Journal of Materials Processing Technology 139 (2003) 476–480

Study on intelligent reasoning of product assemblysequences and simulation

L.C. Zhaoa,∗, D.Q. Zhua, B. Zhaob,1

a Department of Mechanical Engineering of East China Shipbuilding Institute, Zhenjiang, Jiangsu 212003, PR Chinab Department of Thermal Engineering, Tsinghua University, Beijing 100084, PR China

Abstract

This paper proposes a layer-built and degree relation assembly (LDRA) model after analyzing some existing assembly models and newDFA method, which has 15 key-part criteria. It can evaluate the product assembly quality by calculating the assembly efficiency and theratio of the part function value to its assembly difficulty and it is integrated into CAAPP. A prototype system has been developed forreducing gears and it can generate assembly sequences by AI reasoning, evaluate the assembly quality by the proposed DFA method andcheck the assembly interference by 3D simulation in UGII environment. In the proposed assembly reasoning method, the goal is not toobtain an optimum assembly sequence but a better one, it is divided into two steps: (1) to determine the relational priority between theparts, (2) to obtain topology sequences; and it places emphasis on the assembly reliability.© 2003 Elsevier Science B.V. All rights reserved.

Keywords: LDRA model; AI reasoning; DFA; Assembly simulation

1. Introduction

The production has been an order model and prod-uct competition is more and more intensive because themarket becomes more open to the world. Every enter-prise has to shorten the period of developing and produc-ing new products and to occupy the market in advancewith its best efforts. Some experts indicate that the de-sign expense is only 5% of the whole product price, butis more than 80% of the product price, the main qualitybeing based on the design quality. Assembly design andsimulation play an important role in the product designprocess.

There exist many assembly models and DFA methods,they have some advantages and disadvantages and used inthe certain product design cases. This paper proposes alayer-built and degree relation assembly (LDRA) model andan improved DFA method, which are proved by a developedprototype system for reducing gears.

∗ Corresponding author.E-mail address: zjlczhao@public.zj.js.cn (L.C. Zhao).

1 Present address: Department of Mechanical Engineering, Universityof Illinois, Urbana, IL, USA.

2. LDRA product assembly model

2.1. Introduction

The LDRA model is proposed after analyzing the ad-vantages and disadvantages among two branch trees, a 2Dtopology relation tree model and a virtual link relation treemodels[1–5]. The two branch tree model is very simple,but it can mainly reflect only the function design process.The 5E model (represents relation among five elements) istoo complicated to be applied but the information includedin the 2D model is not enough for reasoning. Thus a hy-brid model should be investigated. The LDRA model inte-grates the main advantages of the above. First, a productconsists of parts and assemblies and an assembly has itssub-assemblies and parts, so there exists a layer relation be-tween a product and its assemblies or between an assemblyand its sub-assemblies (parts). This layer relation implies anassembly order, the parts on the down-layer having an as-sembly priority. Secondly, parts on the same layer also havesome relations, and these relations are not equal and havedegrees. This degree relation also implies an assembly or-der. This paper divides the assembly relations into the fol-lowing types: base part relation (degree 1); positioning andclamping relation (degrees 2 and 3); high relation among

0924-0136/03/$ – see front matter © 2003 Elsevier Science B.V. All rights reserved.doi:10.1016/S0924-0136(03)00519-3

L.C. Zhao et al. / Journal of Materials Processing Technology 139 (2003) 476–480 477

Fig. 1. ASM an example of products.

one group parts (degree 4); side relation on one shaft (de-gree 4); inner and outer relation (degree 5); other relation(degree 6).

2.2. Example

(1) Divide layers (Fig. 1)Assembly c1 consists of parts 4, 5, 6; product ASM

(Fig. 1) consists of parts 1, 2, 3 and c1.(2) Description of the ASM using LDRA model (Fig. 2)

In Fig. 2 (r, v) is a set of relation and degree values,e.g. (a) (r1, v1) indicates relation between parts 1 and 2,where 1 is position part of 2 (degree 2) and 2 is clamppart of 1 (degree 3); (b) (r2, v2) indicates relation be-tween part 1 and assembly c1, where 1 is the positionpart of c1 (degree 2); (c) (r4, v4) indicates relation be-tween parts 2 and 3 where 3 is position part of 2 (degree2) and 2 is clamp part of 3 (degree 3).

3. New DFA methods

There are DFA methods: Hitachi AEM, B&D assemblydesign method, NEC and Xerox DFA methods[4,6]. Afteranalyzing the above methods some disadvantages are dis-covered: (1) the key-part criteria are not enough and somecriteria should be aided; (2) how to integrate CAAPP andDFA and how to make suggestions by quantitatively analyz-ing should be considered.

Fig. 3. System functions and working process.

Fig. 2. Description of ASM by LDRA model.

The DFA method, presented in this paper, is integratedwith the assembly sequence reasoning (ASR) and thekey-parts are selected by more criteria and by calculatingfunction values (Fv) also. The assembly difficulty value(Dv) is calculated based on the difficulty factors which aresaved in a database and the suggestions of improvementsare made according to the value of Fv/Dv. The assemblyefficiency is calculated to decide if DFA analysis will becontinued or not.

4. Functions of integrated ASR, DFA and simulationsystem

As mentioned above, product quality and price are mainlybased on design quality and the more important factor is thereasonable structure of the product. Product assembly designand DFA analysis can provide necessary evaluation for theproduct structure and an interference check from assemblysimulation (AS) can avoid redone work. To integrate ASR,DFA and simulation can provide a self-improvement processfor assembly design (Fig. 3).

4.1. Assembly model position in the system

The assembly model is the core of the developed CAAPPsystem and this can be seen inFig. 4. The information

478 L.C. Zhao et al. / Journal of Materials Processing Technology 139 (2003) 476–480

Fig. 4. Position of the assembly model.

in the assembly model includes the management, descrip-tion of assembly relation, difficulty and geometry infor-mation and result files. The management information is inthe core position, on the periphery is the application layer,all operations in this layer being related to the assemblymodel.

4.2. Functions and process of the system

A developed CAAPP system is based on knowledge rea-soning and it includes two sub-systems: the ASR and DFA(ASRD) analysis, and the AS. The former is programmed inthe VFP environment and the later is developed by UG-Griplanguage and Macro command. The functions and the work-ing process are shown inFig. 3. The input data are fromthe product assembly structure sketch, technical require-ments and production information. The users can describethe product through the input module and the data will besaved into the database. The ASRD sub-system generatesthe assembly sequences based on a knowledge base and thedatabase, then the assembly process planning file will begenerated after selecting the assembly tools and calculat-ing the assembly times. This file can be output in a cer-tain form as demanded by the users. The assembly modelis also the core of this sub-system and it is related to theframe of reasoning, the structure of part information andthe knowledge base. The assembly reasoning is the key ofthis sub-system and it transfer parts from disorder to or-

Fig. 5. Menu of the system.

der. The AS sub-system and DFA module will be discussedlater.

5. Process of ASRD

This sub-system menu is shown inFig. 5 and its func-tions are: (A) assembly sequence generation; (P) file gener-ation; (D) DFA analysis; (L) database and knowledge basemaintenance; whilst (F) (T) (H) items are VFP functions.The data for the ASR module are product assembly data(including management and assembly relation data), assem-bly tools, results and intermediate data. The data for DFAare assembly difficult factor data and assembly sequencesand product data. ASR is based on knowledge and it isexpressed by rules, where every rule has a left and rightpart.

The left are conditions which indicate that this rule willbe called when the conditions are satisfied. The right areactions which indicate what will be done when this rule iscalled.

• General form of rule◦ If condition 1, AND/OR condition 2. . . then conclu-

sion 1, OR conclusion 2. . .• Typical rules

◦ If Pi is the base part Then Pi is first assembly (degree1).

◦ If Pi is position part of Pj Then Pi> Pj (Pi assemblybefore Pj; degree 2).

5.1. Process of ASR

There are many ASR methods. Homen de Mello andSanderson[2] proposed a method based on assembly con-straints; Ko and Lee[7] suggested a method based on matingconditions; whilst other methods[8–10]are based on knowl-edge. These methods are mainly for research purposes, be-ing generally too complicated to be used practically. For themethod introduced in this paper, its goal is not to get a op-timum assembly sequence but a better one, and it has someimprovements and especially it strongly emphasizes on theassembly reliability.

The ASR is a topology sequencing method based on AI.This method is divided into two steps: (1) To obtain a relationpriority between the parts, to call the rules and to take part

L.C. Zhao et al. / Journal of Materials Processing Technology 139 (2003) 476–480 479

Fig. 6. Process of ASR.

assembly data, then to use the rules to compare one partwith another continuously and to save the obtained assemblyrelation priority into T1 and T2 tables. This assembly orderis partial and it is named the local order on one mathematicalset. (2) Topology sequencing: to obtain the product assemblyorder by the topology sequencing method and this processis called to get the whole order from the partial order on onemathematical set (Fig. 6).

The assembly sequence obtained by the traditional topol-ogy sequencing method is not optimum because the assem-bly order on the connection point, for which the enter degreeis zero (E0), is selected randomly or naturally, so the part re-liability is sometimes not retained. The enter degree meansthe number of arcs to the connection point. The improved

Fig. 7. Interface for calculating the ADE and generating suggestions.

Fig. 8. Menu of the simulation sub-system.

method used in this system ensures that when a position partis assembled, its clamp part has an assembly priority, thusthe assembly reliability can be ensured.

5.2. Process of DFA analysis

The DFA module functions are: (1) to calculate the partfunction values (Fv), assembly design efficiency (ADE), as-sembly difficult values (Dv) and times; (2) to analyze whatkind of parts need to be improved and then to give sugges-tions. Fig. 7 shows the interface for calculating ADE andgiving suggestions. The Fv is calculated according to 15key-part criteria, when Fv≥ Fv0 (Fv0, presetting value)the part is a key-part; ADE= (∑

Fv(i)/Fv0∗ n) × 100,

wheren is the part number; if ADE greater than the goalvalue, it indicates that the product design is very good andthe DFA analysis should be stopped. The goal ADE dependson the product design level and the resources of enterprises.

480 L.C. Zhao et al. / Journal of Materials Processing Technology 139 (2003) 476–480

In Fig. 7, E is the ADE; IP the improvement parts; S thesuggestion part.

6. Simulation of the assembly process

The menu of the AS sub-system is shown inFig. 8 andit includes the modules: assembly parts (APs), assemblyconditions (ACs), interference checking (IC) and assemblyviewing (AV). The AP module can call a part on the screen tobe assembled according to the generated assembly sequence.The AC module is used to interactively enter the assemblyposition relation including mating, align and orient relations.The IC module can check the interference between parts.The AV module can record the process of assembling partsand then can reshow it.

7. Conclusions

The CAAPP plays a very important role in the processof product optimum design. The advantages of the LDRAmodel and the improved DFA method are demonstrated by adeveloped prototype system for reducing gears. The LDRAmodel has been successfully demonstrated to analyze theassembly relation between two entities, but it should be im-proved in order to make it more useful. The prototype system

is a basic model to develop application systems integratingCAAPP and DFA analysis functions.

References

[1] K. Lee, D.C. Gossard, A hierarchical data structure for representingassemblies. Part 1, CAD 17 (1) (1985) 15–19.

[2] L.S. Homen de Mello, A.C. Sanderson, A correct and completealgorithm for the generation of mechanical assembly sequence, IEEETrans. Robot. Automat. 7 (1) (1991) 228–240.

[3] M. Shi, S. Tang, M. Li, A review of assembly sequences planning,Comp. Res. Dev. 11 (6) (1994) 30–34 (in Chinese).

[4] T. Gu, G. Gao, X. Xu, Study on the quantitative DFA method basedon function analysis, China Mech. Eng. 9 (6) (1998) 3–5 (in Chinese).

[5] T. Gu, G. Gao, X. Xu, Study on the approach for generation andevaluation of assembly sequences for assembly process planning,CIMS 4 (1) (1998) 25–27 (in Chinese).

[6] Q. Su, Study on computer aided assembly sequence planning andDFA methods and development of integrated system, Ph.D. Thesis,Xian Jiaotong University, 1997 (in Chinese).

[7] H. Ko, K. Lee, Automatic assembling procedure generation frommating condition, CAD 19 (1) (1987) 3–10.

[8] H.K. Tonshoff, E. Menzel, H.S. Partk, A knowledge-based systemfor automated assembly planning, Ann. CIRP 41 (1) (1992) 19–24.

[9] G. Dini, M. Santochi, Automated sequencing and subassembly de-tection in assembly planning, Ann. CIRP 41 (1) (1992) 1–4.

[10] D.Q. Zhu, L.C. Zhao, J. Zhang, Assembly sequence planning basedon the layer-built and degree relation assembly model, J. East ChinaShipbuilding Inst. 14 (1) (2000) 71–75 (in Chinese).