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CAD Comparison Model For Data Reuse And Manufacturing Cost Estimation
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Procedia CIRP 00 (2017) Procedia CIRP 000–000 70 (2018) 59–65 www.elsevier.com/locate/procedia
28th 28th CIRP CIRP Design Design Conference, Conference, May May 2018, 2018, Nantes, Nantes, France France
CAD Model Data And Manufacturing Cost 28th CIRP DesignFor Conference, May 2018, Nantes, France CAD Comparison Comparison Model For Data Reuse Reuse And Manufacturing Cost Estimation Estimation A new methodology to analyze the functional and physical architecture of a b b b, Borhen Louhichi Mehdi Montasser Letaief existing products forTlija ana,, assembly oriented family bidentification Mehdi Tlija Montasser Billah Billah Letaiefproduct , Borhen Louhichi a a
University of Monastir, Mechanical Engineering Laboratory, National Engineering School of Monastir (LGM_ENIM), 5019 Monastir, Tunisia. University of Monastir, Mechanical Engineering Laboratory, National Engineering School of Monastir (LGM_ENIM), 5019 Monastir, Tunisia. b b University of Sousse, Mechanics Laboratory of Sousse , National Engineering School of Sousse (LMS_ENISo), 4023 Sousse, Tunisia. University of Sousse, Mechanics Laboratory of Sousse , National Engineering School of Sousse (LMS_ENISo), 4023 Sousse, Tunisia. École Nationale d’Arts et; fax:+0-000-000-0000.E-mail Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France * Corresponding author. Tel.:Supérieure +0-000-000-0000 address:[email protected] * Corresponding author. Tel.: +0-000-000-0000 ; fax:+0-000-000-0000.E-mail address:[email protected]
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract Abstract This paper proposes a novel method for data reuse and estimation of the manufacturing cost in design phase. The method is founded on the Abstract This paper proposes a novel method for data reuse and estimation of the manufacturing cost in design phase. The method is founded on the comparison, in manufacturing semantics, between the new product and company database. An Unified Feature Technology (UFT) is used as a comparison, in manufacturing semantics, between the new product and company database. An Unified Feature Technology (UFT) is used as a multidisciplinary of the reuse of product company knowledge. The developed tool is focused calculation of Inmultidisciplinary today’s businessdefinition environment, themodel trend better towards more and customization is unbroken. Due toon thisthe thesimilarity need of definition of the model better reuse of companyvariety knowledge. The developed tool is focused on thedevelopment, calculation of similarity ratioand between studied model and systems masteredemerged models tobycope the with company. The comparison modelfamilies. allows To the design reuse and of CAM andproduction cost data. agile reconfigurable production various products and product optimize ratio between studied model and mastered models by the company. The comparison model allows the reuse of CAM and cost data. Consequently, estimated and machining of studied modelmethods are deduced. The feasibility the proposed approach is validated systems as well the as to choose cost the optimal productparameters matches, product analysis are needed. Indeed,of of the known methods aim to Consequently, the estimated cost and machining parameters of studied model are deduced. The feasibility ofmost the proposed approach is validated through a case study. analyze or one product family on the physical level. Different product families, however, may differ largely in terms of the number and througha aproduct case study. © 2017 The Authors. Published by Elsevier B.V. nature ofThe components. This fact by impedes anLtd. efficient and article choice under of appropriate product family combinations for the production © Authors. Elsevier This iscomparison an open access the CC BY-NC-ND license © 2018 2017 The Authors. Published Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. (http://creativecommons.org/licenses/by-nc-nd/3.0/) system. A newunder methodology is proposed to analyze existing products in CIRP view of their functional physical architecture. The aim is to cluster Peer-review responsibility of the scientific committee of the 28th Design Conferenceand 2018. Peer-review responsibility of the scientific committee of the 28th CIRPofDesign Conference these productsunder in new assembly oriented product families for the optimization existing assembly 2018. lines and the creation of future reconfigurable Keywords: CAD; CAM; Unified Feature Technology; Comparison; Cost estimation. assembly Based on Datum Chain, the physicalCost structure of the products is analyzed. Functional subassemblies are identified, and Keywords:systems. CAD; CAM; Unified FeatureFlow Technology; Comparison; estimation. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of 1. Introduction thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. 1. Introduction 2. State of the art © 2017 The Authors. Published by Elsevier B.V. 2. State of the art The knowledge of an industrial company represents all Peer-review under responsibility of the scientific committee of the 28th The knowledge of an industrial company represents all CIRP Design Conference 2018.
necessary and sufficient data to manufacture products which necessary and sufficient data to manufacture products which least cost and least cost and in shortest time. Those data are collected during the products in shortest time. Those data are collected during the products processing in the Digital Muck-Up (DMU), from the processing in the Digital Muck-Up (DMU), from the geometric modeling phase to the marketing one. As a result, geometric modeling phase to the marketing one. As a result, digital models become rich in information and must be 1.the Introduction the digital models become rich in information and must be highlighted for better exploiation. Thus, this work presents a highlighted for better exploiation. Thus, this work presents a novel the reuse of theincompany knowledge Due approach to theallowing fast development the domain of novel approach allowing the reuse of the company knowledge during the study ofan a ongoing new product. A digitization comparison,and in communication and trend of during the study of a new product. A comparison, in manufacturingmanufacturing semantics, is enterprises established are to identify the most digitalization, facing important manufacturing semantics, is established to identify the most similar CAD the studied product anda reuse these challenges in model today’sto environments: continuing similar CAD model to market the studied product and reuse these features data to calculate its estimated cost. In this paper, the tendency reductionitsofestimated product development features towards data to calculate cost. In this times paper,and the processes of machining by material removal are considered. shortened lifecycles. In addition, thereare is considered. an increasing processesproduct of machining by material removal Thisofpaper is organizedbeing as follows. In Section 2, a review demand customization, at the same time in global This paper is organized as follows. In Section 2, a review of the literature is presented. Then, an overview of the competition with competitors all Then, over the This of trend, of the literature is presented. anworld. overview the proposed data model is described. In Section 4, the proposed which is inducing theis development from macro micro proposed data model described. In Section 4, the to proposed algorithm is exposed. Section 5 presentes a case study to algorithmresults is exposed. Section lot 5 presentes study to markets, in diminished sizes duea tocase augmenting validate the proposed tool. In section 6, a discussion is validatevarieties the proposed tool. In section 6, production) a discussion[1]. is product (high-volume to low-volume proposed. Finally, conclusions are presented. proposed. Finally, conclusionsvariety are presented. To cope with this augmenting as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2017 The Authors. Published Elsevier B.V. knowledge production is important toby a precise 2212-8271 © 2017 TheitAuthors. Published byhave Elsevier B.V. are faithful to theDesign customers with Keywords: Assembly; method;requirements Family identification are faithful to the customers requirements with
This paper describes an approach to estimate This paper describes an approach to estimate manufacturing cost of a new CAD model using a database of manufacturing cost of a new CAD model using a database of old designs. An Unified Feature Technology (UFT) is defined old designs. An Unified Feature Technology (UFT) is defined to find closely related past designs. Thus, the proposed to find closely related past designs. Thus, the proposed approach can be positioned under the research works approach can range be positioned under the research and/or works of the product and characteristics manufactured belonging to four themes: the identification and extraction belonging to four themes: the identification and extraction assembled in this system. Inand this context, the mainofchallenge in features, the comparison correspondence geometric features, the comparison and correspondence ofwith geometric modelling and analysis is now not only to cope single shapes (Feature Technology (FT)), the reuse of manufacturing shapes (Feature Technology (FT)), reuse of manufacturing products, a limited product range orthe existing data and the estimation of the product cost product families, data and the estimation of the product cost but also to analyze and toretrieval comparesearch products to define Tao toetbe al.able proposed a partial methods of Tao et al.families. proposed a partial retrieval search methods of new product It can be observed that classical features[1]. The model is based on the gradient flowsexisting in Lie features[1]. The model is based on the gradient flows in Lie product families adjacency are regrouped in function clients ormatrix features. group through relation matrix,ofgradient as group through adjacency relation matrix, matrix as However, assembly product familiesgradient are hardly to find. well as faces and oriented edges codes representations. The authors well as faces and edges codes representations. The authors On the product level, products differ in two developed anotherfamily method, in [2], founded onmainly local surface developed another method, in [2], founded on local surface region decomposition by number handlingofmodel’s face and adjacency main characteristics: (i) the components (ii) the region decomposition by handling model’s face adjacency graphs. The correspondence is established according to the type of components (e.g. mechanical, electrical, electronical). graphs. The correspondence is established according to the similarity ofmethodologies the models’ region codes.mainly Equally, Huang et al. Classical considering single products similarity of the models’ region codes. Equally, Huang et al. presented analready effectiveexisting approach to retrieving sub-parts of the 3D or solitary, product families analyze presented an effective approach to retrieving sub-parts of 3D CAD models inonorder to belevel reused in the manufacturing product structure a physical (components level) which CAD models in order to be reused in the manufacturing causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. 2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 28thDesign CIRP Conference Design Conference Peer-review under responsibility of the committee of the of 28th 2018. 2018. 10.1016/j.procir.2018.03.132
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process [3]. This approach, based on features, evaluates not only the similarity according the geometric level but also the manufacturing semantics level. The above research works [1– 3] are based on partial retrieval search approaches and does not provide a solution to estimate the manufacture cost. Murena et al. established two approaches to identify and extract features from CAD model [4]: feature recognition and design with-features. The feature extraction and recognition is based on geometric description identification. Equally, Cheng et al. presented a method of functional feature modeling [5]. This method is based on abstract geometry feature representation to guide designers building valid CAD models which respects design functionalities. Hoque et al. established a design concept basing only on manufacturing features [6]. A library of predefined features is taken into account when modeling a product. Zhenbo et al. proposed an approach to generate automatically a 3D assembly dimension chain based on the feature model [7]. The developed module is integrated into CATIA® system based on the feature attributes set concept. Holland et al. proposed an approach for feature extraction from a STEP file, in order to be used in a material deformation process [8]. The method allows estimating the product manufacturing cost. In the same context, Le et al. proposed a method for process planning design which is based on the concept of additive manufacturing features and machining features [9]. The approach targets the production of new parts by directly reusing end-of-life components or existing components. Gao et al. suggested a method allowing to use of the knowledge, formed by the history of the realized models and accumulated in history machining data model, to create a process plan [10]. In the cited works[4–10], the feature definition is limited to one DMU activity. Indeed, the feature does not include a multidisciplinary data. The comparison between CAD models is used by several authors and in different contexts: comparison for the propagation of modifications in a DMU [11], local remeshing [12], collaborative engineering [13], finite element calculation [14] and manufacturing process [10]. Louhichi et al. developed a comparing algorithm which is based mainly on faces and adjacent faces for the identification of changes between initial work package and modified work package [11]. This work targets the propagation of changes within a DMU. Cuilliere et al. proposed a new geometrical and topological descriptor to compare and localize, automatically, changes realized on models [12]. This comparison scenario is used to perform a partial mesh on modified zones. The complete mesh is avoided. The above contribution [11,12] are interested in geometry and topology, without FT, for comparison. However, Huifen et al. developed a system, based on FT, to support collaborative engineering, where collaborators can act together from anywhere. This system is founded on CAD, CAPP and CAM features [13]. These features are used as information to characterize a product. For finite element analysis, Riesenfeld R.F. et al. evaluated current CAD systems and provided recommendations for future CAD systems [14]. There commended architecture of CAD systems respects both manufacturing and analysis constraints. Kumar et al. presented a quality loss function approach to calculate total cost of product [15]. The computation model is based on Taguchi’s method. The calculation of the cost is expressed in exponential model [16]. Ghali et al. proposed an
approach allowing tolerance integration into a CAD model, while taking into account functional and manufacturing requirements in an early DMU phase [17]. The same mathematical model cited in [15–17], is used. The manufacturing cost model, in [15–17] used coefficients experimentally deduced. This model depends on tolerance values and not consider removed volumes of feature. The above contributions [13,14] confirms that the featurebased modeling is the adequate solution to accommodate the maximum of data. Thus, FT is recommoded for collaborative engineering. Thus, the feature concept is adopted in this work. The proposed system must support a multidisciplinary activities, unlike the models cited in[4–10] , and can achieve several goals. Therefore, a new UFT concept will be defined. The comparison between models is a strategy that certainly saves time in DMU as in [6,11–14]. The search of models similarity should be a global retrieval, unlike in [1–3], for a better cost estimation and a suitable CAM data reusing. The model cost, cited in [15–17] will be performed by considering, in addition to the tolerance value, the removed volume for cost computation . 3. Proposed data model The proposed approach is based on the standardization of the feature technological data structure, according to a Unified Feature Technology (UFT) model (Fig. 1), in order to compare a Reference Model (RM) with a Company Models Database (CMDB). This comparison is established in manufacturing semantics to deduce the approximate cost of RM. Topological Data CAD Data
Geometrical parameters Dimensional Tolerances General parameters
Feature Data
CAM Data
Operation Name
Machines parameters
Tool Parameters
Operations parameters
Linking Parameters Toolpath Time
True machining cost
Cost Data
Theoretical machining cost
Fig. 1. Feature data
•
CAD data: The CAD data consists on geometrical and topological structures of CAD model as well as specified dimensional tolerances. The geometrical and topological parameters are standardized. α (DF2) Depth (DF2) β (DF2) Down loop (DF2)
Top loop (DF2) DF2(δ2) DF3(δ3)
Depth (PF1) Top loop (PF1) Down loop (PF1)
PF1(δ1) PF2(δ2) DF1(δ1)
Fig. 2. Example of a CAD model containing 3 DF and 2 PF
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2017) 000–000
Two feature types (XF) are considered: Drilling Feature (DF), case of an open drill feature, and Pocket Feature (PF), case of a closed pocket. This choice is established only to simplify the approach. Fig. 2 describes the parameters of an open hole (the topology is a cylindrical face and the geometrical parameters are diameter, length, top loop, down loop as well as the two angles α and β) and a pocket (the topology is closed and the geometrical parameters are edges number, edges length, area, depth, maximum and minimum radius, top loop and down loop). The choice of tools for pocket machining depends on the radius existing on loop. • CAM Data: The CAM data consist on general parameters of the CAD model, used machines parameters and finally parameters related to the manufacturing operations (Fig.3). Each machining operation contains five types of data: machining plan, operation type, tool parameters, linking parameters and toolpath time. A pocket feature is considered performed in two operations: Pocket (PO) and Contouring (CO). Similarly, two operations are defined for the drilling feature: Centering operation (CDO) followed by a Drilling (DO) one.
Machine i
. . .
W TpPO = ( L − DT ) × RoundUp Pr
Machining Plan (MP)
Operation j
Tool Parameters
. . .
Linking Parameters
Clearance Retraction Top of Stock Depth Shifting
Toolpath Time
Toolpath Feed Time Toolpath Total Time
Fig. 3. Generalized CAM structure for a CAD Model
In CAM model, the parameter of Machining Plan (MP) is the operation principle direction. Thus, a new link between features is defined. Fig. 3 shows the generalized CAM data structure for all features. The structure of these features is considered as a standard for the company, which can be used by all engineering disciplines. • Cost Data: The feature cost data structure is formed by two parameters: empirical machining cost and real machining cost. The real cost is inserted in the database after part manufacturing. The empirical cost is computed using two proposed methods: The first method uses Eq.1 to compute the cost such as gV(δ) represents the cost of a machining function according to the removed volume with the tolerance δ (tolerance interval) [14]. C0 and C1 are determined experimentally after NC machining. Thus, Eq. 1 depends on the feature type and tolerance value. However, the feature geometry is neglected. For that, the notion of feature volume is introduced: VRM and VCM are the volume to be removed from the RM and CM features
+ (W − DT )
d ×RoundUp Pd
TpCO = 2 × ( L − DT ) + (W − DT ) Tp PFCost = Tp Starting point
Name Station Number Offset (X,Y,Z) Cut Diameter Technical-DB-ID Useful length Material Feed per Tooth Holder ID Type Tool holder Spindle Speed Feed (XY Feed) Type
(1)
RM PO CM PO
RM CO CM CO
+ Tp + Tp
Toolpath
(3)
CM × TPF × UP
Pocket outline
(2)
(4) d
Tool
DT
. . .
Operation N Machine M
Operation Type
C0 × e − C1δ VCM
Pr
CAM Data
g (δ ) = C0e − C1δ ⇒ gV (δ ) = VRM ×
Machine Definition Machining Total Time Adjustment cost Operation 1
. . .
respectively. The second technique consists on computing the toolpath length. For example, Fig. 4 and Tab. 6 describe the toolpath for a pocket operation ( TpPO ) using "ZIG ZAG" machining strategy ( TPFCM is the PF machining time for CM). The length of the toolpath is computed using the Eq. 2 for a pocket operation and Eq. 3 for a contouring operation. The linking parameters are neglected because they are quite weak of a few millimeters. The drilling toolpath is updated according depth and drilling strategy. Obviously, the rapid movement (G00) of the tool is not taken into account. The estimated cost for pocket feature machining is deduced by Eq. 4.
W
Machine 1
61 3
L End point
Pd
Fig. 4: Parameters of a finishing pocket operation with the machining strategy "ZIG ZAG"
4. Proposed algorithm The proposed approach aims to sweep the CMDB to identify the most similar model to the studied model (RM) according to features parameters. A correspondence between features of two models, RM and Most Similar CM (MSCM), is established. The approximate cost of the RM is calculated after the reuse and updating CAM data (Fig. 6). The developed model comprises five sub-algorithms: • CMDB organization: The CAD models are organized in a database (CMDB). The above models are mastered and validated by a company, i.e. CAD, CAM and cost data are known. The database is decomposed into two types: prismatic models (CMDBP) and cylindrical models (CMDBC). Each CAD model of the database is stored, in adequate database depending on model type (cylindrical or prismatic), according the uniform CAD data structure shown in Fig. 5. Thus, each feature respects the unified feature data structure presented in Fig. 1. The proposed data structure is flexible and supports additional types of parameters used in other disciplines. A code is assigned to each model of the CMDB: "nDF-mPF"; where n and m are
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2018) 000–000
the number of drilling and pocket features respectively (Fig.5). G.CAD Data
Global Data
G.CAM Data G.Cost Data
Model volume Model mass Faces number BOX dimension Material Stock Feature number (Code: nDF-mPF) Machine number Operation number Theoretical Real
Model Data
DF 1 Drilling Features
. . . .
CAD Data CAM Data Cost Data
DF i DF n
Features Data
PF 1 Pocket Features
. .
CAD Data CAM Data Cost Data
PF j . .
PF m
Fig. 5. Structure of CAD Model Data
• Comparison based on codes: This step represents the first level of comparison, based on models codes (Fig. 6). The step objective is to save enough run time compared to a direct comparison based on feature parameters. The Comparison-Based Similarity Ratio (CBSR) is defined and calculated according to Eq.5. A CBSR closer to 1 implies that the two compared models much similar via feature number. Thus, CBSR is used to lighten the mother database. The lightened base, with a reduced number of CAD models, is noted CMDBFP%. CMDBFP%, composed by models with higher CBSR, is established according to a filtration percentage, denoted by FP%. The filtration is chosen by the designer. This choice should be based on knowing the total number of models in CMDB. Indeed, in the case of a big company database, the use of reduced value of FP% is required to significantly decrease the run time. CBSR = 1 −
RM nCM − nPF + PF RM nPF
RM nDF nFeatureType
FBSRXF
DF 2 (δ=0,5) Reference Model
1)
Extraction, Classification and Codification
3)
4)
6)
7)
8)
9)
10)
11)
12)
Organization of CMDB
RM Code
Comparison based on codes
CM K Code
CBSR Computation Selection of 30% of Models
(5)
2)
5) DF1 (δ=0,3)
Data Recovery for CMDB3 0 %
Comparison Based on Features
Where nFeatureType is the number of feature types (DF and RM PF) as well as nCM DF and nDF are the number of DF in CM CM RM are the number of PF and RM respectively ( nPF and nPF in CM and RM respectively). • Comparison based on features: This sub-algorithm consists on the comparison of geometric parameters of same type features: CAD models from CMDBFP% compared to RM. The Feature-Based Similarity Ratio (FBSR) (Fig.6) is proposed to distinguish the most similar feature to RM one (Eq.6). n−1 XF CM − XF RM i i ∑ XFi RM i =1 × 0.5 n −1 = 1 − CM RM XFδ − XFδ × 0.5 + RM XF δ
PF1 (δ=0,2)
FBSR,MFBSR and GFBSR Computation
RM: 2DF-1PF CM8 : 1DF-1PF 1−
1− 2 1−1 + 2 1 = 0,75 2
Correspondent Feature
RM nCM DF − nDF
•
The feature tolerance ratio represents 50% of the FBSR. This choice is due to that tolerance is considered the most important feature parameter, ie. the cost of feature machining highly depends on assigned tolerance value. Therefore, two features are similar when their tolerances are as nigh as possible and the cost will be proportional to the volumes. The maximum of FBSR of each feature type, noted by MFBSR, is determined. Then, the Global FBSR (GFBSR), for each CM, is the average of all MFBSR. Therefore, the MSCM to RM has the largest GFBSR. Correspondance between features: The interactions between features, of the same model are respected through MP requirement: the features to be executed in the same phase have a parallel MP. For exemple, in Fig. 7 shows that DF1a and DF2b are not totally corresponding because the MP of DF1a (MP( DF1a )) is perpondicular to MP of PF1a (MP( PF1a )). On the other hand, DF2b and PF1b have the same MP. Thus, the CAM parameters of DF1a are used but it requires one more phase during machining. In the same Figure, DF1a and DF2b are totally corresponding.
Topology
62 4
Selection of similar CM (CM 5 ) Features Correspondence Condition 1
Reuse and Updating Parameters
CAM parameters and Cost Association
Condition 2 CAM parameters and Cost Update
Cost RM Computation
Fig.6. Result of the application of proposed algorithm
(6)
Where n is the number of feature parameters, XFiCM is the ith CM parameter, XFi RM is the ith RM parameter, XF is the feature type as well as XFδCM and XFδRM are the tolerance δ of XF.
The correspondence of MSCM with RM features is based on FBSR ratio: The ith RM Feature corresponds to jth MSCM feature which admits maximal FBSR ratio. If one of RM feature does not correspondent (FBSR=0) to any MSCM feature, a return to CMDBFP% is necessary. The highest ratio in the others CAD model of CMDBFP% is identified. If no correspondance is found, the CMDB, for the same model type, is used to look for the first model code that contains feature with the same topology. The Global Similarity Ratio of RM, which is the average of all
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2017) 000–000
FBSR MSCM features, is deduced. The CAM and cost data of the features identified by correspondence are recovered. a. RM
b. CM5
MP(PF1)
DF2
PF1 MP(PF1)=MP(DF1) =MP(DF2)
DF2
DF1 PF1
DF1 MP(DF1)
Fig.7. Machining plan correspondence
• Updating parameters and manufacturing cost estimation: Following correspondence, the calculation of the tool parameters is requested if the materials of the two models are different. The CAM and Cost data are updated and reused according to the following two conditions in order that the approximate cost of RM can be calculated: Condition 1: If the topological and geometrical parameters as well as dimensional tolerance value are identical, then a direct association of CAM parameters and cost of MSCM features to RM features is established. Condition 2: If only the topological parameters are identical, the CAM parameters are updated according to the geometrical parameters and tolerance value of RM features. 5. Case study In this section, a case study is presented to test the proposed algorithm. 12 CAD models are chosen to form CMDB as shown in Fig. 6. The sub-algorithm of CMDB organization allows the codification of the 12 models. The RM features data are extracted, classified and codified. The RM includes two drilling and one pocket (2DF-1PF). Thereafter, a comparison based on codes is established. The CBSR is allocated to each CM of the CMDB. As a result, a refined database is filtered from CMDBP with a filtration percentage chosen equal to 30%. In the case study, FP% value has a reduced impact on the total run time of the algorithm, unlike the big data case. FP% is used to show the algorithm operation and to simplify the illustration of the subsequent results. Table. 1. Drilling Features parameters.
Diameter (mm)
Length (mm)
Tolerance (mm)
RM-DF1
6,6
10
0,3
RM-DF2
11
20
0,5
CM3-DF1
11
25
0,3
CM3-DF2
11
25
0,1
CM5-DF1
13,5
28
0,4
CM5-DF2
9
28
0,2
CM9-DF1
15,5
30
0,4
CM9-DF2
15,5
30
0,5
CM11-DF1
11
51,718
0,3
CM11-DF2
11
51,718
0,4
Based on geometric parameters of CAD data of drilling features (Tab.1) and pocket features (Tab. 2), the FBSR is
63 5
computed (Tab. 3). Then, the MFBSR is determined. Each MFBSR represents the maximum value of FBSR for each RM features (Fig. 6). After that, GFBSR is computed. Subsequently, the CM5, with GFBSR equal to 0,26, is selected as the MSCM. A correspondence between RM and CM5 features is established according the fourth sub-algorithm (correspondence between features) (Fig. 7). Consequently, the Global Similarity Ratio of RM is deduced that is equal to 0,34. According to the machining plans of the three features, the machining order is: (1) PF1 and (2) DF2 are executed in the first phase, and (3) DF1 is executed in the second one. Table. 2. Pocket Features parameters.
RM-PF1 CM3-PF1 CM5-PF1 CM9-PF1 CM11-PF1
Depth (mm) 10 12 13 10 4
Area (mm²) 2291,82 4029,51 2482,12 3209,34 1312,44
Edges Length (mm) 206,248 262,91 212,26 225,54 139,27
Edges Nb. 4 12 4 8 8
Tolerance (mm) 0,2 0,4 0,5 0,2 0,2
Table. 3 . Results of FBSR computation
RM-DF1 RM-DF2
RM-PF1
CM3-DF1
0,598
0,565
CM3-PF1
-1,116
CM3-DF2
0,265
0,365
CM5-PF1
0,044
CM5-DF1
0,174
0,463
CM9-PF1
0,253
CM5-DF2
0,515
0,286
CM11-PF1
-0,176
CM9-DF1
-0,023
0,313
CM9-DF2
-0,189
0,413
CM11-DF1 -2,069
-2,887
CM11-DF2 -2,236
-2,787
Table 4. Computation RM theoretical machining cost
C0'
C1
RM-DF1 2101857,12 1,7329 RM-DF2 2101857,12 1,7329 RM-PF1 1324643,01 1,9711 Manufacturing cost Stock cost Adjustment cost Model cost
Tolerance (mm) 0,2 0,2 0,3
Volume (mm3) 3,42E-07 1,90E-06 2,87E-05
Feature Cost (DT) 0,508 2,823 21,067 24,399 5,616 3 33,015
Using condition 2 described previously, the CAM and Cost data parameters are updated (Fig. 6). The machining cost estimation is calculated. At this step, the two proposed methods for the approximate cost calculation are used. The first method is devoted to determine the cost of DFs and PFs using Eq. 1 whither constants C0 and C1 are identified (Tab. 4). In this case, the second method is applied using "ZIGZAG" and "Peck" strategies for PFs and DFs respectively. The toolpath time for the four operations (pocket, contouring, centering and drilling), and the feature approximate cost are shown in Tab. 5 and Tab. 6. Finally, the manufacturing cost of RM features is deducted by summing all features costs. The product cost is the adding of stock cost (which is computed by multiplying the unit price with CAD model volume) and total adjustment cost of all phases (This cost is unitary for each machining phase where adjustment is needed only once for each "Machining plan") to the features
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2018) 000–000
64 6
manufacturing cost. The cost difference is 9.6%. This is a small difference. In this case study, there is not one method better than the others. Table 5 . Time toolpath computation
Parameter
CM-PF1
RM-PF1
Pr
8
8
Pd
1
1
DT
16
16
W
32
30
L
73
56
d
13
10
3172
1740
21min8s
13min39s
Toolpath PO ( TpPO
)
Machining Time of PO Toolpath CO ( TpCO
)
Machining Time of CO Toolpath CDO ( TpCDO
)
Machining Time of CDO Toolpath DO ( TpDO
)
Machining Time of DO
146
1080
9s
51s
12
12
3s
3s
110
316
32s
1min31s
Table 6. The RM estimate cost using second method Unit price (DT/h)
80,000
RM manufacturing time
16min04s
Cost PF1 (DT)
24,090
Cost DF1 (DT)
1,911
Cost DF2 (DT)
1,911
relatively high (about 11 min) even for a small FP% (20%). The identification and the comparaison steps are based on the geometric and topological parameters in manufacturing semantic. The operation parameter MP is used as an interaction between features during the correspondence step. Among the above disadvantages of this approach, the reinforcement of RM features links is required [3,18]. Also, the position tolerance and process plan must be considered in the proposed algorithm because those constrains affects the cost calculation. These limitations do not diminish the importance of the proposed tool, but they must be taken into account to consolidate its reliability. 7. Conclusion and perspectives In this paper, a new comparison approach between a studied CAD model and a manufactured CAD models database (CMDB), is presented using UFT. As a result, CAM as well as cost data are retrieved, updated and reused to estimate the RM estimated cost. The RM is prepared to be machined with time benefit. In future works, the proposed approach can be generalized for other feature types to be exploited in the future industry as additive manufacturing. The developped tool can be applied to CAD models in "STEP" format on using the Product and Manufacturing Information (PMI) representation [19] or "STEP-NC" format [20,21]. The proposed UFT is flexible and can support other constraints : positional tolerance, process plan and other manufacturing environment. References
5.1.1. RM Features manufacturing cost (DT)
27,912
5.1.2. Adjustment cost (DT)
3,000
[1] S. Tao, Z. Huang, B. Zuo, Y. Peng, W. Kang, Partial retrieval of CAD models based on the gradient flows in Lie group, Pattern Recognit. 45
5.1.3. Stock Cost (DT)
5,616
5.1.4. RM Cost (DT)
36,528
6. Discussion The classical approach of manufacturing product comprises three steps: CAD, CAM and machining execution in the workshop. The resolution of problems, encountered during machining models, forms the expertise of the company. This experience and know-how must be highlighted during studing a new product. From this fact, an improvement of manufacturing approach, benefiting from the company's knowlege, is proposed. As a result, the machining parameters are defined and manufacturing cost is estmated since the design phase. The empirical method, allowing the estimation of the cost of RM, is adapted to features that have different tolerances. On the other hand, the method based on the calculation of the toolpath is suitable for features that have different geometries. The run time of the case study is about 34 seconds. The proposed method is tested, also, in the case of a big CMDB (500 complex models (an average of 15 features per model). The run time decreases according to FP%, but remains
(2012) 1721–1738. [2] S. Tao, Z. Huang, L. Ma, S. Guo, S. Wang, Y. Xie, Partial retrieval of CAD models based on local surface region decomposition, CAD Comput. Aided Des. 45 (2013) 1239–1252. [3] H. Rui, Z. Shusheng, B. Xiaoliang, X. Changhong, H. Bo, An effective subpart retrieval approach of 3D CAD models for manufacturing process reuse, Comput. Ind. 67 (2015) 38–53. [4] E. Murena, K. Mpofu, J. Trimble, N. Gwangwava, Model for Developing a Feature Recogntion System for A Reconfigurable Bending Press Machine, Procedia CIRP. 63 (2017) 533–538. [5] Z. Cheng, Y. Ma, A functional feature modeling method, Adv. Eng. Informatics. 33 (2017) 1–15. [6] A.S.M. Hoque, T. Szecsi, Designing using manufacturing feature library, J. Mater. Process. Technol. 1 (2008) 204–208. [7] G. Zhenbo, W. Jing, C. Yanlong, Y. Jiangxin, Automatic Generation of 3D Assembly Dimension Chain Based on Feature Model, Procedia CIRP. 43 (2016) 70–75. [8] P. Holland, P.M. Standring, H. Long, D.J. Mynors, Feature extraction from STEP (ISO 10303) CAD drawing files for metalforming process selection in an integrated design system, J. Mater. Process. Technol. 125– 126 (2002) 446–455. [9] V.T. Le, H. Paris, G. Mandil, D. Brissaud, A Direct Material Reuse Approach
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manufacturing environments., Comput. Methods Appl. Mech. Eng. 284 (2015) 1054–1072. [15] S.R. Kumar, N. Alagumurthi, R. Ramesh, Calculation of Total Cost , Tolerance Based on Taguchi ’ s , Asymmetric Quality Loss Function Approach, Am. J. Eng. Appl. Sci. 2 (2009) 628–634. [16] R. Sampath, N. Alagumurthi, R. Ramesh, Optimization of design tolerance and asymmetric quality loss cost using pattern search algorithm, Int. J. Phys. Sci. 4 (2009) 629–637. [17] M. Ghali, M. Tlija, N. Aifaoui, E. Pairel, A CAD method for tolerance allocation considering manufacturing difficulty based on FMECA tool, Int. J. Adv. Manuf. Technol. (2017). [18] J. Tao, Z. Chen, S. Yu, Z. Liu, Integration of Life Cycle Assessment with computer-aided product development by a feature-based approach, J. Clean. Prod. 143 (2017) 1144–1164. [19] R. Lipman, J. Lubell, Conformance checking of PMI representation in CAD model STEP data exchange files, CAD Comput. Aided Des. 66 (2015) 14–23. [20] C. Danjou, J. Le Duigou, B. Eynard, Closed-loop Manufacturing, a STEP-NC Process for Data Feedback: A Case Study, Procedia CIRP. 41 (2016) 852–857. [21] J. Pobozniak, S. Sobieski, Extension of STEP-NC Data Structure to Represent Manufacturing Process Structure in CAPP System, Procedia Manuf. 11 (2017) 1692–1699.
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28th 28th CIRP CIRP Design Design Conference, Conference, May May 2018, 2018, Nantes, Nantes, France France
CAD Model Data And Manufacturing Cost 28th CIRP DesignFor Conference, May 2018, Nantes, France CAD Comparison Comparison Model For Data Reuse Reuse And Manufacturing Cost Estimation Estimation A new methodology to analyze the functional and physical architecture of a b b b, Borhen Louhichi Mehdi Montasser Letaief existing products forTlija ana,, assembly oriented family bidentification Mehdi Tlija Montasser Billah Billah Letaiefproduct , Borhen Louhichi a a
University of Monastir, Mechanical Engineering Laboratory, National Engineering School of Monastir (LGM_ENIM), 5019 Monastir, Tunisia. University of Monastir, Mechanical Engineering Laboratory, National Engineering School of Monastir (LGM_ENIM), 5019 Monastir, Tunisia. b b University of Sousse, Mechanics Laboratory of Sousse , National Engineering School of Sousse (LMS_ENISo), 4023 Sousse, Tunisia. University of Sousse, Mechanics Laboratory of Sousse , National Engineering School of Sousse (LMS_ENISo), 4023 Sousse, Tunisia. École Nationale d’Arts et; fax:+0-000-000-0000.E-mail Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France * Corresponding author. Tel.:Supérieure +0-000-000-0000 address:[email protected] * Corresponding author. Tel.: +0-000-000-0000 ; fax:+0-000-000-0000.E-mail address:[email protected]
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract Abstract This paper proposes a novel method for data reuse and estimation of the manufacturing cost in design phase. The method is founded on the Abstract This paper proposes a novel method for data reuse and estimation of the manufacturing cost in design phase. The method is founded on the comparison, in manufacturing semantics, between the new product and company database. An Unified Feature Technology (UFT) is used as a comparison, in manufacturing semantics, between the new product and company database. An Unified Feature Technology (UFT) is used as a multidisciplinary of the reuse of product company knowledge. The developed tool is focused calculation of Inmultidisciplinary today’s businessdefinition environment, themodel trend better towards more and customization is unbroken. Due toon thisthe thesimilarity need of definition of the model better reuse of companyvariety knowledge. The developed tool is focused on thedevelopment, calculation of similarity ratioand between studied model and systems masteredemerged models tobycope the with company. The comparison modelfamilies. allows To the design reuse and of CAM andproduction cost data. agile reconfigurable production various products and product optimize ratio between studied model and mastered models by the company. The comparison model allows the reuse of CAM and cost data. Consequently, estimated and machining of studied modelmethods are deduced. The feasibility the proposed approach is validated systems as well the as to choose cost the optimal productparameters matches, product analysis are needed. Indeed,of of the known methods aim to Consequently, the estimated cost and machining parameters of studied model are deduced. The feasibility ofmost the proposed approach is validated through a case study. analyze or one product family on the physical level. Different product families, however, may differ largely in terms of the number and througha aproduct case study. © 2017 The Authors. Published by Elsevier B.V. nature ofThe components. This fact by impedes anLtd. efficient and article choice under of appropriate product family combinations for the production © Authors. Elsevier This iscomparison an open access the CC BY-NC-ND license © 2018 2017 The Authors. Published Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. (http://creativecommons.org/licenses/by-nc-nd/3.0/) system. A newunder methodology is proposed to analyze existing products in CIRP view of their functional physical architecture. The aim is to cluster Peer-review responsibility of the scientific committee of the 28th Design Conferenceand 2018. Peer-review responsibility of the scientific committee of the 28th CIRPofDesign Conference these productsunder in new assembly oriented product families for the optimization existing assembly 2018. lines and the creation of future reconfigurable Keywords: CAD; CAM; Unified Feature Technology; Comparison; Cost estimation. assembly Based on Datum Chain, the physicalCost structure of the products is analyzed. Functional subassemblies are identified, and Keywords:systems. CAD; CAM; Unified FeatureFlow Technology; Comparison; estimation. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of 1. Introduction thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. 1. Introduction 2. State of the art © 2017 The Authors. Published by Elsevier B.V. 2. State of the art The knowledge of an industrial company represents all Peer-review under responsibility of the scientific committee of the 28th The knowledge of an industrial company represents all CIRP Design Conference 2018.
necessary and sufficient data to manufacture products which necessary and sufficient data to manufacture products which least cost and least cost and in shortest time. Those data are collected during the products in shortest time. Those data are collected during the products processing in the Digital Muck-Up (DMU), from the processing in the Digital Muck-Up (DMU), from the geometric modeling phase to the marketing one. As a result, geometric modeling phase to the marketing one. As a result, digital models become rich in information and must be 1.the Introduction the digital models become rich in information and must be highlighted for better exploiation. Thus, this work presents a highlighted for better exploiation. Thus, this work presents a novel the reuse of theincompany knowledge Due approach to theallowing fast development the domain of novel approach allowing the reuse of the company knowledge during the study ofan a ongoing new product. A digitization comparison,and in communication and trend of during the study of a new product. A comparison, in manufacturingmanufacturing semantics, is enterprises established are to identify the most digitalization, facing important manufacturing semantics, is established to identify the most similar CAD the studied product anda reuse these challenges in model today’sto environments: continuing similar CAD model to market the studied product and reuse these features data to calculate its estimated cost. In this paper, the tendency reductionitsofestimated product development features towards data to calculate cost. In this times paper,and the processes of machining by material removal are considered. shortened lifecycles. In addition, thereare is considered. an increasing processesproduct of machining by material removal Thisofpaper is organizedbeing as follows. In Section 2, a review demand customization, at the same time in global This paper is organized as follows. In Section 2, a review of the literature is presented. Then, an overview of the competition with competitors all Then, over the This of trend, of the literature is presented. anworld. overview the proposed data model is described. In Section 4, the proposed which is inducing theis development from macro micro proposed data model described. In Section 4, the to proposed algorithm is exposed. Section 5 presentes a case study to algorithmresults is exposed. Section lot 5 presentes study to markets, in diminished sizes duea tocase augmenting validate the proposed tool. In section 6, a discussion is validatevarieties the proposed tool. In section 6, production) a discussion[1]. is product (high-volume to low-volume proposed. Finally, conclusions are presented. proposed. Finally, conclusionsvariety are presented. To cope with this augmenting as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2017 The Authors. Published Elsevier B.V. knowledge production is important toby a precise 2212-8271 © 2017 TheitAuthors. Published byhave Elsevier B.V. are faithful to theDesign customers with Keywords: Assembly; method;requirements Family identification are faithful to the customers requirements with
This paper describes an approach to estimate This paper describes an approach to estimate manufacturing cost of a new CAD model using a database of manufacturing cost of a new CAD model using a database of old designs. An Unified Feature Technology (UFT) is defined old designs. An Unified Feature Technology (UFT) is defined to find closely related past designs. Thus, the proposed to find closely related past designs. Thus, the proposed approach can be positioned under the research works approach can range be positioned under the research and/or works of the product and characteristics manufactured belonging to four themes: the identification and extraction belonging to four themes: the identification and extraction assembled in this system. Inand this context, the mainofchallenge in features, the comparison correspondence geometric features, the comparison and correspondence ofwith geometric modelling and analysis is now not only to cope single shapes (Feature Technology (FT)), the reuse of manufacturing shapes (Feature Technology (FT)), reuse of manufacturing products, a limited product range orthe existing data and the estimation of the product cost product families, data and the estimation of the product cost but also to analyze and toretrieval comparesearch products to define Tao toetbe al.able proposed a partial methods of Tao et al.families. proposed a partial retrieval search methods of new product It can be observed that classical features[1]. The model is based on the gradient flowsexisting in Lie features[1]. The model is based on the gradient flows in Lie product families adjacency are regrouped in function clients ormatrix features. group through relation matrix,ofgradient as group through adjacency relation matrix, matrix as However, assembly product familiesgradient are hardly to find. well as faces and oriented edges codes representations. The authors well as faces and edges codes representations. The authors On the product level, products differ in two developed anotherfamily method, in [2], founded onmainly local surface developed another method, in [2], founded on local surface region decomposition by number handlingofmodel’s face and adjacency main characteristics: (i) the components (ii) the region decomposition by handling model’s face adjacency graphs. The correspondence is established according to the type of components (e.g. mechanical, electrical, electronical). graphs. The correspondence is established according to the similarity ofmethodologies the models’ region codes.mainly Equally, Huang et al. Classical considering single products similarity of the models’ region codes. Equally, Huang et al. presented analready effectiveexisting approach to retrieving sub-parts of the 3D or solitary, product families analyze presented an effective approach to retrieving sub-parts of 3D CAD models inonorder to belevel reused in the manufacturing product structure a physical (components level) which CAD models in order to be reused in the manufacturing causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. 2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 28thDesign CIRP Conference Design Conference Peer-review under responsibility of the committee of the of 28th 2018. 2018. 10.1016/j.procir.2018.03.132
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Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2018) 000–000
process [3]. This approach, based on features, evaluates not only the similarity according the geometric level but also the manufacturing semantics level. The above research works [1– 3] are based on partial retrieval search approaches and does not provide a solution to estimate the manufacture cost. Murena et al. established two approaches to identify and extract features from CAD model [4]: feature recognition and design with-features. The feature extraction and recognition is based on geometric description identification. Equally, Cheng et al. presented a method of functional feature modeling [5]. This method is based on abstract geometry feature representation to guide designers building valid CAD models which respects design functionalities. Hoque et al. established a design concept basing only on manufacturing features [6]. A library of predefined features is taken into account when modeling a product. Zhenbo et al. proposed an approach to generate automatically a 3D assembly dimension chain based on the feature model [7]. The developed module is integrated into CATIA® system based on the feature attributes set concept. Holland et al. proposed an approach for feature extraction from a STEP file, in order to be used in a material deformation process [8]. The method allows estimating the product manufacturing cost. In the same context, Le et al. proposed a method for process planning design which is based on the concept of additive manufacturing features and machining features [9]. The approach targets the production of new parts by directly reusing end-of-life components or existing components. Gao et al. suggested a method allowing to use of the knowledge, formed by the history of the realized models and accumulated in history machining data model, to create a process plan [10]. In the cited works[4–10], the feature definition is limited to one DMU activity. Indeed, the feature does not include a multidisciplinary data. The comparison between CAD models is used by several authors and in different contexts: comparison for the propagation of modifications in a DMU [11], local remeshing [12], collaborative engineering [13], finite element calculation [14] and manufacturing process [10]. Louhichi et al. developed a comparing algorithm which is based mainly on faces and adjacent faces for the identification of changes between initial work package and modified work package [11]. This work targets the propagation of changes within a DMU. Cuilliere et al. proposed a new geometrical and topological descriptor to compare and localize, automatically, changes realized on models [12]. This comparison scenario is used to perform a partial mesh on modified zones. The complete mesh is avoided. The above contribution [11,12] are interested in geometry and topology, without FT, for comparison. However, Huifen et al. developed a system, based on FT, to support collaborative engineering, where collaborators can act together from anywhere. This system is founded on CAD, CAPP and CAM features [13]. These features are used as information to characterize a product. For finite element analysis, Riesenfeld R.F. et al. evaluated current CAD systems and provided recommendations for future CAD systems [14]. There commended architecture of CAD systems respects both manufacturing and analysis constraints. Kumar et al. presented a quality loss function approach to calculate total cost of product [15]. The computation model is based on Taguchi’s method. The calculation of the cost is expressed in exponential model [16]. Ghali et al. proposed an
approach allowing tolerance integration into a CAD model, while taking into account functional and manufacturing requirements in an early DMU phase [17]. The same mathematical model cited in [15–17], is used. The manufacturing cost model, in [15–17] used coefficients experimentally deduced. This model depends on tolerance values and not consider removed volumes of feature. The above contributions [13,14] confirms that the featurebased modeling is the adequate solution to accommodate the maximum of data. Thus, FT is recommoded for collaborative engineering. Thus, the feature concept is adopted in this work. The proposed system must support a multidisciplinary activities, unlike the models cited in[4–10] , and can achieve several goals. Therefore, a new UFT concept will be defined. The comparison between models is a strategy that certainly saves time in DMU as in [6,11–14]. The search of models similarity should be a global retrieval, unlike in [1–3], for a better cost estimation and a suitable CAM data reusing. The model cost, cited in [15–17] will be performed by considering, in addition to the tolerance value, the removed volume for cost computation . 3. Proposed data model The proposed approach is based on the standardization of the feature technological data structure, according to a Unified Feature Technology (UFT) model (Fig. 1), in order to compare a Reference Model (RM) with a Company Models Database (CMDB). This comparison is established in manufacturing semantics to deduce the approximate cost of RM. Topological Data CAD Data
Geometrical parameters Dimensional Tolerances General parameters
Feature Data
CAM Data
Operation Name
Machines parameters
Tool Parameters
Operations parameters
Linking Parameters Toolpath Time
True machining cost
Cost Data
Theoretical machining cost
Fig. 1. Feature data
•
CAD data: The CAD data consists on geometrical and topological structures of CAD model as well as specified dimensional tolerances. The geometrical and topological parameters are standardized. α (DF2) Depth (DF2) β (DF2) Down loop (DF2)
Top loop (DF2) DF2(δ2) DF3(δ3)
Depth (PF1) Top loop (PF1) Down loop (PF1)
PF1(δ1) PF2(δ2) DF1(δ1)
Fig. 2. Example of a CAD model containing 3 DF and 2 PF
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2017) 000–000
Two feature types (XF) are considered: Drilling Feature (DF), case of an open drill feature, and Pocket Feature (PF), case of a closed pocket. This choice is established only to simplify the approach. Fig. 2 describes the parameters of an open hole (the topology is a cylindrical face and the geometrical parameters are diameter, length, top loop, down loop as well as the two angles α and β) and a pocket (the topology is closed and the geometrical parameters are edges number, edges length, area, depth, maximum and minimum radius, top loop and down loop). The choice of tools for pocket machining depends on the radius existing on loop. • CAM Data: The CAM data consist on general parameters of the CAD model, used machines parameters and finally parameters related to the manufacturing operations (Fig.3). Each machining operation contains five types of data: machining plan, operation type, tool parameters, linking parameters and toolpath time. A pocket feature is considered performed in two operations: Pocket (PO) and Contouring (CO). Similarly, two operations are defined for the drilling feature: Centering operation (CDO) followed by a Drilling (DO) one.
Machine i
. . .
W TpPO = ( L − DT ) × RoundUp Pr
Machining Plan (MP)
Operation j
Tool Parameters
. . .
Linking Parameters
Clearance Retraction Top of Stock Depth Shifting
Toolpath Time
Toolpath Feed Time Toolpath Total Time
Fig. 3. Generalized CAM structure for a CAD Model
In CAM model, the parameter of Machining Plan (MP) is the operation principle direction. Thus, a new link between features is defined. Fig. 3 shows the generalized CAM data structure for all features. The structure of these features is considered as a standard for the company, which can be used by all engineering disciplines. • Cost Data: The feature cost data structure is formed by two parameters: empirical machining cost and real machining cost. The real cost is inserted in the database after part manufacturing. The empirical cost is computed using two proposed methods: The first method uses Eq.1 to compute the cost such as gV(δ) represents the cost of a machining function according to the removed volume with the tolerance δ (tolerance interval) [14]. C0 and C1 are determined experimentally after NC machining. Thus, Eq. 1 depends on the feature type and tolerance value. However, the feature geometry is neglected. For that, the notion of feature volume is introduced: VRM and VCM are the volume to be removed from the RM and CM features
+ (W − DT )
d ×RoundUp Pd
TpCO = 2 × ( L − DT ) + (W − DT ) Tp PFCost = Tp Starting point
Name Station Number Offset (X,Y,Z) Cut Diameter Technical-DB-ID Useful length Material Feed per Tooth Holder ID Type Tool holder Spindle Speed Feed (XY Feed) Type
(1)
RM PO CM PO
RM CO CM CO
+ Tp + Tp
Toolpath
(3)
CM × TPF × UP
Pocket outline
(2)
(4) d
Tool
DT
. . .
Operation N Machine M
Operation Type
C0 × e − C1δ VCM
Pr
CAM Data
g (δ ) = C0e − C1δ ⇒ gV (δ ) = VRM ×
Machine Definition Machining Total Time Adjustment cost Operation 1
. . .
respectively. The second technique consists on computing the toolpath length. For example, Fig. 4 and Tab. 6 describe the toolpath for a pocket operation ( TpPO ) using "ZIG ZAG" machining strategy ( TPFCM is the PF machining time for CM). The length of the toolpath is computed using the Eq. 2 for a pocket operation and Eq. 3 for a contouring operation. The linking parameters are neglected because they are quite weak of a few millimeters. The drilling toolpath is updated according depth and drilling strategy. Obviously, the rapid movement (G00) of the tool is not taken into account. The estimated cost for pocket feature machining is deduced by Eq. 4.
W
Machine 1
61 3
L End point
Pd
Fig. 4: Parameters of a finishing pocket operation with the machining strategy "ZIG ZAG"
4. Proposed algorithm The proposed approach aims to sweep the CMDB to identify the most similar model to the studied model (RM) according to features parameters. A correspondence between features of two models, RM and Most Similar CM (MSCM), is established. The approximate cost of the RM is calculated after the reuse and updating CAM data (Fig. 6). The developed model comprises five sub-algorithms: • CMDB organization: The CAD models are organized in a database (CMDB). The above models are mastered and validated by a company, i.e. CAD, CAM and cost data are known. The database is decomposed into two types: prismatic models (CMDBP) and cylindrical models (CMDBC). Each CAD model of the database is stored, in adequate database depending on model type (cylindrical or prismatic), according the uniform CAD data structure shown in Fig. 5. Thus, each feature respects the unified feature data structure presented in Fig. 1. The proposed data structure is flexible and supports additional types of parameters used in other disciplines. A code is assigned to each model of the CMDB: "nDF-mPF"; where n and m are
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2018) 000–000
the number of drilling and pocket features respectively (Fig.5). G.CAD Data
Global Data
G.CAM Data G.Cost Data
Model volume Model mass Faces number BOX dimension Material Stock Feature number (Code: nDF-mPF) Machine number Operation number Theoretical Real
Model Data
DF 1 Drilling Features
. . . .
CAD Data CAM Data Cost Data
DF i DF n
Features Data
PF 1 Pocket Features
. .
CAD Data CAM Data Cost Data
PF j . .
PF m
Fig. 5. Structure of CAD Model Data
• Comparison based on codes: This step represents the first level of comparison, based on models codes (Fig. 6). The step objective is to save enough run time compared to a direct comparison based on feature parameters. The Comparison-Based Similarity Ratio (CBSR) is defined and calculated according to Eq.5. A CBSR closer to 1 implies that the two compared models much similar via feature number. Thus, CBSR is used to lighten the mother database. The lightened base, with a reduced number of CAD models, is noted CMDBFP%. CMDBFP%, composed by models with higher CBSR, is established according to a filtration percentage, denoted by FP%. The filtration is chosen by the designer. This choice should be based on knowing the total number of models in CMDB. Indeed, in the case of a big company database, the use of reduced value of FP% is required to significantly decrease the run time. CBSR = 1 −
RM nCM − nPF + PF RM nPF
RM nDF nFeatureType
FBSRXF
DF 2 (δ=0,5) Reference Model
1)
Extraction, Classification and Codification
3)
4)
6)
7)
8)
9)
10)
11)
12)
Organization of CMDB
RM Code
Comparison based on codes
CM K Code
CBSR Computation Selection of 30% of Models
(5)
2)
5) DF1 (δ=0,3)
Data Recovery for CMDB3 0 %
Comparison Based on Features
Where nFeatureType is the number of feature types (DF and RM PF) as well as nCM DF and nDF are the number of DF in CM CM RM are the number of PF and RM respectively ( nPF and nPF in CM and RM respectively). • Comparison based on features: This sub-algorithm consists on the comparison of geometric parameters of same type features: CAD models from CMDBFP% compared to RM. The Feature-Based Similarity Ratio (FBSR) (Fig.6) is proposed to distinguish the most similar feature to RM one (Eq.6). n−1 XF CM − XF RM i i ∑ XFi RM i =1 × 0.5 n −1 = 1 − CM RM XFδ − XFδ × 0.5 + RM XF δ
PF1 (δ=0,2)
FBSR,MFBSR and GFBSR Computation
RM: 2DF-1PF CM8 : 1DF-1PF 1−
1− 2 1−1 + 2 1 = 0,75 2
Correspondent Feature
RM nCM DF − nDF
•
The feature tolerance ratio represents 50% of the FBSR. This choice is due to that tolerance is considered the most important feature parameter, ie. the cost of feature machining highly depends on assigned tolerance value. Therefore, two features are similar when their tolerances are as nigh as possible and the cost will be proportional to the volumes. The maximum of FBSR of each feature type, noted by MFBSR, is determined. Then, the Global FBSR (GFBSR), for each CM, is the average of all MFBSR. Therefore, the MSCM to RM has the largest GFBSR. Correspondance between features: The interactions between features, of the same model are respected through MP requirement: the features to be executed in the same phase have a parallel MP. For exemple, in Fig. 7 shows that DF1a and DF2b are not totally corresponding because the MP of DF1a (MP( DF1a )) is perpondicular to MP of PF1a (MP( PF1a )). On the other hand, DF2b and PF1b have the same MP. Thus, the CAM parameters of DF1a are used but it requires one more phase during machining. In the same Figure, DF1a and DF2b are totally corresponding.
Topology
62 4
Selection of similar CM (CM 5 ) Features Correspondence Condition 1
Reuse and Updating Parameters
CAM parameters and Cost Association
Condition 2 CAM parameters and Cost Update
Cost RM Computation
Fig.6. Result of the application of proposed algorithm
(6)
Where n is the number of feature parameters, XFiCM is the ith CM parameter, XFi RM is the ith RM parameter, XF is the feature type as well as XFδCM and XFδRM are the tolerance δ of XF.
The correspondence of MSCM with RM features is based on FBSR ratio: The ith RM Feature corresponds to jth MSCM feature which admits maximal FBSR ratio. If one of RM feature does not correspondent (FBSR=0) to any MSCM feature, a return to CMDBFP% is necessary. The highest ratio in the others CAD model of CMDBFP% is identified. If no correspondance is found, the CMDB, for the same model type, is used to look for the first model code that contains feature with the same topology. The Global Similarity Ratio of RM, which is the average of all
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2017) 000–000
FBSR MSCM features, is deduced. The CAM and cost data of the features identified by correspondence are recovered. a. RM
b. CM5
MP(PF1)
DF2
PF1 MP(PF1)=MP(DF1) =MP(DF2)
DF2
DF1 PF1
DF1 MP(DF1)
Fig.7. Machining plan correspondence
• Updating parameters and manufacturing cost estimation: Following correspondence, the calculation of the tool parameters is requested if the materials of the two models are different. The CAM and Cost data are updated and reused according to the following two conditions in order that the approximate cost of RM can be calculated: Condition 1: If the topological and geometrical parameters as well as dimensional tolerance value are identical, then a direct association of CAM parameters and cost of MSCM features to RM features is established. Condition 2: If only the topological parameters are identical, the CAM parameters are updated according to the geometrical parameters and tolerance value of RM features. 5. Case study In this section, a case study is presented to test the proposed algorithm. 12 CAD models are chosen to form CMDB as shown in Fig. 6. The sub-algorithm of CMDB organization allows the codification of the 12 models. The RM features data are extracted, classified and codified. The RM includes two drilling and one pocket (2DF-1PF). Thereafter, a comparison based on codes is established. The CBSR is allocated to each CM of the CMDB. As a result, a refined database is filtered from CMDBP with a filtration percentage chosen equal to 30%. In the case study, FP% value has a reduced impact on the total run time of the algorithm, unlike the big data case. FP% is used to show the algorithm operation and to simplify the illustration of the subsequent results. Table. 1. Drilling Features parameters.
Diameter (mm)
Length (mm)
Tolerance (mm)
RM-DF1
6,6
10
0,3
RM-DF2
11
20
0,5
CM3-DF1
11
25
0,3
CM3-DF2
11
25
0,1
CM5-DF1
13,5
28
0,4
CM5-DF2
9
28
0,2
CM9-DF1
15,5
30
0,4
CM9-DF2
15,5
30
0,5
CM11-DF1
11
51,718
0,3
CM11-DF2
11
51,718
0,4
Based on geometric parameters of CAD data of drilling features (Tab.1) and pocket features (Tab. 2), the FBSR is
63 5
computed (Tab. 3). Then, the MFBSR is determined. Each MFBSR represents the maximum value of FBSR for each RM features (Fig. 6). After that, GFBSR is computed. Subsequently, the CM5, with GFBSR equal to 0,26, is selected as the MSCM. A correspondence between RM and CM5 features is established according the fourth sub-algorithm (correspondence between features) (Fig. 7). Consequently, the Global Similarity Ratio of RM is deduced that is equal to 0,34. According to the machining plans of the three features, the machining order is: (1) PF1 and (2) DF2 are executed in the first phase, and (3) DF1 is executed in the second one. Table. 2. Pocket Features parameters.
RM-PF1 CM3-PF1 CM5-PF1 CM9-PF1 CM11-PF1
Depth (mm) 10 12 13 10 4
Area (mm²) 2291,82 4029,51 2482,12 3209,34 1312,44
Edges Length (mm) 206,248 262,91 212,26 225,54 139,27
Edges Nb. 4 12 4 8 8
Tolerance (mm) 0,2 0,4 0,5 0,2 0,2
Table. 3 . Results of FBSR computation
RM-DF1 RM-DF2
RM-PF1
CM3-DF1
0,598
0,565
CM3-PF1
-1,116
CM3-DF2
0,265
0,365
CM5-PF1
0,044
CM5-DF1
0,174
0,463
CM9-PF1
0,253
CM5-DF2
0,515
0,286
CM11-PF1
-0,176
CM9-DF1
-0,023
0,313
CM9-DF2
-0,189
0,413
CM11-DF1 -2,069
-2,887
CM11-DF2 -2,236
-2,787
Table 4. Computation RM theoretical machining cost
C0'
C1
RM-DF1 2101857,12 1,7329 RM-DF2 2101857,12 1,7329 RM-PF1 1324643,01 1,9711 Manufacturing cost Stock cost Adjustment cost Model cost
Tolerance (mm) 0,2 0,2 0,3
Volume (mm3) 3,42E-07 1,90E-06 2,87E-05
Feature Cost (DT) 0,508 2,823 21,067 24,399 5,616 3 33,015
Using condition 2 described previously, the CAM and Cost data parameters are updated (Fig. 6). The machining cost estimation is calculated. At this step, the two proposed methods for the approximate cost calculation are used. The first method is devoted to determine the cost of DFs and PFs using Eq. 1 whither constants C0 and C1 are identified (Tab. 4). In this case, the second method is applied using "ZIGZAG" and "Peck" strategies for PFs and DFs respectively. The toolpath time for the four operations (pocket, contouring, centering and drilling), and the feature approximate cost are shown in Tab. 5 and Tab. 6. Finally, the manufacturing cost of RM features is deducted by summing all features costs. The product cost is the adding of stock cost (which is computed by multiplying the unit price with CAD model volume) and total adjustment cost of all phases (This cost is unitary for each machining phase where adjustment is needed only once for each "Machining plan") to the features
Mehdi Tlija et al. / Procedia CIRP 70 (2018) 59–65 M.B. Letaief et al. / Procedia CIRP 00 (2018) 000–000
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manufacturing cost. The cost difference is 9.6%. This is a small difference. In this case study, there is not one method better than the others. Table 5 . Time toolpath computation
Parameter
CM-PF1
RM-PF1
Pr
8
8
Pd
1
1
DT
16
16
W
32
30
L
73
56
d
13
10
3172
1740
21min8s
13min39s
Toolpath PO ( TpPO
)
Machining Time of PO Toolpath CO ( TpCO
)
Machining Time of CO Toolpath CDO ( TpCDO
)
Machining Time of CDO Toolpath DO ( TpDO
)
Machining Time of DO
146
1080
9s
51s
12
12
3s
3s
110
316
32s
1min31s
Table 6. The RM estimate cost using second method Unit price (DT/h)
80,000
RM manufacturing time
16min04s
Cost PF1 (DT)
24,090
Cost DF1 (DT)
1,911
Cost DF2 (DT)
1,911
relatively high (about 11 min) even for a small FP% (20%). The identification and the comparaison steps are based on the geometric and topological parameters in manufacturing semantic. The operation parameter MP is used as an interaction between features during the correspondence step. Among the above disadvantages of this approach, the reinforcement of RM features links is required [3,18]. Also, the position tolerance and process plan must be considered in the proposed algorithm because those constrains affects the cost calculation. These limitations do not diminish the importance of the proposed tool, but they must be taken into account to consolidate its reliability. 7. Conclusion and perspectives In this paper, a new comparison approach between a studied CAD model and a manufactured CAD models database (CMDB), is presented using UFT. As a result, CAM as well as cost data are retrieved, updated and reused to estimate the RM estimated cost. The RM is prepared to be machined with time benefit. In future works, the proposed approach can be generalized for other feature types to be exploited in the future industry as additive manufacturing. The developped tool can be applied to CAD models in "STEP" format on using the Product and Manufacturing Information (PMI) representation [19] or "STEP-NC" format [20,21]. The proposed UFT is flexible and can support other constraints : positional tolerance, process plan and other manufacturing environment. References
5.1.1. RM Features manufacturing cost (DT)
27,912
5.1.2. Adjustment cost (DT)
3,000
[1] S. Tao, Z. Huang, B. Zuo, Y. Peng, W. Kang, Partial retrieval of CAD models based on the gradient flows in Lie group, Pattern Recognit. 45
5.1.3. Stock Cost (DT)
5,616
5.1.4. RM Cost (DT)
36,528
6. Discussion The classical approach of manufacturing product comprises three steps: CAD, CAM and machining execution in the workshop. The resolution of problems, encountered during machining models, forms the expertise of the company. This experience and know-how must be highlighted during studing a new product. From this fact, an improvement of manufacturing approach, benefiting from the company's knowlege, is proposed. As a result, the machining parameters are defined and manufacturing cost is estmated since the design phase. The empirical method, allowing the estimation of the cost of RM, is adapted to features that have different tolerances. On the other hand, the method based on the calculation of the toolpath is suitable for features that have different geometries. The run time of the case study is about 34 seconds. The proposed method is tested, also, in the case of a big CMDB (500 complex models (an average of 15 features per model). The run time decreases according to FP%, but remains
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