A System for Generating Process and Material Selection Advice During Embodiment Design of Mechanical Components

Transcription

1 A System fr Generating Prcess and Material Selectin Advice During Embdiment Design f Mechanical Cmpnents Satyandra K. Gupta and Yusheng Chen Mechanical Engineering Department and Institute fr Systems Research University f Maryland, Cllege Park, MD Shaw Feng and Ram Sriram Natinal Institute f Standards and Technlgy Gaithersburg, MD ABSTRACT This paper describes a systematic apprach t material and prcess selectin during the embdiment design f mechanical cmpnents and a system fr generating prcess and material selectin advice. Quite ften during the embdiment design stage, design requirements are nt precisely defined. Therefre, the system described in this paper accunts fr imprecisin in design requirements during generatin and evaluatin f alternative prcess sequences and material ptins. T reduce the cmputatinal effrt, the system uses a depth-first branch-andbund search algrithm. This aids in explring prmising prcess sequences and material ptins that can be used t meet the given set f design requirements. Varius prcess sequences and material ptins are evaluated by using a cmmercial cst estimatin tl. 1 INTRODUCTION Design f a prduct requires the satisfactin f a set f functinal requirements. In additin, there are sets f manufacturing prcess-dependent cnstraints that must als be satisfied in rder t prduce the individual cmpnents cmprising the prduct. Designers, therefre, must satisfy bth types f cnstraints. In mst designs, prcess cnstraints play a significant rle in determining the detailed features f the final frm f the cmpnents. Mrever, there is usually mre than ne manufacturing prcess that can be used t manufacture these cmpnents. Therefre, the satisfactin f a given set f functinal cnstraints can be realized by cmpnents that can appear in many different frms, depending n the prcess cnstraints. Usually cst cnsideratins play a majr rle in the final selectin f the prcess and material cmbinatin. Traditinally, designers select prcess and materials using either their wn previus experience, r the experience f the manufacturing engineer. Mst designers are familiar with very limited number f manufacturing prcesses. Therefre, if designers rely n their wn knwledge, they might nt cnsider unfamiliar manufacturing prcesses that may turn ut t be an attractive alternative t the prcesses knwn t designers. This is increasingly becming a prblem in tday s era f rapid changes in manufacturing technlgies. This makes it difficult fr a designer t be familiar with all the manufacturing prcesses. Many designers apprach manufacturing prcess prviders and ask them fr advice n the prcess and material cmbinatin. If the design task is nt very cmplex, manufacturing engineers can use their knwledge f the manufacturing prcesses and materials t suggest a pssible cmbinatin that wuld prduce the design. As this step is dne manually, it is very likely that manufacturing engineers may nt be cnsidering all 1

2 the available prcesses and materials. Mrever, they may nt even be aware f the existence f certain prcesses and materials. Hence, even if their advice help the designer, it may nt be the best advice. There exist a large number f manufacturing prcesses and materials that are being used widely all ver the wrld. Each f these prcesses and materials has its wn capabilities and characteristics. As these cmbinatins are numerus, the designer may have t spend enrmus amunt f time trying t find ut the right prcess and material cmbinatin. Hence, in a realistic scenari, it is nt pssible fr the designer t be able t get the best prcess and material cmbinatin by evaluating all pssible cmbinatins manually. It wuld be helpful t have a prcess and material selectin sftware where all the available prcesses are registered. The designer can use this sftware t get advice n the pssible prcesses and materials. Design activity can be divided int three main stages: cnceptual design, embdiment design, and detailed deign [Pahl and Beitz 1996]. Usually sufficient infrmatin exists at embdiment design level fr starting prcess and material selectin. The fllwing aspects need t be addressed during material and prcess selectin. Cupling between material, cmpnent size, and prcesses: Whether r nt a prcess can be selected depends n bth the material type and the cmpnent size. In many cases, cmpnent size cannt be defined withut knwing the material type and the prcess cannt be selected withut knwing the size. Therefre, it is difficult t treat this prblem as a single database lk-up prblem. Cst Interactins amng prcesses: Often a cmpnent may require multiple prcesses t achieve the required frm and finish. It is difficult t select prcesses fr varius features in the bjects withut accunting fr interactin f csts amng different prcesses. Fr example cnsider six different designs shwn in Figure 1. Table 1 lists the prcesses that shuld be assigned t different hles based n the manufacturing cst. Even thugh hle A has the same tlerances in Designs 1 and 3, it has been assigned different prcesses. Based n the tlerance alne, casting prcess is adequate t make this hle in bth designs. Hwever, casting requires use f cres t make hles and therefre increases the cst f casting prcess. Cmbined setup cst and prcessing cst fr drilling a hle is higher then the use f a cre in the casting prcess. Therefre, in Design 1 casting is preferred fr creating hle A. On the ther hand in Design 3, there exists anther hle labeled as hle B that requires drilling t meet its stricter tlerance requirements. Therefre a drilling setup cst will be incurred in drilling hle B irrespective f the prcess assigned t hle A. Based n nly the prcessing cst, drilling is cheaper than casting (i.e., if the part is ging t be setup n a drilling machine t create hle B, then making hle A in the same setup csts less cmpared t the cst f using a cre in the casting prcess t make hle A). Therefre in case f Design 3, drilling is the preferred methd f making hle A even thugh casting can create it. Table 1: Prcess Selectin Results fr Different Designs Shwn in Figure 1 2

3 Design Index Prcess fr hle A Prcess fr hle B Prcess fr hle C Design 1 Sand Casting NA NA Design 2 NA Drilling NA Design 3 Drilling Drilling NA Design 4 NA NA Sand Casting and Bring Design 5 Sand Casting NA Sand Casting and Bring Design 6 NA Sand Casting and Bring Sand Casting and Bring Cst estimatin in presence f imprecisin: In embdiment design, there is uncertainty in design parameters. These parameters can be represented as ranges, thereby resulting in ranges n prcess parameters. Cmmercial cst estimatin systems d nt allw imprecisin in prcess parameters. Therefre, the cst estimatin step needs t accunt fr such imprecisin. A wide variety f cmputatinal methds have emerged t prvide sftware-aids fr perfrming manufacturability analysis. Such methds vary significantly by apprach, scpe, and level f sphisticatin. A detailed survey f these systems can be fund in a survey paper by Gupta et al [Gupta et al. 1997]. This paper describes a systematic apprach t select prcess and material cmbinatins during the embdiment design stage. Based n the apprach and the algrithms described in this paper, we have develped a prcess and material selectin system called WisePrM (Wizard fr Selectin f Prcesses and Materials) that can be used by designers during the embdiment design stage. Given design requirements in terms f business, material, and frm requirements, this system helps designers in selecting the prper cmbinatin f materials and prcesses t meet design requirements. Varius prmising prcess and material ptins are evaluated using a cmmercial cst estimatin system. Our system recmmends the least expensive prcess and material cmbinatin t the designer. WisePrM allws designers t cnsider a wide variety f prcess and material ptins during the embdiment design stage. This allws them t find the mst cst-effective cmbinatin. By selecting the prcess and material cmbinatin during the design embdiment stage, designers can ensure that the detailed design is cmpatible with all f the prcess cnstraints fr the selected materials and prcesses. 2 BACKGROUND AND OVERVIEW 2.1 Terminlgy This sectin describes the terminlgy needed t describe the apprach presented in subsequent sectins. Cst Interval: Due t imprecisin in the design requirements, it is nt pssible t assign exact parameters t prcesses (e.g., diameter f hle) and material (e.g., weight f material) in a material and prcess sequence. Therefre it is nt pssible t exactly cmpute cst 3

4 assciated with a sequence. Instead cst interval is used t describe the minimum and maximum cst assciated with a sequence due t imprecisin in design parameters. Ttal Prductin Cst: Ttal prductin cst C fr a sequence s is defined as: n n n Cs () = C + C + C + C M Pi Ti Si i= 1 i= 1 i= 1 where C M is material cst, C Pi is the prcessing cst assciated with the i th prcess in the sequence (it includes bth labr and capital cst), C Ti is tling cst with the i th prcess in the sequence, and C Si is setup cst with the i th prcess in the sequence. If there is any imprecisin in design requirements, then the cst is defined by a cst interval. Dminated Sequences: Let s be a sequence. We dente the minimum cst assciated with s by C min (s) and the maximum cst assciated with s by C max (s). Please nte that C max (s) C min (s). A sequence s dminates anther sequence s, if and nly if: C max (s ) C min (s). This cnditin implies that despite imprecisin, the wrst pssible utcme fr sequence s due t imprecisin is still better than r cmparable t the best pssible utcme fr sequence s. Therefre, we can always prefer s ver s and hence s is dminated by s. Material Requirements (R M ): These requirements are stated in terms f required ranges f material prperty parameters. Business Requirements (R B ): These requirements are stated in terms f ranges n required tling lead-time, prductin rate, and verall prductin quantity. Here the term tling lead-time means the time needed t create and/r prcure tling t start prductin, and the term prductin rate means the units prduced per week. Frm Requirements (R F ): We use envelpe size as the high-level frm requirement. Detailed frm requirements are expressed using ne grss shape and zer r mre shape mdificatin features. Fr grss shapes we have tw types f parameters. The first type f parameters is cmmn t all grss shape types. The secnd type f parameters is specific t each grss shape type. The cmmn grss shape parameters are: (1) grss shape name, (2) grss shape type, (3) tlerance, (4) surface rughness, (5) number f plane faces, and (6) number f curve faces. Details f specific parameters depend n grss shape types, but they are all dimensinal parameters. Varius grss shape types cnsidered in ur current system are shwn in Figure 2. Our grss shape types are similar t the nes described in [Ashby and Easwi 1999]. Fr shape mdificatin features we als have tw types f parameters: cmmn and specific. The cmmn shape mdificatin features are: (1) number f features, (2) feature name, (3) feature type, (4) tlerance, (5) surface rughness, (6) number f plane faces, (7) number f curve faces, and (8) manufacturing directin. Details f specific parameters depend n shape mdificatin feature types. Varius shape mdificatin feature types cnsidered in ur current system are shwn in Figure Overview f Prcess and Material Selectin Apprach Our apprach t prcess and material selectin cnsists f the fllwing steps: 4

5 Step 1. Generating Cmbinatins f Materials and Primary Prcesses. In this step, based n the material requirements, business requirements, and high-level frm requirements, we generate cmbinatins f primary prcesses and materials that can meet these requirements. Sectin 3 describes ur algrithm fr this step in detail. Step 2: Finding Prmising Prcess Sequences by Adding Secndary and Tertiary Prcesses int Varius Cmbinatins f Materials and Primary Prcesses. Fr each primary prcess and material cmbinatin that meets the first level f design requirements, we d the fllwing: Step 2a: Adding Detailed Frm Requirements. Users are asked t add detailed frm requirements in terms f grss shape and shape mdificatin features t the cmbinatin. Such frm requirements need t be cnsistent with the cnstraints impsed by the primary prcess n the grss shape. Step 2b: Cnstructing Prcess Sequences fr Varius Shape Mdificatin Features and Grss Shape. Once the detailed frm requirements have been defined fr a cmbinatin, we start cnstructing alternative prcess sequences fr varius shape mdificatin features. Sectin 4 describes ur algrithm fr this step in detail. Step 2c: Finding Nn-Dminated Sequences. Based n the cst cnsideratins we first prune unprmising prcess sequences fr shape mdificatin features. Sectin 5 describes hw the cst is estimated fr a given sequence. Then, we perfrm a depth-first branchand-bund search t find all nn-dminated sequences frm the remaining alternatives. Sectin 6 describes the search algrithm in detail. Step 3: Selecting a Material and a Prcess Sequence Cmbinatin. At this step, the designer can analyze varius nn-dminated sequences and a detailed cmparisn n the cst can be perfrmed. Dminance amng different sequences can be determined at even narrwer levels and a final decisin can be made by the designer. Sectin 7 describes this step in detail. 2.3 Prcess and Material Infrmatin Mdels In rder t perfrm prcess and material selectin using a sftware system, we need t mdel the required infrmatin abut prcesses and material. Our prcess and material infrmatin mdels are similar t the nes described in [Feng and Sng 2000a, Feng and Sng 2000b]. Bth materials and prcesses can be classified hierarchically. Usually decisins that deal with the selectin f specific instances f prcess are made during the prcess planning stage. On the ther hand, decisins that deal with the selectin f specific instances f materials are made during the detailed design stage. Therefre, in ur infrmatin mdels, we d nt stre infrmatin abut material and prcess instances. Rather, we stre infrmatin abut material and prcess classes. 5

6 Our current material infrmatin mdel cnsists f the fllwing mechanical, thermal, and envirnmental prperties: (1) material type, (2) material subtype, (3) material name, (4) density, (5) elastic limit, (6) fracture tughness, (7) Yung's mdulus, (8) hardness, (9) ductility, (10) Pissn's rati, (11) melting pint, (12) specific heat, (13) resistivity, and (14) slvent resistance. Our material infrmatin mdel has been implemented using a relatinal database. Currently it cntains 52 material classes. Many mechanical cmpnents are prduced using a sequence f prcesses, invlving mre than ne prcess. Therefre the prcesses used t manufacture a mechanical prduct can als be classified int fur types. Primary prcesses are net-shape prcesses such as casting, frging, and injectin mlding. These prcesses are used t prduce the grss shape f a part. Secndary prcesses are feature creatin prcesses, such as machining and electr-discharge machining. Tertiary prcesses are feature-finishing peratins (i.e., prcesses that d nt affect grss part and grss feature gemetry) such as grinding, reaming, and lapping. Finally, surface treatment prcesses d nt alter the gemetry but change the material prperties. Examples f surface treatment prcesses include quenching, annealing, and tempering. Our current wrk des nt include surface treatment prcesses. The shape f a part has a majr bearing n the prcess selected t prduce it. Flat parts with thin crss sectins cannt be cast prperly. Very cmplex parts cannt be manufactured by machining ecnmically in large vlumes, whereas they may be easily cast r mlded. S different manufacturing prcesses vary in their limitatins n the shapes prduced. As described abve, a part can be cmpsed f ne grss shape and zer r mre shape mdificatin features. Fr each prcess we mdel its shape prducing capabilities. Cmpnent dimensins can be imprtant cnsideratins in selecting a prcess. Generally, the maximum size that can be prduced by any ne given prcess is ften limited simply by the size f available equipment. In sme prcesses, there are limitatins due t prcess cnditins themselves. Mre frequently, prcessing techniques are limited in their capacity t prduce small size, especially minimum wall thickness. The wall thickness f a casting may be limited by the fluidity f metal. Therefre, very thin, very small, r very large cmpnents usually can be made nly under special circumstances and at an extra cst. Tlerance and surface rughness requirements als determine which prcess can be used. Therefre, we mdel tlerance and surface rughness capabilities f varius prcesses in ur system. Prductin quantity is anther significant factr that plays an imprtant rle in prcess selectin. Fr example, if we chse a prcess that requires a reusable custm tl such as ht chamber die- casting, it will nly be suitable fr a large quantity prductin run. Sme prcesses have precnditin requirements. Fr example, cnsider braching. If it is used t manufacture an irregular hle, it requires an already existing pening in the part. Our prcess infrmatin mdel cnsists f the fllwing: (1) prcess type, (2) prcess name, (3) prductin quantity, (4) prductin rate, (5) tl lead-time, (6) grss shape types supprted, (7) shape mdificatin features types supprted, (8) manufacturing directin, (9) tlerance, (10) surface rughness, (11) prcess precnditin requirement type, (12) precnditin requirement, 6

7 (13) precnditin prcesses, (14) dimensins which are different fr different prcesses, generally it includes length, width, height, and thickness. Our prcess infrmatin mdel has been implemented using a relatinal database. Currently it cntains 31 prcesses. Material prperties directly influence the prductin methds. The selectin f material must be clsely cupled with the selectin f a manufacturing prcess. Fr example, plastics can be mlded, but cannt be frged; steels can be cast r frged, but cannt be vacuum frmed. As an example, cnsider mlybdenum titanium ally. It is a refractry material with high hardness. If we want t manufacture a small hle, cavity-type electr discharge machining is the prcess t be used instead f drilling. Cmpatibility amng prcesses and materials is mdeled using a material prcess cmpatibility mdel. The material and prcess cmpatibility mdel cnsists f the fllwing fields: prcess name, material name, and cmpatibility status (Yes r N). Figure 4 shws relatinship between prcess and material infrmatin mdels graphically. 3 GENERATING COMBINATIONS OF MATERIAL AND PRIMARY PROCESSES This sectin describes the algrithm used t generate a set f cmbinatins f material and primary prcesses C mp that can satisfy business requirements, material requirements, and highlevel frm requirements. Varius steps in ur algrithm are described belw: Step 1: Search thrugh material database t find materials that can meet material requirements and stre them in set M (mre details n this step are given later in this sectin). Step 2: If set M is nt empty, then fr each material m in set M, search thrugh the prcess database t find a set f prcesses P p that are: (1) primary prcesses, (2) cmpatible with material m, and (3) meet all business and high level frm requirements (mre details n this step are given later in this sectin). Fr every p in P p, add the material and primary prcess cmbinatin (p, m) int set C mp. Step 3: Shw all cmbinatins in set C mp t the designer. Designer can select ne r mre number f cmbinatins that are ging t be used during the secnd level selectin. As described abve, we first chse materials t meet all Material Requirements (R M ). Requirements fr each material attribute are expressed in terms f a range. The material infrmatin mdel als stres ranges assciated with every material. Tw types f searches are pssible fr every attribute: 1. Cntained: This type f search implies that the cmplete range f requirement n the material attribute can be met by the entry in the database. 2. Intersectin: This type f search implies that at least ne pint in the specified range f requirement n the material attribute can be met by the entry in the database. Our algrithm fr finding all suitable materials (M) is described belw: 7

8 1. Initialize set M as an empty set. 2. Chse a material m frm the material database that has nt been cnsidered s far. If n such material exists, then return M. 3. Cmpare value f each material attribute with the crrespnding attribute in the material requirements (if designer didn't input value fr this attribute, it means that this attribute isn't under cnsideratin, hence g t the next attribute). If any attribute cannt meet the requirement, then g t Step 2. If it can meet the requirement f all attributes, then add material m int material set M and g t Step 2. Our algrithm fr finding all suitable primary prcesses P p fr material m is described belw: 1. Initialize set P p as an empty set. 2. Chse a prcess p frm prcess database that has nt been cnsidered befre. If n such prcess exists, then return P p. 3. Check whether p is cmpatible with material m. If nt, g back t Step Cmpare value f each business attribute and high level frm attribute with the crrespnding attribute in the business and high level frm requirement (if designer didn't input value fr this attribute, it means that this attribute isn't under cnsideratin, hence g t the next attribute). If any attribute cannt meet the requirement, then g t Step 2. If it can meet the requirement f all attributes, then add it t Set P p and g t Step 2. 4 CONSTRUCTING SETS OF ALTERNATIVE PROCESS SEQUENCES FOR SHAPE MODIFICATION FEATURES AND GROSS SHAPE This sectin describes the algrithm used t generate a set f alternative prcess sequences fr each shape mdificatin feature and grss shape s unfinished accuracy requirements. Fr each cmbinatin f material and primary prcess, designers need t define ne grss shape and zer r mre shape mdificatin features t prvide detailed frm requirements. Befre we cnstruct alternative prcess sequences, we need t make sure that the primary prcess is cnsistent with the grss shape. The algrithm fr ding this is given belw: Step 1: Identify what design requirements can be reached by primary prcess p p using the fllwing steps: Check whether p p can manufacture this type f grss shape and can meet the dimensin requirements. If p p cannt manufacture this type f grss shape, then shw crrespnding errr infrmatin t the designer. Designer can either change design requirements r delete this cmbinatin frm the cmbinatin list. While checking whether prcess p p can meet the dimensin requirement, search type cntained is used. 8

9 Check whether p p can als reach grss shape s accuracy requirements. While checking whether prcess p p can meet the accuracy requirement, search type intersectin is used. Step 2: If bth dimensin and precisin requirements are reached, then update shape mdificatin feature s current accuracy level and set it equal t grss shape s accuracy level. If nly dimensin requirements are reached, then update grss shape s current accuracy t primary prcess s capability. The available prcess ptins fr shape mdificatin features and grss shape s unfinished accuracy requirements can be mdeled as a prcess ptin frest (i.e., a set f trees), where, each prcess ptin tree T(N, E) in the frest describes either prcess sequence ptins fr a shape mdificatin feature, r prcess sequence ptins fr the grss shape s unfinished accuracy requirements. Varius edges E represent varius prcesses and varius ndes N represent the list f unfinished frm requirements with current accuracy infrmatin. We cnstruct each prcess ptin tree using a frward chaining scheme. First we generate a prcess ptin tree fr the unfinished accuracy requirements fr the grss shape. If accuracy requirements are nt met, then varius alternative prcesses fr meeting the unfinished requirements are added t the prcess ptin tree. If all grss shape s frm requirements can be reached, then every shape mdificatin feature s current accuracy level is als updated and set equal t the accuracy level f the grss shape. Fr shape mdificatin features, each rt nde cntains the shape mdificatin feature s frm requirements. Varius prcesses that can meet these requirements are added int the ptin tree. Secndary prcesses are used t manufacture shape mdificatin feature. Secndary prcess selectin begins with the retrieval f all secndary prcesses generally assciated with the design. Then apprpriate prcesses are selected fr the shape mdificatin feature and are assessed fr glbal feasibility and their cmpatibility with the primary prcess. Smetimes the primary prcess used t manufacture the grss shape can als be used t manufacture shape mdificatin features. Tertiary prcesses are used t satisfy surface finish requirements fr shape mdificatin features that require mre accuracy than that can be prvided by the feasible primary and feasible secndary prcess. Fr every secndary prcess with unfinished tlerance/surface requirement, tertiary prcess ndes are created and apprpriate tertiary prcesses are selected fr the remaining tlerance/surface finish requirements. Leaf ndes cntain n requirements. Hence they crrespnd t the finished feature. Figure 5 shws a prtin f a prcess ptin tree fr a shape mdificatin feature. The detailed algrithm t cnstruct the prcess ptin tree fr a shape mdificatin feature is described belw: Step 1: Call recursive algrithm TREE(R, n) t cnstruct the prcess ptin tree by setting R t be the frm requirement fr the feature and n t be the rt nde in the frest (i.e., a set f trees) crrespnding t this shape mdificatin feature. 9

10 Step 2: Cnvert the tree int set f sequences S using tree traversal methd [Crmen et al. 1990]. The number f sequences is equal t the number f leaf ndes in the tree. Let R be a set f frm requirements fr a feature. Algrithm TREE(R, n) is described belw: Algrithm TREE(R, n) If R is empty, then return. Otherwise, d the fllwing: Find the set f prcesses P that can satisfy ne r mre requirements in R (Detailed steps are described later). If P is empty then return. Otherwise fr every p in P, d the fllwing: Add a nde n crrespnding t the requirements left after using p n n. Stre remaining unfinished requirements in R. Call TREE(R, n ). The algrithm fr finding the set f prcesses that can satisfy ne r mre requirements in R is described belw: Step 1: Initialize P as an empty set. Step 2: Select a prcess p frm the database that has nt been cnsidered befre. If n such prcess exists then return P. Check if p is cmpatible with the material m assciated with nde n, if it is nt cmpatible, then g t Step 2. Check the fllwing tw cnditins fr p: If there is a dimensin requirement that has nt been reached at n, then check if p is a secndary prcess r the primary prcess assciated with the cmbinatin. If there is accuracy requirement left, then check if p is a tertiary prcess. If the abve cnditins are nt satisfied then g t Step 2. Otherwise, d the fllwing: Test the fllwing tw cnditins: 10

11 Check whether p can be used t manufacture shape mdificatin feature s feature type. Check the shape mdificatin feature s parameters are cmpatible with crrespnding values in the prcess database fr p. If the abve tw cnditins are met then add p t P. Otherwise g t Step 2. 5 ESTIMATING COST FOR PROCESS SEQUENCES We use SEER-DFM cst estimatin system fr estimating cst interval (C min (s), C max (s)) fr a given sequence s. SEER-DFM is a cmmercial system t analyze csts assciated with manufacturing, as well as ther life-cycle factrs [Galrath 1999]. SEER-DFM system requires exact specificatins f the prcess parameters fr estimating cst. Due t imprecisin in design parameters, we als have imprecisin in the prcess parameters. Therefre, rather then estimating a single cst number, ur apprach is t btain a cst interval. Hwever, we cannt directly call SEER-DFM t estimate a cst interval. We usually need t make multiple calls t the cst estimatin system, each call with cmpletely specified set f parameters by selecting specific value f parameters in the parameter range. Each call results in a single cst estimate. By apprpriately sampling the entire parameter ranges, and making calls t cst estimatin system, we estimate the cst interval. Since each call t the cst estimatin system is cmputatinally expensive, we wuld like t estimate the cst interval with the minimum number f calls t the cst estimatin system. Sectin 5.1 describes ur apprach fr this prblem. Calling SEER-DFM and getting the cst estimatin results is a time cnsuming step. We need t estimate cst f many different prcesses. Therefre whenever pssible we try t cache the previusly estimated cst. Sectin 5.2 describes ur apprach. 5.1 Cmputing Cst in Presence f Imprecisin in Deign Parameters T estimate the maximum cst and the minimum cst, we need t first identify the values f prcess parameters that will lead t the maximum and minimum cst values. In rder t develp a systematic apprach fr making calls t the cst estimatin system, we need t first understand what kind f effect varius prcess parameters will have n the final cst. By perfrming a systematic study f the SEER-DFM system, we have identified the nature f the relatinship between varius prcess parameters and the final cst. The relatinship between each prcess parameter and the final cst in SEER-DFM is mntnic. Relatinships between prcess parameters and final cst can be classified int the fllwing tw types: the final cst f using this prcess decreases as the value f prcess parameter increases ( ). the final cst f using this prcess increases as the value f prcess parameter increases ( ). We make tw calls t SEER-DFM system t estimate cst interval when prcess parameters are independent. Let X be the set f prcess parameters. Fr each parameter x X, the fllwing apprach is used t decide which value f the parameter will be used in estimating the cst. 11

12 If the relatinship between parameter x and final cst is : Minimum value f x is used t estimate the maximum cst. Maximum value f x is used t estimate the minimum cst. If the relatinship between parameter x and final cst is : Minimum value f x is used t estimate the minimum cst. Maximum value f x is used t estimate the maximum cst. We make tw calls t the cst estimatin system. The first call is made fr estimating the maximum cst and the secnd call is made fr estimating the minimum cst. When prcess parameters are dependent we need anther methd t determine the parameters values t estimate the maximum and the minimum cst. Let C be the prcessing and material cst fr a given prcess. Let us assume that x 1 and x 2 are tw prcess parameters that are mutually dependent. Nw C can be mdeled as: C = a1x1+ a2x2+ a0 Nw, due t dependency between x 1 and x 2, x2 = bx 2 1+ d2 min max The value range f x 1 is: x x x We need t get values f x 1 and x 2 fr cmputing the maximum and minimum cst. Our algrithm t cmpute this is as fllwing: 1. Replace x 2 in the cst equatin with x 1 and we get: C = ( a + a b ) x + a d + a If a1+ a2b2 > 0, max max x, bx + d is the value set t get maximum cst. ( ) min min ( x1, bx 2 1 d2) + is the value set t get minimum cst. If a1+ a2b2 < 0, min min x, bx + d is the value set t get maximum cst. ( ) max max ( x1, bx 2 1 d2) + is the value set t get minimum cst. S far we have nly discvered a linear dependency between at mst tw parameters. Hwever, ur scheme can be easily extended t cst estimatin situatins where dependency may exist 12

13 between multiple parameters and such dependency may be nn-linear in nature. T discver dependency cnstraints, a design f experiments apprach can be used, by treating the cst estimatin system as a black bx. Varius cmbinatins f input parameter values can be used t generate the input set fr the cst estimatin system and its utput can be analyzed t determine if there are dependencies in the input parameters. Once a suitable expressin f dependency is identified, an apprach similar t the ne utlined abve can be used t find the apprpriate setting f input parameters t cmpute the cst interval. 5.2 Caching Cst Estimatin Results frm Previus Sequences Whenever a new edge is inserted int the prcess ptin tree, we need t calculate the cst assciated with the prcess that results in this edge. Fr a given feature we may need t cnsider the same prcess many different times at different levels in the tree. Typically it takes 3 secnds fr SEER-DFM t estimate the cst fr ne nde, making it cmputatinally intensive. Therefre, we use the fllwing techniques t reduce the cst estimatin time: 1. If cst interval has already been cmputed fr a prcess and shape mdificatin feature cmbinatin, then cache the cst interval rather then recmputing it. We make use f a unit cst interval (i.e., cst interval fr ne shape mdificatin feature) during caching. T get the cst fr multiple features we simply multiply the unit cst by the number f features. 2. Divide the cst interval (C min (s), C max (s)) int tw different cst interval cmpnents, s s ns ns sharable ( Cmin ( s), Cmax ( s )) and nn-sharable ( Cmin ( s), Cmax ( s )). C () s = C () s + C () s s ns min min min s ns max = max + max C () s C () s C () s As described in Sectin 2.1, each prcess has three csts assciated with it: setup cst, tling cst and prcessing cst. Amng these csts the tling and the prcessing csts are nt sharable. Setup cst is sharable. If, in a sequence tw r mre prcesses use the same setup then setup cst is nly incurred nce. Hence, it is shared between these prcesses. But tling and prcessing csts are nt sharable. Breaking up the cst int sharable and nnsharable cmpnents increases the caching efficiency by creating mre caching pprtunities. Fr different prcesses, methds t calculate tling and prcessing csts are different. Fr prcesses belnging t machining, such as turning and milling, we can easily estimate the unit cst f making a feature using SEER-DFM. But fr prcesses belnging t casting/mlding, cmputing the unit cst fr making a feature is mre cmplex using SEER-DFM. Fr example, let us assume that we need t calculate the tling and prcessing cst assciated with the sand casting prcess t manufacture a cylindrical hle. T cmpute this we first need t estimate the tling and prcessing cst f prcess sand casting t manufacture the part with n hle. Then, we estimate the tling and prcessing cst f prcess sand casting t manufacture part with ne hle. The difference between them gives the tling and prcessing cst f prcess sand casting t manufacture the hle. 13

14 6 FINDING NON-DOMINATED SEQUENCES We use a depth-first branch-and-bund search algrithm t explre varius ptins and identify a set f nn-dminated slutins in a cmputatinally efficient manner. Details n varius search algrithms can be fund in [Sriram 1997]. In presence f imprecisin, we cannt use the classical branch and bund algrithm that just stres the current best slutin. Instead, we need t stre the set f nn-dminated slutins. A sequence is cnsidered as a part f the current set f nndminated slutins, if s far during the search n ther sequence has been fund that dminates any slutin in this set. Let S ND be the set f current nn-dminated slutins. During the search prcess, S ND will have the fllwing prperties: 1. Fr every sequence s in S ND, there is n ther sequence s in S ND such that C min (s ) is greater than C max (s). 2. Fr every sequence s in S ND, there is n sequence s in S ND such that C max (s ) is lwer than C min (s). The branch and bund algrithm prceeds in the fllwing manner. Let s be a new sequence (either partial r full) being cnsidered during the search. During the search prcess, s is handled in the fllwing manner: 1. If there is ne s in the set f the current nn-dminated slutins S ND that dminate s, then s is pruned. 2. If s is a cmplete sequence and there is at least ne s in the set f current nn-dminated sequences S ND such that C min (s ) is smaller than C max (s), then s is added t the set f current nn-dminated slutins. If s is added t the set f current nn-dminated slutins S ND, then we examine S ND t make sure that it still satisfies the tw cnditins described abve. If after inserting s int S ND any slutin in S ND is dminated by s, then we remve the dminated slutin frm set S ND. The depth-first branch-and-bund algrithm is initialized by setting the current nn-dminated set as an empty set. Then dminated sequences belnging t each feature are pruned. Then a recursive algrithm EXPAND, is called t actually cnstruct and evaluate the ptin space. Algrithm INITIALIZE that initializes the search is described belw: Algrithm INITIALIZE(F) Initialize the set f current nn-dminated slutin S ND t a null set. Initialize set f unfinished features F t include all shape mdificatin feature f i (i (1, K)), K is the number f shape mdificatin features. 14

15 Fr each f i in F, Let S fi be the set f sequences available t create f i. Fr each s in S fi d the fllwing: If s is dminated by a sequence s in S fi, then d the fllwing: If s des nt includes any setup sharable step, then prune s. If s includes the same setup sharable steps as s, then prune s. Initialize the current hybrid sequence s (unin f sequences fr individual shape mdificatin features being cnsidered) t be a null set. Call EXPAND (F, s) Details f algrithm EXPAND are described belw: Algrithm EXPAND(F, s) Evaluate bth sharable and nt-shareable cst intervals fr s. If s is empty, bth minimum and maximum values are set 0. If F is empty, then d the fllwing: If s is nt dminated by any sequence in current nn-dminated sequences S ND, then insert s int S ND Otherwise, return Otherwise, d the fllwing: ns Cmpute the lwer bund f cst C l (F) fr F. c ( ) min s is the minimum nn-sharable cst value fr sequence s f. C ns l ( F) = min ( cmin s f S ( f ) f F ( S f f )) If interval (C max (s) + C l (F), C min (s) + C l (F)) is dminated by current nn-dminated set S ND, then return. Otherwise, d the fllwing: Pick a feature f in F that has smallest number f sequences assciated with it. 15

16 Fr every sequence s f fr f, calculate the cst C s (s f ) assciated with s f by using n ns s equatin C ( s ) = Cmin ( p ) + δ Cmin ( p ) s f i i i i= 1 i= 1 n. δ i is equal t 1 if p i des nt share a setup with a previus step. Otherwise, δ i is equal t 0.Rank them in the increasing rder and select s f with minimum cst and d the fllwing: EXPAND (F-f, s s f ) 7 SELECTING MATERIAL AND A PROCESS SEQUENCE After a set f nn-dminated slutins has been fund, we prvide the fllwing detailed cst analysis utilities t help designers select a cmbinatin f a material and a prcess sequence. These utilities are described belw: Cmparisn f Cst Interval f Selected Sequences: Shw cmparisn f cst interval (C min (s), C max (s)) in selected sequences. This utility can be used by the designer t identify mre prmising sequence amng the set f nn-dminated sequences. Pair-Wise Cmparisn f Cst: After a set f nn-dminated slutin has been fund, designer can prceed with the pair-wise analysis f the slutins. At this stage we try t find ut if the structure f the cst equatin is such that ne slutin in the pair will dminate the ther slutin fr all values f the parameter in the given range. T simplify visualizatin, we have cmbined varius frms parameters int three main parameters that influence the final cst: shape cmplexity, dimensin and precisin. The quantity is used as the furth main parameter. Each parameter has five value levels. Designer can select ne attribute t study the lcal dminance relatinship, setting all ther three t specific value levels. We remve lcally dminated slutins frm the set f nn-dminated slutins. The remaining slutins can be further examined by the designer. If the designer tightens the bunds n sme parameter, then we reevaluate slutins and remve the slutins that are dminated. Cst Decmpsitin f a Sequence: This utility can be used t study varius cmpnents f cst fr a sequence. It includes material, setup, tling, and prcessing cst fr primary prcess; and setup, tling, and prcessing cst fr each secndary and tertiary prcess. Designer can use this utility t study a sequence in mre detail and see hw changes in an imprecise parameter influence varius cst cmpnents. Finally, the designer can assign a prbability distributin functin with each parameter range. At this stage, the designer can prceed with cmputing the expected value f prductin cst fr each nn-dminated sequence, and finally select the sequence that has the lwest value f expected prductin cst. 8 DESCRIPTION OF IMPLEMENTATION AND DISCUSSION Our system has been implemented using Java. All databases are maintained in Micrsft Access. A designer can use a brwser t cnnect t ur server. Brwser autmatically dwnlads the Java 16

17 prgram, and it runs in the brwser. We use SEER-DFM cst estimatin sftware t estimate the cst assciated with varius material and prcess sequences. Figure 6 shws ur system architecture. Our system is based n an pen architecture and has the fllwing characteristics: 1. It is easy t include new materials r prcesses. The system reads materials, prcesses infrmatin thrugh data files that are autmatically generated by Micrsft Access. When a system administratr needs t add new materials r prcesses frm, she/he can update the database stred in Access. 2. It is easy t edit grss shape types and shape mdificatin feature types. The system makes use f tw definitins files: grss shape types file, and shape mdificatin feature types file. By editing these files and incrprating apprpriate changes in the prcess infrmatin mdel, the system can wrk with an expanded set f grss shapes and shape mdificatin features. 3. It is easy t adjust the cmplexity cefficients. We use SEER-DFM t d cst estimatin. SEER-DFM requires selecting prper cmplexity cefficients fr crrect cst estimatin. We prvide a file fr each prcess cst estimatin mdel t include all cmplexity cefficients. This allws us t recnfigure the cst estimatin system easily. Let us cnsider design f a husing. A rugh sketch f its frm is shwn in Figure 7. Varius design requirements are given as fllws: Business Requirements: Ttal Prductin Value Range: 500 t 2000 Material Requirements: Density Range: 6.8 t kg/m 3 Elastic Limit/Density Range: 23 t 33 MPa/(10 3 kg/m 3 ) 2 Fracture tughness/density Range: 1.8 t 3.0 MPa m 1/ /(10 3 kg/m 3 ) Yung's mlulus/density Range: 14 t 23 GPa/ (10 3 kg/m 3 ) High Level Frm Requirements: Envelp Size Range: 3 t mm 3 Several material and primary prcess cmbinatins are generated using this infrmatin. We assume that fr every cmbinatin the detailed frm requirements are as fllws: Grss Shape 17

18 Grss shape type: 3D Parallel Slid Number f Planes: 11 Number f Curve Surfaces: 0 Bunding bx length: 220 t 230 mm Bunding bx width: 120 t 130 mm Bunding bx Height: 140 t 150 mm Vlume: 3.7 t 4.5 ( Tlerance: 1 t 3 millimeter Feature 1: inner husing Feature type: Pcket => Nn-cylindrical mm ) Surface Area: 1.5 t 1.7 ( 10 mm ) Rughness: 10 t 13 micrmeter Number f Planes: 5 Number f Curve Surfaces: 0 Bunding bx length: 180 t 190 mm Bunding bx width: 80 t 90 mm Bunding bx Height: 120 t 130 mm Number f features: 1 Vlume: 1.7 t 2.2 ( Tlerance: 1 t 3 millimeter Feature 2: face Feature type: Surface finish => flat mm ) Surface Area: 0.9 t 1.0 ( 10 mm ) Rughness: 10 t 13 micrmeter Number f Planes: 1 Number f Curve Surfaces: 0 Bunding bx length: 220 t 230 mm Bunding bx width: 120 t 130 mm Number f features: 1 Surface Area: 0.9 t 1.5 ( 4 2 Tlerance: 0.2 t 0.3 millimeter Rughness: 1 t 3 micrmeter Feature 3: hle 1 Feature type: Hle => Cylindrical 10 mm ) Number f Planes: 0 Number f Curve Surfaces: 1 Bunding bx length: 30 t 32 mm Bunding bx width: 30 t 32 mm Bunding bx Height: 10 t 12 mm Number f features: Vlume: 7.1 t 9.6 ( 10 mm ) Surface Area: 0.9 t 1.2 ( Tlerance: 0.03 t 0.06 millimeter Rughness: 0.5 t 0.7 micrmeter Feature 4: hle 2 Feature type: Hle => Cylindrical mm ) Number f Planes: 0 Number f Curve Surfaces: 1 Bunding bx length: 10 t 12 mm Bunding bx width: 10 t 12 mm Bunding bx Height: 16 t 18 mm Number f features: 10 t Vlume: 1.3 t 2.0 ( 10 mm ) Surface Area: 5.0 t 6.8 ( Tlerance: 0.5 t 0.7 millimeter Rughness: 3 t 6 micrmeter mm ) The fllwing sequence is ne f the sequences in the set f nn-dminated sequences: Material: Grey Cast Irn Step1: Sand casting t meet the design requirements f grss shape, feature1. Step2: Milling t meet the requirements f Feature 2. Step3: Drilling t create hle 1 and hle 2. Step4: Grinding t meet the precisin requirement f hle 1. We calculated the cst fr this sequence using the SEER-DFM cst estimatin tl. Table 3 shws estimated csts prduced by SEER-DFM system. 18

19 Table 3: Cst Assciated with the Sequence fr Example 1 Prcess Lwer Bund Upper Bund Sand casting cst ($) milling cst ($) drilling cst ($) grinding cst ($) ttal cst ($) If the designer decides t use this sequence, then he shuld generate the prcess dependent design based n the prcess cnstraints f casting as shwn in Figure 8. Table 4 shws the cmparisn f the fur ther material and prcess selectin systems with ur system. As shwn in this table ur system cmpares favrably with all the systems n the defined criteria. It has tw main nvel features nt fund in thers systems: (1) it can autmatically generate prcess sequences fr varius shape mdificatin features; and (2) it is fully integrated with an external cst evaluatin system. Table 4: Cmparatin f Varius Material Prcess Selectin Systems System Metrics Web-based r nt Number f materials Number f prcesses MAS [Smith 1999] DA [Kunchithapath am 1996] CPP [Feng and Zhang 1999] CES [Ashby and Easwi 1999] WisePrM Yes N N N Yes Nt available Nt available Custmizatin N N N Yes Yes Input Hw input is defined Single pint "High", "Medium", "Lw" Single pint Range Range Prcesses Prcess sequences Single prcesses Single prcesses Prcess Sequences Prcess Sequences Output Selectin Algrithm Cst reprt Yes N N N Yes Ranking Yes N N Yes Yes Algrithm Autmatin in Prcess Sequence Generatin Database search Semiautmatic Database search Database search Database search Branch & Bund and Database Search Nt Available Nt Available Nt Available Autmatic Cupling f Prcess and Material Yes Yes Yes Yes Yes 19

20 Cupling f Prcess and Shape Integratin with External Cst Estimatin System Yes N Yes Yes Yes N N N N Yes Apart frm the fur systems described abve, ther systems in material and prcess selectin area include CAMPS (Cmputer Aided Material/Prcess Selectin) [Bck 1991], OSPAM [Lam 1995] and MAMPS (Material and Manufacturing Prcess Selectin) [Giachetti 1998]. 9. CONCLUSIONS In this paper we have described a systematic apprach t material and prcess selectin during embdiment design f mechanical cmpnents. We fllw a three-step apprach t prcess and material selectin. We first generate cmbinatins f materials and primary prcesses. Then, we find the set f nn-dminated sequences fr each cmbinatin fund in the first step by adding secndary and tertiary prcesses t meet detailed frm requirements and pruning dminated sequences. Finally, designers can use ur cst analysis functins t cmpare different nndminated sequences t select the final cmbinatin f material and prcess sequence. We have implemented ur apprach and algrithms in a prttype system called WiSePrM (Wizard fr Selecting Prcesses and Materials). Our system demnstrates the fllwing: 1. It shws that it is pssible t accunts fr imprecisin in design parameters in selecting material and prcesses. Effectiveness f ur algrithm depends n hw tightly varius parameters can be defined during the embdiment design stage. If parameters have very large ranges, then very few slutins dminate ther slutins and the pruning cnditins d nt wrk very effectively. If parameters are have reasnably small ranges, then pruning cnditins wrk effectively. 2. It shws hw t autmatically generate prcess sequences t satisfy the frm requirements when a single prcess cannt meet all the frm requirements. Unlike previus appraches, there is n restrictin n the number f prcesses used in a sequence. Therefre, it allws us t slve prblems that require fur r mre prcesses. 3. It shws hw t cnstruct an pen architecture system in which databases and algrithms are cmpletely separated. Therefre, as sn as new material and/r prcess infrmatin is added int the database, it can be immediately used in ur system. We believe that ur system will allw designers t explre a large number f material and prcess ptins during the embdiment design stage and t select the mst cst-effective cmbinatin. By selecting the material and prcess cmbinatin during the early design stages, designers can ensure that the detailed design is cmpatible with all f the prcess cnstraints fr the selected prcesses. 20

21 The current system has the fllwing limitatins: We have nt cnsidered thse classes f prcesses that change material prperties such as annealing and quenching. We nly use cst estimatin sftware SEER-DFM t d cst estimatin. Due t the limitatin f SEER-DFM, nly limited number f prcesses and materials are supprted. The system uses very simplistic measures f prductin tl lead-time and prductin rate parameters. In general, these parameters depend n the manufacturing facility. Imprvements will be needed t handle these parameters in a better way. If n suitable prcess material cmbinatin is fund, the system des nt recmmend hw t mdify design requirements. Techniques described in [Gupta et al 1997] can be used achieve this functinality. Current cst estimatin nly estimates manufacturing csts. Life cycle csts may play a majr rle in the decisin and they shuld be cnsidered in future extensin. Current system handles ne cmpnent at a time. One pssible extensin will be t extend the system t wrk with assemblies. Acknwledgments and Disclaimer. The authrs gratefully acknwledge the supprt prvided by NIST's System Integratin and fr Manufacturing Applicatins (SIMA) prgram. Cmmercial prduct r cmpany names in this paper are given fr infrmatinal purpses nly. Their use des nt imply recmmendatin r endrsement by the Natinal Institute f Standards and Technlgy r the University f Maryland. REFERENCES [Ashby and Easwi 1999] M. F. Ashby and A. M. Esawi. Cst estimatin fr prcess selectin. In Prceedings f ASME Design fr Manufacture Cnference, Las Vegas, Nevada, September, [Bck 1991] L. Bk. Material prcess selectin methdlgy: design fr manufacturing and cst using lgic prgramming. Cst Engineering, 33(5): 9-14, [Crmen et al 1990] T. H. Crmen, C. E. Leisersn and R. L. Rivest. Intrductin t Algrithms. New Yrk, McGraw-Hill, [Dewhurst and Bthryd 1988] G. Bthryd and P. Dewhurst. Early cst estimating in prduct design. Jurnal f Manufacturing Systems, 7(3): ,

22 [Feng and Zhang 1999] S. Feng and Y. Zhang. Cnceptual prcess planning a definitin and functinal decmpsitin. In the Prceedings f the Internatinal Mechanical Engineering Cngress and Expsitin, Manufacturing Science and Engineering, 1999, pp [Feng and Sng 2000a] S. Feng and E. Sng. Infrmatin mdeling n cnceptual prcess planning integrated with cnceptual design. The 5 th Design Fr Manufacturing Cnference, September [Feng and Sng 2000b] S. Feng and E. Sng. Infrmatin mdeling n cnceptual design integrated with prcess planning. In the Prceedings f Sympsia fr Design Fr Manufacturablity, the 2000 Internatinal Mechanical Engineering Cngress and Expsitin, Nvember [Galrath Inc, 1999] Galrath Inc. SEER-DFM cst designer fr parts, prcess and assembly. User s Manual, [Giachetti 1998] G. Giachetti. A decisin supprt system fr material and manufacturing prcess selectin. Jurnal f Intelligent Manufacturing, 9(3), , [Gupta et al 1997] S.K. Gupta, D. Das, W.C. Regli, and D.S. Nau. Autmated manufacturability analysis: A survey. Research in Engineering Design, 9(3): , [Gupta 1997] S.K. Gupta. Using manufacturing planning t generate manufacturability feedback. ASME Jurnal f Mechanical Design, 119:73--79, March [Kunchithapatham 1996] A. Kunchithapatham. A Manufacturing Prcess and Materials Design Advisr. MS thesis, University f Maryland, Cllege Park, MD, [Lam 1995] G. Lam. Autmated High Level Prcess Planning and Manufacturability Analysis fr Agile Manufacturing. MS thesis, University f Maryland, Cllege Park, MD, [Pahl and Beitz 1996] G. Pahl and W. Beitz. Engineering Design A Systematic Apprach. 2 nd editin. Springer-Verlag, [Smith 1999] C. S. Smith. The Manufacturing Advisry Service: Web Based Prcess and Material Selectin. PhD thesis, University f Califrnia, Berkeley, CA, [Sriram 1997] R. D. Sriram. Intelligent Systems fr Engineering: A Knwledge-based Apprach. Springer Verlag,

23 Hle A 50±0.500mm Hle B 50±0.050mm Hle A 50±0.500mm (a) Design 1 (b) Design 2 Hle C 50±0.010 mm Hle B 50±0.050mm (c) Design 3 (d) Design 4 Hle A 50±0.500mm Hle C 50±0.010 mm Hle C 50±0.010 mm Hle B 50±0.050mm (e) Design 5 (f) Design 6 Figure 1: Examples that illustrate hw presence f a feature affects the prcess selected fr anther feature

24 Grss Shapes ROTATION Cylinder Tapered Cylinder PRISMATIC Regular plygn Cnvex Irregular plygn Cncave Unifrm thickness Thin Thick Nn unifrm thickness SHEET Flat Nn-axisym Dished Axisym Dished Deep-reentrant Dished 3-D Parallel Slid Transverse Slid Figure 2: Grss Shape Types

25 Features HOLE Cylindrical Nn-Cylindrical POCKET Cylindrical Nn-Cylindrical PROFILE Cylindrical Nn-Cylindrical SLOT Straight Surface Finish Curve Flat Cylindrical Curved Figure 3: Shape Mdificatin Feature Types

26 Prcess Material Business parameters Cmpatibility Dimensinal parameters Tlerance and Surface rughness Frm Prcess precnditin requirements Mechanical parameters Envirnmental parameters Thermal parameters Electrical parameters Grss shape Shape mdificatin Features Figure 6: 4: prcess material infrmatin mdel

27 Quantity: Material: graphite cast irn Shape mdificatin feature: cylindrical hle Number f Features: 1-2 Directin: parallel t grss shape Dimensinal requirements: diameter: mm length: mm Precisin requirements: tlerance: mm rughness: um Prcess: sand casting Prcess: drilling Unfinished requirements: precisin Current precisin: tlerance: 1-3 mm rughness: um Unfinished Requirements: precisin Current precisin: tlerance: 0.05mm rughness: um Prcess: internal grinding Unfinished Requirements: nne Figure 5: Example f a Prcess Optin Tree fr a Shape Mdificatin Feature

28 User Frm Parameters Material Parameters Business Parameters Brwser Cst Estimatin Sftware Seer-DFM Web Server Prc./Mat. Cmpatibility Database Prcess Capability Database Material/Prcess Sequences Java Applet Material Capability Database Figure 6: System Architecture

29 Figure 7: A rugh sketch f husing

30 Fillet Draft angle Chamfer Figure 8: Prcess dependent design f husing