and the new products can be put into production when the new products reach or are lower than the target cost in the design stage.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a part self-adaptive cost estimation method based on process knowledge, which realizes the cost estimation of parts in the design stage and reduces unnecessary cost in product development.
To achieve the above object, according to the present invention, there is provided a part adaptive cost estimation method based on process knowledge, the method comprising the steps of:
s1, identifying manufacturing characteristics in the three-dimensional structure model of the part to be processed so as to obtain all manufacturing characteristics included in the part to be processed and characteristic information corresponding to the manufacturing characteristics;
s2, for each manufacturing feature, determining a process route corresponding to the manufacturing feature according to the feature information corresponding to the manufacturing feature, and for each processing technology in the process route, selecting an optimal tooling resource combination according to tooling resources required by the processing technology by adopting a minimum cost principle;
s3, according to the process route and the tool resource combination determined in the step S2, a production total cost calculation model is constructed, and the total cost of the parts to be processed is calculated by using the cost calculation model.
Further preferably, in step S1, the manufacturing features are obtained according to the following method:
firstly, extracting geometric topological information of the three-dimensional structure, and analyzing the geometric topological information into a graph format; secondly, comparing the geometric topological information converted into the format of the graph with a geometric characteristic graph in a preset database so as to determine the characteristic type corresponding to the geometric topological information, namely realizing the identification of the manufacturing characteristic.
Further preferably, in step S1, the characteristic information corresponding to the manufacturing characteristic includes common information and characteristic information, and the common information includes a part name, a part type, a material code, a part volume, a heat treatment mode, and a part batch; the feature information comprises a manufacturing feature name, a manufacturing feature category, a feature size value and feature process requirement information, wherein the feature process requirement information comprises surface roughness and a tolerance grade.
Further preferably, in step S2, the tooling resources include machine tools, fixtures, and consumables.
Further preferably, in step S2, the cost minimization principle is performed as follows:
the selection of the machine tool, the jig, or the consumable supplies is performed at a lowest cost when there are a plurality of machine tools, jigs, or consumable supplies that satisfy the preset conditions.
Further preferably, in step S3, the calculation model of the total production cost is performed according to the following relation:
C＝Cm+Cp+Co
wherein C is the total cost of production, CmIs the cost of the material, CpIs the processing cost, CoIs an indirect cost.
Further preferably, the material cost is calculated according to the following relation:
Cm＝vhair with bristles×ρm×wm
Wherein v isHair with bristlesIs the volume of the blank, pmIs the density of the material, wmIs the unit mass cost of the material. Further preferably, the indirect cost CoCalculated according to the following relationship:
Co＝Cf+Cw+Cc
wherein, CfIs the cost of maintenance of the tool, CwThe reject cost and CcIs a consumable cost component.
Further preferably, the processing cost CpCalculated according to the following relationship:
Figure BDA0003282877020000031
wherein, wiIs the unit time rate of machine i, TiIs the machine i operating time; w is alIs the density, T, of the materialGeneral assemblyIs the total time including machine processing time, production setup time and equipment change time, i is the machine number, n is the total number of machines.
Further preferably, the total time TGeneral assemblyCalculated according to the following relationship:
Figure BDA0003282877020000041
wherein the production preparation time TpFor and equipment change time Tc。
Generally, compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the data source of the method is a full three-dimensional part model of the part, is different from the problem that the two-dimensional CAD cannot automatically identify the pain point of the manufacturing characteristic, and provides a quantitative part cost evaluation method by combining characteristic identification and process decision aiming at that enterprises only utilize the three-dimensional CAD to store modeling information but do not apply three-dimensional labeled process information to optimization design iteration;
2. the invention provides a method for quantitatively calculating the cost based on a process knowledge base, a resource allocation process of processing is simulated based on a logic operation sequence of manufacturing characteristic processing, an automatic process decision is realized, cost data is obtained in a design stage, the method is different from the traditional complex flow of firstly designing, then planning the process and then calculating the cost, the development flow of products is favorably shortened, the cost is reduced as the target of optimizing the design, and a self-adaptive design is formed;
3. in the invention, for the condition of a plurality of results in the process decision stage, a unique decision method based on cost sequencing is adopted, because the process decision stage is not only provided with the unique decision method, and the processing scheme with the least cost is selected from a plurality of methods to be most consistent with the actual condition, so that the rise and the reduction of the cost can be intuitively and effectively evaluated when the design changes;
4. the invention further optimizes a cost quantitative calculation model, and takes the cost of consumables, the cost of equipment maintenance and the cost of waste products into consideration; the time of changing the machine and the time of preparing for production are taken into account in the calculation of labor cost, and the method is more suitable for the actual production process.
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FIG. 1 is a schematic flow diagram of a process knowledge-based part adaptive cost estimation method constructed in accordance with the present invention;
FIG. 2 is a flow chart of the manufacturing feature identification of the process knowledge based part adaptive cost estimation method constructed in accordance with the present invention;
FIG. 3 is a process decision flow diagram of a process knowledge-based part adaptive cost estimation method constructed in accordance with the present invention;
FIG. 4 is a characteristic identification process diagram of the part adaptive cost estimation method based on process knowledge, wherein (a) is a key slot characteristic diagram, (b) is a key slot characteristic attribute adjacency diagram, and (c) is a processing surface characteristic attribute adjacency diagram;
FIG. 5 is a graph of the feature recognition results of the process knowledge based part adaptive cost estimation method constructed in accordance with the present invention;
FIG. 6 is a data diagram of the feature cost calculation of the process knowledge based part adaptive cost estimation method constructed in accordance with the present invention;
FIG. 7 is a diagram of an example of a process knowledge based part adaptive cost estimation method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Aiming at the defects that the utilization of full three-dimensional model data is insufficient at the present stage and the blank of a cost estimation method of a universal mechanical part is made up, the invention provides the cost estimation method of the universal mechanical part, which takes an MBD model as a data source, decomposes the cost generation of a manufacturing process into a logic operation sequence of characteristic processing based on the established process knowledge base, and takes the logic operation sequence as a core idea of cost estimation, thereby realizing the quantitative evaluation of the cost of the universal mechanical part, and designers modify the design according to evaluation feedback, thereby realizing the requirement of self-adaptive design of products.
A part self-adaptive cost estimation method based on process knowledge comprises the following steps:
s1: the purpose of the manufacturing feature recognition is to automatically recognize a machining object having strong manufacturing semantics (holes, hole systems, grooves, straight grooves, and the like) from a CAD model including weak manufacturing semantics (points, lines, planes, and the like) and extract manufacturing requirement information related to the machining object.
The step of feature recognition is shown in fig. 2, extracting geometric topological information of the three-dimensional part model, analyzing the geometric topological information into a graph format, performing graph isomorphic matching with a predefined attribute adjacency graph of the features, and obtaining the type of the manufacturing features through a defined manufacturing feature recognition rule.
The identification result comprises public information and characteristic information, wherein the public information comprises part names, part types, material codes, part volumes, heat treatment modes and part batches; the feature information includes a manufacturing feature name, a manufacturing feature category, a feature size value, and feature process requirement information. The characteristic process requirement information comprises surface roughness and tolerance grade.
S2: a process decision step, further, extracting corresponding characteristic attribute parameters according to the identification result of the manufacturing characteristics, deciding a process route and tool resources of the manufacturing characteristics based on a process knowledge base to obtain a processing method and the process route of the characteristics, and using data of a machine tool, a cutter, a clamp and a consumable;
the process decision making step is as shown in fig. 3, and includes firstly making a feature processing method based on a feature rule base according to the identified feature result, and then matching the processing equipment meeting the conditions in a process resource base according to the feature information and the constraints of the processing method.
The knowledge of the process decision is expressed by applying a production rule method, wherein the expression comprises process route making knowledge, machine tool selection knowledge, cutter selection knowledge and the like. These knowledge represent the convention about the way in which process decision knowledge is described, which is the premise of intelligent process decision, and the basic form is:
p → Q or IF P Then Q
The meaning is "if the precondition P holds then the conclusion Q". In practical cases, the premise P and the knot may be a conjunctive of multiple sub-items, as shown in the following form:
Q＝P1^P2^...^Pn
when more than one rule exists for a result, i.e., the result set Q ═ Q1,Q2,...,Qn}. Sorting the results of the matching rules according to the lowest-cost conflict elimination strategy, and selecting the only activation rule with the minimum cost attribute, namely:
Q＝Mincost{Q1,Q2,...,Qn}
s3: cost modeling and calculation, decomposing cost into material cost CmMachining cost CpAnd other indirect costs CoCalculating the cost of the part according to the feature recognition result and the result data of the process decision and the corresponding cost calculation method, wherein the established cost model is as follows:
C＝Cm+Cp+Co
wherein, the material cost CmCalculated in terms of the volume of the blank, the density of the material and the cost per unit mass of the material, as shown below, where vHair with bristlesVolume of the blank, pmIs the density of the material, wmIs the unit mass cost of the material.
Cm＝vHair with bristles×ρm×wm
Further, the processing cost is obtained by accumulating according to the machine cost and the labor cost. The machine cost is calculated by the man-hour quota and the unit time rate of the machine, wherein the calculation of the man-hour quota refers to a technical manual, the processing man-hour of each characteristic is calculated, the corresponding machine is processed according to each characteristic, the man-hour of the machine is summarized, and the machine rate is obtained by comprehensive calculation. The labor cost is calculated according to the wages per unit time and the processing duration as follows:
Figure BDA0003282877020000071
wherein wiRate per unit time for machine i, TiWorking time of a machine i; w is alIs the density of the material, TGeneral assemblyThe total time is determined by the machining time and the production preparation time TpFor and equipment change time TcConsists of the following components:
Figure BDA0003282877020000081
further, other costs are mainly composed of tool maintenance costs, reject costs and consumable costs, as follows:
Co＝Cf+Cw+Cc
the tool maintenance cost and the consumable cost are calculated in batches, and the data and the rate of the design batches are used for calculation. The waste reporting cost is calculated by apportioning the batch of parts according to the defective rate of 1 percent.
The embodiment of the invention adopts a revolving body type mechanical part, and the method and the system provided by the invention are utilized to realize the rapid estimation process of the manufacturing cost of the part.
The CATIA V5R 19 is selected as a three-dimensional CAD platform in system development, sample parts are opened, the parts are revolving body parts and comprise typical manufacturing characteristics, outer cylindrical surfaces, holes, external threads, tool withdrawal grooves, key grooves and other characteristics and three-dimensional marking information.
Firstly, identifying manufacturing features, taking the feature identification process of the key slot as an example, as shown in fig. 4, extracting geometric information of the key slot, and preliminarily obtaining a feature attribute adjacency graph:
the key surface f1 is extracted based on the labeling information, and is further acquired as a machined surface feature attribute adjacency graph.
And identifying to obtain a result through isomorphic matching of the graph, and extracting process requirement labels and dimension data corresponding to the features. In the same way as other features, the feature recognition is performed according to the method, and the obtained feature recognition result is shown in fig. 5.
And further, making decisions on a process route and a processing tool. Taking the outer cylinder 1 in fig. 5 as an example, the process route of the feature is planned according to the feature process knowledge in the process knowledge base, and an example of matching the knowledge according to the feature parameters is shown in the following table.
Figure BDA0003282877020000082
Figure BDA0003282877020000091
And obtaining a process route of rough turning, semi-fine turning, fine turning or rough turning, semi-fine turning and grinding according to the rule example matching. Sorting according to the weight with the cost priority, and selecting the weight with the low cost priority, so that the process route is determined to be rough turning, semi-finish turning and finish turning;
and (4) making a decision on the machine tool according to the machine tool selection knowledge example, wherein the machine tool selection knowledge rule is as follows.
Machine＝P1^P2^P3,
Wherein P1 is (machine tool can support machining method — current process machining method); p2 is (maximum machining accuracy of machine > characteristic accuracy grade); p3 is (machine maximum machining size > part size); the resulting set of machine results is not unique and needs to be optimized.
Figure BDA0003282877020000092
According to the preferred principle, Machine is Mincost { M1,M2}; selecting a lathe C616A.
And similarly, selecting the cutter and other tool resources according to the method. And obtaining a decision result of the tooling resources. The data combined with the feature identification forms the complete feature cost calculation data, as shown in fig. 6. Similarly, other features are also subject to process decisions in accordance with this example.
The results of calculating the material cost, the machining cost and other costs respectively according to the formula model of the cost calculation and the introduced parameters are shown in FIG. 7.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A part adaptive cost estimation method based on process knowledge is characterized by comprising the following steps:
s1, identifying manufacturing characteristics in the three-dimensional structure model of the part to be processed so as to obtain all manufacturing characteristics included in the part to be processed and characteristic information corresponding to the manufacturing characteristics;
s2, for each manufacturing feature, determining a process route corresponding to the manufacturing feature according to the feature information corresponding to the manufacturing feature, and for each processing technology in the process route, selecting an optimal tooling resource combination according to tooling resources required by the processing technology by adopting a minimum cost principle;
s3, according to the process route and the tool resource combination determined in the step S2, a production total cost calculation model is constructed, and the total cost of the parts to be processed is calculated by using the cost calculation model.
2. The part adaptive cost estimation method based on process knowledge as claimed in claim 1, wherein in step S1, the manufacturing features are obtained according to the following method:
firstly, extracting geometric topological information of the three-dimensional structure, and analyzing the geometric topological information into a graph format; secondly, comparing the geometric topological information converted into the format of the graph with a geometric characteristic graph in a preset database so as to determine the characteristic type corresponding to the geometric topological information, namely realizing the identification of the manufacturing characteristic.
3. The part adaptive cost estimation method based on process knowledge as claimed in claim 1 or 2, wherein in step S1, the characteristic information corresponding to the manufacturing characteristic includes common information and characteristic information, the common information includes a part name, a part category, a material code, a part volume, a heat treatment manner, a part batch; the feature information comprises a manufacturing feature name, a manufacturing feature category, a feature size value and feature process requirement information, wherein the feature process requirement information comprises surface roughness and a tolerance grade.
4. The part adaptive cost estimation method based on process knowledge as claimed in claim 1, wherein in step S2, the tooling resources include machine tools, fixtures and consumables.
5. The adaptive part cost estimation method based on process knowledge as claimed in claim 4, wherein in step S2, the cost minimization principle is performed according to the following manner:
the selection of the machine tool, the jig, or the consumable supplies is performed at a lowest cost when there are a plurality of machine tools, jigs, or consumable supplies that satisfy the preset conditions.
6. The method for part adaptive cost estimation based on process knowledge as claimed in claim 1, wherein in step S3, the calculation model of the total production cost is performed according to the following relation:
C＝Cm+Cp+Co
wherein C is the total cost of production, CmIs the cost of the material, CpIs the processing cost, CoIs an indirect cost.
7. The part adaptive cost estimation method based on process knowledge as claimed in claim 6, wherein the material cost is calculated according to the following relation:
Cm＝vhair with bristles×ρm×wm
Wherein v isHair with bristlesIs the volume of the blank, pmIs the density of the material, wmIs the unit mass cost of the material.
8. The part adaptive cost estimation method based on process knowledge as claimed in claim 6, wherein the indirect cost C isoCalculated according to the following relationship:
Co＝Cf+Cw+Cc
wherein, CfIs the cost of maintenance of the tool, CwThe reject cost and CcIs a consumable cost component.
9. The part adaptive cost estimation method based on process knowledge as claimed in claim 6, wherein the machining cost CpCalculated according to the following relationship:
Figure FDA0003282877010000021
wherein, wiIs the unit time rate of machine i, TiIs the machine i operating time; w is alIs the density, T, of the materialGeneral assemblyIs the total time including machine processing time, production setup time and equipment change time, i is the machine number, n is the total number of machines.
10. The part adaptive cost estimation method based on process knowledge as claimed in claim 9, wherein the total time T isGeneral assemblyCalculated according to the following relationship:
Figure FDA0003282877010000031
wherein the production preparation time TpFor and equipment change time Tc。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116880411A (en) * 2023-08-08 2023-10-13 安徽三禾一信息科技有限公司 Collaborative manufacturing method and system for intelligent workshop

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116880411A (en) * 2023-08-08 2023-10-13 安徽三禾一信息科技有限公司 Collaborative manufacturing method and system for intelligent workshop
CN116880411B (en) * 2023-08-08 2024-02-06 安徽三禾一信息科技有限公司 Collaborative manufacturing method and system for intelligent workshop

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