a comparative study of assembly planning in traditional and virtual environments

546 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 29, NO. 4, NOVEMBER 1999

A Comparative Study of Assembly Planningin Traditional and Virtual Environments

Nong Ye, Prashant Banerjee, Amarnath Banerjee, and Fred Dech

Abstract—This paper presents an experiment that investigatedthe potential benefits of virtual reality (VR) environments insupporting assembly planning. In the experiment, fifteen sub-jects performed an assembly planning task in three differentconditions: a traditional engineering environment (TE), a nonim-mersive desktop VR (DVR) environment, and an immersive careautomatic virtual environment (CAVE) VR environment (CVRenvironment). The effects of the three conditions on the subjects’performance were analyzed. The subjects’ performance time inthe TE condition was significantly longer than that in the DVRcondition and that in the CVR condition, whereas the differencein performance time between the DVR condition and the CVRcondition was not significant. The total number of problematicassembly steps in the TE condition was significantly greater thanthat in the CVR condition. Specifically, the subjects’ assemblysequences in the TE condition involved more reorientations thanin the DVR condition. The number of difficult assembly stepsin the TE condition was significantly greater than that in theDVR condition, which was significantly greater than that in theCVR condition. The number of dissimilar assembly steps in theTE condition was significantly greater than that in the CVRcondition, which was significantly greater than that in the DVRcondition. Hence, the results revealed advantages of the two VRenvironments over the traditional engineering environment inimproving the subjects’ overall assembly planning performanceand in minimizing the handling difficulty, excessive reorientation,and dissimilarity of assembly operations.

Index Terms—Assembly planning, computer-aided manufac-turing, rapid phototyping, virtual reality

I. INTRODUCTION

A SSEMBLY planning determines the sequence andprocess details of assembly operations that put individual

parts together into an assembly [1]–[5]. An assembly plan hasa large impact on production efficiency and costs. Assemblyplanning usually is performed by production engineersfor an assembly design. Since assemblies have becomeincreasingly complex (i.e., involving hundreds of parts),assembly planning has presented a considerable challengeto production engineers. Many factors must be considered inassembly planning [1]–[6]. For example, production engineersmust examine the geometric design of an assembly to ensure afeasible assembly sequence that does not induce part collisionsand part trappings. Production engineers also need to look intoother factors such as the reorientation, directionality, stability,manipulability, and parallelism of assembly operations, aswell as the complexity of tools and fixtures. Those factors

Manuscript received March 28, 1998; revised March 18, 1999.N. Ye is with the Department of Industrial Engineering, Arizona State

University, Tempe, AZ 85287-5906 USA.P. Banerjee, A. Banerjee, and F. Dech are with the Department of

Mechanical Engineering, University of Illinois, Chicago, IL 60607 USA.Publisher Item Identifier S 1094-6977(99)08206-1.

determine a good assembly sequence with respect to efficiency,costs, product safety, and operator safety relating to assemblyoperations [6]–[12].

Aiming at easing the assembly planning task for productionengineers, many studies have been carried out to automate thegeneration of assembly plans [13]–[28]. Since the assemblysequence is the backbone of an assembly plan, most of theefforts have focused on the automatic generation of assemblysequences.

Although progress has been made to generate feasibleassembly sequences even for complex assemblies, there stillexist difficulties in generating good assembly sequences [6],[11]. First of all, it is difficult to quantify goodness criteria forcomputerizing the goodness evaluation of assembly sequences,not to mention that there may exist conflicts among goodnesscriteria. Another difficulty lies in the computational complexityof searching for good assembly sequences in a large searchspace of feasible assembly sequences. The presentation offeasible assembly sequences to production engineers for amanual selection of good assembly sequences is not practical,because a large number of unfamiliar assembly sequencessimply confuse and overwhelm production engineers.

Since there is a long way to realize the automatic generationof assembly sequences, assembly planning still relies onproduction engineers. Caldwell, Ye, and Urzi reported a studywith a manufacturing corporation to analyze computer toolsavailable to support assembly planning [29]. It was concludedthat many computer-aided design/computer-aided manufac-turing (CAD/CAM) systems used by production engineersprovided little support to assembly planning. Decisions fortransforming an assembly design into an assembly plan andthen to an assembly production system were made manuallyand were communicated between departments (e.g., productdesign, production engineering, and manufacturing facility,which were often geographically separated) mostly throughconventional channels rather than the electronic channel ofinformation sharing.

Recently, ideas of supporting the assembly planning task ofproduction engineers in ways other than the automatic gen-eration of assembly sequences have emerged [11], [30]–[34].Among those ideas, assembly planning in a virtual reality (VR)environment has attracted much attention [35]–[36]. Advancesin automatically loading CAD data of an assembly design intoa VR environment have enabled the rapid prototyping of anassembly and its parts in a VR environment [31]–[32]. This hasopened a new avenue for assisting production engineers in as-sembly planning. Through viewing and manipulating a virtualassembly and virtual parts, production engineers are able to

1094–6977/99$10.00 1999 IEEE

YE et al.: A COMPARATIVE STUDY OF ASSEMBLY PLANNING 547

create, express, simulate, and evaluate an assembly sequence ina virtually natural but more cost-effective setting. Productionengineers are able to examine various aspects (i.e., feasibilityand goodness) of an assembly sequence by performing virtual-assembly operations on virtual parts just as real parts arebeing put together through real assembly operations into a realassembly. Thus, the assembly sequence can be tested while it isgenerated in a VR environment. Furthermore, the simulation ofassembly operations can be saved, demonstrated, and printedout as a specification of the assembly plan. Therefore, a VRenvironment will enable an integration of assembly designand assembly planning, a more realistic presentation of anassembly design, a simulation-based testing and verificationof assembly planning outcome, and an integration of assemblyplanning and assembly specification.

In contrast, in a traditional engineering environment, assem-bly design and assembly planning are performed separatelyby people in different departments. After product designerscomplete an assembly design and generate product drawings,production engineers perceive an assembly through its designdrawings and generate an assembly sequence. Since both theunderstanding of an assembly and the generation of an assem-bly sequence are performed mentally, assembly planning is acognitively demanding and thus error-prone task. Productionengineers often have to use physical prototypes of real partsand trials of real production runs for verifying their assemblyplan and revealing any errors. Prototypes of real parts and trialsof real production runs come with high costs and prolongedcycles of assembly planning.

Therefore, a VR environment has the potential to offera more natural, powerful, economic, flexible platform thana traditional engineering environment to support assemblyplanning. This paper presents an experiment that examinesthe potential benefits of using VR environments to supportassembly planning by comparing the assembly-planning per-formance of subjects in traditional and VR environments.

II. EXPERIMENT

A. Apparatus

In the experiment, two VR environments were investigated:a nonimmersive desktop VR environment (DVR) and animmersive CAVE VR environment (CVR). The DVR en-vironment that we used for our experiment consisted of aSilicon Graphics workstation with IRIX CosmoPlayer VRML2.0 browser plug-in to Netscape [37].

The CVR environment uses an IRIS Performer CAVEinterface developed at the Electronic Visualization Laboratory,University of Illinois, Chicago [38]. The care automated virtualenvironment (CAVE) is a 10 10 9-foot room that usesrear-projected, high-resolution projectors to produce an immer-sive, three-dimensional (3-D) environment. The commerciallyavailable CAVE environment produces a 3-D stereo effect bydisplaying, in alternating succession, the left and the right-eyeviews of the scene as rendered from the viewer’s perspective.These views are then seen by the user through a pair ofLCD shutter glasses whose lens opens and closes 48 times

per second in synchronization with the left and right-eyeviews. The correct viewer-centered projection is calculatedbased on the viewer’s position and orientation as determinedby an electromagnetic tracking system. The position andorientation of a 3-D wand are also tracked. This wand allowsfor navigation in the virtual world.

The immersion in CVR provides subjects with a morerealistic sense of virtual assemblies and parts. However, CVRcosts much more than DVR. It is likely that a manufacturingcompany is willing to set up only one fully immersive VRenvironment and that the access to the VR environment isrestrictive. A low-end VR display on a desktop computer,such as the DVR environment in this study, is more affordableand accessible, although it is not clear how well it supportsassembly planning in comparison to a high-end, immersiveVR environment. Therefore, in this study we investigated botha high-end immersive VR environment and a low-end, non-immersive VR environment to examine possible performancedifferences between them.

B. Design of Experiment

The experiment was based on a between-subject factorialdesign. The assembly-planning environment as the indepen-dent variable has three conditions: a traditional engineeringenvironment (TE), a nonimmersive DVR environment, and animmersive CVR environment. The TE condition provided thebasis of comparison with the VR conditions. The three con-ditions differed in ways in which an assembly was presentedand manipulated. The assembly used in the experiment wasan air-cylinder that consisted of 34 parts. The assembly wasa real product from a company. A main reason for selectingthis assembly for the study is the moderate complexity of theassembly (i.e., the number of parts). The assembly presented acertain level of task difficulty to the subjects, and yet allowedthem to complete the experiment within a reasonable periodof time without fatigue.

For the TE condition, the air-cylinder assembly and each ofits parts were presented using a stack of hard-copy drawings.The drawings were generated using the commercial packageProEngineer from specifications gathered from the company.Each drawing is printed on an 8.5 in11 in sheet of paper.Drawings of both the solid-model type and the wireframetype were included in the stack to show the assembly in acomposite mode and in an exploded mode. Drawings of theassembly from different perspectives were provided to presentall features of the assembly. Fig. 1 shows a solid model of theair-cylinder assembly in an exploded mode. Fig. 2 shows asolid model of the air-cylinder assembly in a composite modefrom two different perspectives. From one perspective (viewA), the positions of two hose connectors were clearly shown,but the feature of the main shaft going through the bottomplate was not revealed. From another perspective (view B),the feature of the main shaft going through the bottom platewas revealed, but one hose connector was not shown.

The drawing of an assembly in an exploded mode withall parts labeled alphabetically was placed on the top of thestack (see Fig. 1). Part names and part labels were printed


Fig. 1. Solid-model drawing of an air-cylinder assembly in an exploded mode for the traditional engineering environment.

on this drawing. We refer to this drawing as the top assemblydrawing. Drawings of the wireframe type were used to presentparts, one drawing for each part. On a part drawing, thepart name and the part label were also printed. The stackof drawings were arrranged in the following order: a solid-model drawing of the assembly in an exploded mode onthe top of the stack, a solid-model drawing of the assemblyin an exploded mode from another perspective, two solid-model drawings of the assembly in a composite mode fortwo different perspectives, two wireframe drawings of theassembly in a composite mode for two different perspectives,and the wireframe drawings of parts in the alphabetical orderof part labels. Provided with a stack of drawings, subjectswere allowed to re-sort the drawings and view drawings fromdifferent angles.

For the DVR and CVR conditions, the solid model ofthe assembly was displayed in a composite mode and inan exploded mode (see Figs. 3 and 4). A hard copy of thesolid model drawing of the assembly in an exploded mode

(the top drawing of the assembly used in the TE condition)also was provided to subjects for showing part names andpart labels. Subjects were allowed to rotate and move theassembly in the composite mode and the parts of the assemblyin the exploded mode along certain axes. Hence, subjectswere able to simulate an assembly operation by moving andputting two parts together. As two parts came close, theDVR environment or the CVR environment attached themand displayed the subassembly if two parts fit in the movedirection to form their relationship as designed. If two partscannot fit in the move direction, it goes back out to its explodedposition.

The DVR and CVR conditions differed in that the assemblyand its parts were displayed on a 21 in color monitor in theDVR condition but were displayed in a CAVE room for theCVR condition. In the CVR environment, subjects wore stereoshutter glasses and used a wand instead of a mouse to makeuse of 3-D immersion in the CAVE room. In addition, theDVR environment did not provide user-centered perspective,


Fig. 2. Solid-model drawing of an air-cylinder assembly in a compositemode from two different perspectives for the traditional engineering envi-ronment.

whereas the CVR condition did. Using the provision in theCVR environment, subjects were able to view the assemblyinside–out if necessary. For example, subjects could dive intothe inside of a part of the assembly to view the exact fit of theassembly, which was not possible with the DVR environment.

In summary, the TE condition showed all features of theassembly and its parts using a limited number of perspectives,each perspective on a separate sheet. The DVR and CVRconditions presented all features of the assembly and its partsbased on specified perspectives by subjects, all in one place.The perspectives in both the TE condition and the DVR condi-tion presented the assembly from the outside. The CVR condi-tion provided user-centered perspectives of the assembly andits parts from both the outside and the inside, all in one place.

Fig. 3. Presentation of an air-cylinder assembly in a composite mode and inan exploded mode for a nonimmersive, desktop VR environment.

C. Subjects

Fifteen subjects from the University of Illinois, Chicagoparticipated in the experiment. They were graduate studentsand college students (juniors and seniors) with backgroundsin industrial engineering, mechanical engineering, electricalengineering, and art. Among the subjects were four femalesand eleven males. The subjects participated in the experimenton a voluntary basis. Except for one subject, all of the subjectsstated that they had no experience in assembly planningprior to the experiment. There were five subjects for eachexperimental condition (TE, DVR, or CVR). The subjects inthe TE condition had an average age of 24.8 with a standarddeviation of 2.68. The subjects in the DVR condition had anaverage age of 26.8 with a standard deviation of 3.96. Theaverage age of the subjects in the CVR condition was 25.0with a standard deviation of 4.00.


Fig. 4. Presentation of an air-cylinder assembly in a composite mode and in an exploded mode for an immersive CAVE VR environment.

D. Experimental Task

The subjects were asked to generate an assembly sequencefor the air-cylinder assembly that was presented in theirassigned environment.

For the TE condition, the subjects were asked to review thestack of drawings for the assembly and its parts, generate anassembly sequence for the assembly, and specify the assemblysequence on the provided sheet. The specification of an as-sembly sequence included the following items of informationfor each assembly operation: the step number, the descriptionof the assembly operation, and the part moved during theassembly operation. The subjects were allowed to view thedrawings from different angles and re-sort the drawings.

For the DVR condition, the subjects were asked to reviewthe assembly and its parts on the computer screen, assemblethe parts in the exploded mode of the assembly by rotatingand moving the parts while generating an assembly sequencefor the assembly, and specify the assembly sequence on theprovided sheet. The subjects were allowed to rotate the assem-bly in the composite mode and parts in the exploded mode ofthe assembly for viewing them from different perspectives.

In the CVR condition, the subjects were asked to performthe assembly planning task in a manner similar to that inthe DVR condition. The major difference between the DVRcondition and the CVR condition lies in the way in whichthe assembly and its parts were viewed (nonimmersive versusimmersive).

For each of the three conditions, the subjects were instructedthat there was no time limit on the assembly planning task.However, the subjects were asked to complete the assemblyplanning task in the shortest time possible without sacrificingthe quality of their assembly sequence. They were told that thequality of their assembly sequence would be evaluated basedon the feasibility and goodness criteria.

E. Experimental Procedure

The subjects were asked to complete the experiment in thefollowing steps.

1) Read and sign an informed consent form.2) Fill out a pre-experiment questionnaire.3) Complete the training and practice on assembly plan-

ning.


4) Perform an assembly planning task in the experimentalsession.

5) Fill out a postexperiment questionnaire.

From the pre-experiment questionnaire, information suchas the subjects’ age, gender, and experience with assemblyplanning was obtained.

Before the experimental session, the subjects received train-ing on assembly planning and their assigned environment.The subjects were asked to read a two-page document, whichintroduced them to assembly planning and illustrated assemblyplanning using an example assembly. The assembly was areduction gear assembly consisting of seven parts. The materialshowed a good assembly sequence for the assembly.

In the material, the feasibility and goodness criteria ofevaluating an assembly sequence were explained. A feasibleassembly sequence prevents part collisions and part trappingsthat are related to the geometric features and topologicalstructure of an assembly and its parts [6]–[12]. In addition topreventing part collisions and part trappings, a good assemblysequence minimizes the reorientation and difficulty of handlingparts and maximizes the efficiency and safety of handling parts[6]–[12]. Four specific goodness criteria were considered inthis study: reorientation, handling difficulty, similarity, andstability. The reorientation criterion measures the numberof excessive parts or assembly reorientation. The difficultycriterion measures the number of difficult assembly operations.The similarity criterion measures whether similar parts (e.g.,four rods in the air-cylinder assembly) are put together in asimilar manner for efficiency. The stability criterion measureshow tightly assembled parts stay together without falling apart.These goodness criteria are aimed at improving the efficiencyof assembly operations and preventing damages to parts whileperforming assembly operations.

The feasibility and goodness criteria were demonstratedto the subjects through a bad assembly sequence for thereduction gear assembly. Feasibility and goodness problemsof this assembly sequence were explained in the material. Thesubjects were encouraged to ask questions about the material.

After reading the material, the subjects were asked topractice an assembly planning task in their assigned conditionfor a gear assembly consisting of eleven parts. The purpose ofthis practice was to let the subjects get familiar with: 1) themethods to view and manipulate an assembly in their assignedcondition; 2) the generation of an assembly sequence based onthe considerations of feasibility and goodness criteria, and 3)the specification of an assembly sequence.

After a subject generated an assembly sequence, the ex-perimenter examined it and pointed out to the subject anyproblems with respect to the feasibility and goodness criteria.The subject was then asked to revise the assembly sequence.This process continued until the subject generated a goodassembly sequence. Then the subject was allowed to proceedto the experimental session. Therefore, through this practice,the subject’s understanding of assembly planning was testedand insured. In the training session, the subjects were allowedto ask questions at any time.

In the experimental session, the subjects performed anassembly planning task for the air-cylinder assembly (see

Fig. 5. Good assembly sequence for an air-cylinder assembly.

Fig. 1) in a similar manner as in the practice during the trainingsession. However, in the experimental session, the subjectsmust work independently without the question–answer inter-actions with the experimenter. In the experimental session, nofeedback was provided by the experimenter to the subjectsregarding the problems with their assembly sequence. Theassembly sequences generated by the subjects in the exper-imental session were later analyzed to collect the subjects’performance data.

After completing the assembly planning task in the ex-perimental session, the subjects were asked to fill out thepostexperiment questionnaire. The subjects were provided withrating scales from one to seven to rate their understandingof the air-cylinder assembly (one for “well” and seven for“poor”), difficulty in determining the feasibility aspect of theirassembly sequence (one for “easy” and seven for “difficult”),and difficulty in determining the goodness aspect of theirassembly sequence (one for “easy” and seven for “difficult”).

III. RESULTS

A. Subject Performance

Two categories of performance data were collected fromeach subject: performance time and performance quality. Thetime that each subject took to complete the assembly planningtask in the experimental session was recorded.

To collect performance quality data, each subject’s assemblysequence was analyzed to obtain seven measures:

NIF The number of infeasible assembly operations.NER The number of assembly operations requiring exces-

sive reorientation.ND The number of difficult assembly operations.NUS The number of unstable assembly operations.NDS The number of dissimilar assembly operations for

similar parts.NM The number of missing parts.TN The total number of problematic assembly operations

which was the sum of the other measures.

These seven measures were obtained by comparing a subject’sassembly sequence with a good assembly sequence for theair-cylinder assembly that we generated (see Fig. 5).

Each step in a subject’s assembly sequence was markedOK, infeasible, excessively reoriented, dissimilar, or unstableby comparing the assembly operation of installing a part in that


TABLE IDESCRIPTIVE STATISTICS OF EXPERIMENTAL RESULTS

step with the assembly operation of installing the same partin the good assembly sequence. For example, if a subject’sassembly sequence contained the sequential steps Q, G, andM, the step of installing M would be marked infeasible.An excessive reorientation was required to install D’s in thesequential steps of Q, E, D, D, F, D, and D as compared tothe way in which D’s were installed in the good assemblysequence. In the sequential steps of V, U, and T, the step ofinstalling T would be marked difficult. In the sequential stepsof Q, R, S, T, U, L, N, M, and G, the step of installing Lwould be marked dissimilar in the way of installing L, whichis similar to U on the other side of Q. In the sequential steps ofQ, E, and F, the step of installing E would be marked unstablebecause it formed a loose contact with Q and might fall apartwhen we work on F. If part B was not specified in an assemblysequence, a step was considered missing.

It should be noted that there exist a number of assemblysequences equivalent to the good assembly sequence shown inFig. 5 with respect to the quality of assembly sequences. Theair-cylinder assembly has the symmetrical structure centeredaround part Q (the center plate). The same group of parts wasadded to both sides of the center plate. The assembly sequencecould start from either side of the center plate, which yieldedthe same level of feasibility and goodness measures for eitherassembly sequence. For example, the sequential steps of Q, O,N, M, G, R, S, and T were equivalent to the sequential stepsof Q, O, R, S, T, N, M, and G.

After marking each step of a subject’s assembly sequence,the number of infeasible steps, excessive reorientation steps,difficult steps, dissimilar steps, unstable steps, and missingsteps were counted. Values were calculated for variablesNIF, NER, ND, NUS, NDS, NM, and TN. Table I showsthe descriptive statistics of these variables and performancetime by the three conditions. Using the SAS’s generalizedlinear models (GLM) procedure [39], an analysis of variance(ANOVA) was performed to analyze the effects of the threeconditions on each of these variables.

The effect of the three conditions on the subjects’ per-formance time was significant .A Duncan’s multiple comparison test [39] revealed that theperformance time of the TE condition was significantly longerthan the DVR and CVR conditions, whereas the differencein the performance time between the DVR condition and theCVR condition was not significant.

There were significant effects of the three conditions onNER , ND

, NDS , and TN

. A Duncan’s test on NER revealed that thesubjects’ assembly sequences in the TE condition involvedsignificantly more reorientations than in the DVR condition,and that differences for other comparisons (TE versus CVR,CVR versus DVR) were not significant. A Duncan’s test onND revealed that the number of difficult steps in the TEcondition was significantly greater than the number of difficultsteps in the DVR condition, and that the number of difficultsteps in the DVR condition also was significantly greater thanthe number of difficult steps in the CVR condition. A Duncan’stest on NDS revealed that the number of dissimilar steps inthe TE condition was significantly greater than the number ofdissimilar steps in the CVR condition, and that the number ofdissimilar steps in the CVR condition was significantly greaterthan the number of dissimilar steps in the DVR condition.A Duncan’s test on TN revealed that the total number ofproblematic steps in the TE condition was significantly greaterthan the number of problematic steps in the CVR condition,and that differences for other comparisons (TE versus DVR,DVR versus DVR) were not significant.

The effects of the three conditions on NIF, NUS, and NMwere not significantly different. They were

for NIF, for NUS, andfor NM.

B. Subjective Ratings

Three subjective rating values were collected for eachsubject directly from the post-task questionnaire.

RU Rating for assembly understanding.RF Rating for meeting the feasibility criterion.RG Rating for meeting the goodness criteria.TR Total rating, which was the sum of RU, RF, and RG.

A lower rating value indicated a better understanding ofthe assembly or less difficulty in meeting the feasibility orgoodness criteria. Table I shows the descriptive statistics ofthe subjective ratings. ANOVA’s for the effects of the threeconditions on RU, RF, RG, and TR revealed no significanteffects for RU,

for RF, for RG, andfor TR). Based on the subjective evaluation

of the three conditions, the subjects did not significantly favorone condition over another.

IV. DISCUSSIONS

Based on the above results, it became apparent that thesubjects in the TE condition performed as well as the subjects


in the DVR and CVR conditions in meeting the feasibilitycriterion. The difference between the TE environment andthe two VR environments was found mainly in meetinggoodness criteria, which contributed to the overall performancedifferences as reflected in the effects of the three conditions onperformance time and TN. Among the various goodness crite-ria, criteria of reorientation, handling difficulty, and similaritybenefited significantly from using the two VR environmentsfor assembly planning. The CVR condition outperformedthe DVR condition on the criterion of handling difficulty,whereas the DVR condition outperformed the CVR conditionon the similarity criterion. There was no significant differencebetween the CVR condition and the DVR condition on thecriterion of excessive reorientation. Overall, the performancedifference between the DVR environment and the CVR envi-ronment was not significant, as reflected in the performancetime and the total number of problematic steps TN.

An examination of the subjects’ assembly sequences fromthe TE condition revealed some reasons underlying the sub-jects’ inferior performance in the TE condition. A commonproblem among the subjects in the TE condition was that allthe subjects used a stack-up method to generate their assemblysequence from the bottom plate (part V) to the top plate (partK). This stack-up order seemed relevant to the way in whichthe parts were presented in the top assembly drawing (seeFig. 1). Since this drawing included the names and labels ofall the parts, it is likely that it was used by the subjects asthe main reference document while generating their assemblysequence.

The parts in this drawing appeared stacked up on thebottom plate (part V). However, the bottom plate should notbe used as the supporting base of the assembly operations,because one end of the main shaft (part O) went througha hole in the bottom plate, making the assembly unstablewhen standing on the bottom plate. This design detail wasillustrated in other drawings of the assembly in the compositemode. Although the subjects were provided with differentdrawings for different perspectives of the assembly and wereallowed to view each drawing from any angle if necessary,their assembly planning appeared to be dominated by theperspective and angle from which the assembly was illustratedin the top assembly drawing. The subjects failed to recognizethe symmetric structure of the assembly. Moreover, somesubjects failed to recognize the position of the hole on theconnector holder (part E) where the plug (part B) was inserted.This position was not obvious in the top assembly drawing, butbecame apparent from the assembly drawings in a compositemode and the part drawing of the connector holder.

The stack-up method caused a number of problems with thesubjects’ assembly sequences. First of all, the installation ofthe aluminum cylinder (part U) onto the bottom plate beforeother parts (e.g., inner locknut, pressure plate, and pressureseal) made it difficult to install those other parts that wentinside the aluminum cylinder. Secondly, the installation ofinner lockout, pressure plate, pressure seal, and center plate(parts T, S, R, Q) in a stacked-up order made it infeasible tolock the main shaft onto the inner locknut. Because the mainshaft went through the bottom plate, a reorientation of the

already stacked-up subassembly (including the bottom plate,aluminum cylinder, inner locknut, pressure plate, pressureseal, and center plate) was required when installing the mainshaft through the already stacked-up subassembly. Finally,the stack-up method also caused the application of differentassembly operations to the similar groups of parts on bothsides of the center plate.

The stack-up problem in the TE condition might be at-tributed to the lack of a coherent view of the assembly. Inthe TE condition, different perspectives of the assembly werepresented in different drawings on separate sheets. Hence,in order to obtain a comprehensive understanding of theassembly, subjects might have to construct a mental model ofthe assembly, including all features from disjoint, partial viewsof the assembly. It also might be difficult for the subjects tocheck the correctness of their mental model of the assembly.In contrast, in the DVR and CVR conditions, the physicalmodel of the assembly was presented for the subjects toexplore features all in one place. The smooth transitions amongdifferent perspectives in the two VR conditions might providethe subjects with an advantage in understanding the assembly.The manipulation of the physical model of the assembly alsomight give the subjects in the two VR environments an easyopportunity to check the correctness of their assembly under-standing. These advantages of the VR environments might helpthe subjects in discovering the symmetrical structure and otherfeatures of the assembly, which might in turn improve theirassembly planning. Even if there were errors in the subjects’understanding of the assembly, the simulation of the assemblysequence in the VR environments might help the subjectsin recognizing those errors when difficulty was encounteredduring the simulation. However, in the TE condition, thesubjects had to carry out the understanding of the assembly andthe verification of their assembly sequence mentally withoutthe assistance of the physical model of the assembly and thephysical simulation of the assembly sequence. Therefore, thephysical model of the assembly and the simulation of theassembly sequence in the VR environments might help inreducing the mental workload of the subjects in assemblyplanning, thus leading to less errors in assembly planning.

Based on the insignificant effects of the three conditions ofNIF, NUS, and NM, we found that the two VR environmentsdid not produce improvements over the TE environment inreducing the number of infeasible assembly operations, thenumber of unstable assembly operations, and the number ofmissing parts.

With respect to the insignificant effect of NUS, an exam-ination of the subjects’ assembly sequences from the threeconditions revealed that the subjects attempted to manipulatemultiple parts at the same time instead of installing parts oneby one. For example, pressure plate and pressure seal (partsS and R or parts M and N) were often put together into asubassembly by the subjects, and then the subassembly (in aloose state) was placed on the center plate. The subassemblyin a loose state was unstable, which created difficulty inperforming the assembly operation of placing them on thecenter plate. Although the CVR environment provided thesubjects with a more realistic visualization of the assembly


and its parts, the CVR environment still differed from the realworld in many other aspects. For example, gravity exists in thereal world, whereas in the CVR environment of this study, thesubjects could not feel the effect of gravity and part weight. Aprovision of gravity and part weight in the CVR environmentmight help the subjects in meeting the stability criterion.

The subjects’ performance in meeting the feasibility cri-terion might be improved by incorporating a rigid collisiondetection into the VR environments to reveal part collisionsand part trappings during the simulation of an assemblysequence. Mechanisms (e.g., an indexing system) must bedeveloped to prevent the subjects from missing parts in anassembly sequence.

In conclusion, the results of this study revealed certainadvantages of the VR environments over the traditional engi-neering environment in supporting assembly planning. Espe-cially a coherent, flexible visualization and manipulation of theassembly and its parts in one place on the computer producedimprovements of the subjects’ overall performance and inmeeting some goodness criteria such as handling difficulty,excessive reorientation, and similarity. Although the enhancedvisualization and simulation are necessary to support assemblyplanning, other factors such as gravity, part weight, flexible-shape parts, and irregular geometry also may be helpful inbringing the subjects’ assembly planning performance in theVR environments to a higher level. The immersive CVRenvironment is a more natural place to incorporate thesefactors than the nonimmersive DVR environment. When thesefactors are incorporated into the CVR environment, we maybe able to observe the more dramatic advantages of theCVR environment in supporting assembly planning. In thisstudy, the differences between the CVR environment andthe DVR environment were not significant with respect tothe performance time and the total number of problematicsteps, despite the fact that one was better than the other forone goodness measure but was worse for another goodnessmeasure.

Considering the advantages of the VR environments inaddressing the goodness aspect of assembly planning, theVR environments could complement the more algorithmicapproaches by presenting feasible assembly steps from thoseapproaches to engineers for evaluation and selection based ongoodness criteria.

It should be noted that the results of this study were obtainedfrom an experiment using a specific assembly. The natureof a controlled experiment prevents us from exhausting allkinds of assembly in a controlled experiment. This may placesome limitation on generalizing the results of this study.For example, the assembly for this study had a stackingstructure, which in turn determined the assembly sequencein a linear manner. This assembly planning task did notinvolve some other issues relating to fragile parts, flexibleparts, irregular geometry, fixtures, etc. Further study can becarried out to classify features of assembly structure andoperation, and to conduct experiments using two independentvariables: one for different assembly features and anotherfor different environments. From this study, we cannot drawconclusions on how VR advantages are related to assembly

features. Nevertheless, the assembly for this study is a realproduct. It has a lot in common with many real-world assemblyproducts in assembly structure and operation. Hence, theresults of this study on this specific assembly should promotethe consideration of using VR environments to support thegoodness aspect of assembly planning.

It also should be noted that almost all of the subjects forthis study had no experience in assembly planning prior to theexperiment in order to provide an unbiased comparison of theenvironments studied. However, in practice, engineers oftenwork on similar assembly products, and thus possess consider-able knowledge of assembly products and their assembly plan.In such cases, the advantages of the VR environments withrespect to the TE environment may be reduced considerablydue to the engineers’ familiarity with assembly products.Further study is needed to investigate how VR environmentscould complement the knowledge and expertise of engineersin reducing cognitive overload of engineers to produce feasibleand good assembly plans efficiently.

ACKNOWLEDGMENT

The authors would like to thank N. Ahmed for running apart of this experiment and S. Anantharaman for developingthe assembly and part drawings in ProEngineer. We also wouldlike to thank the anonymous reviewers for their constructiveand helpful comments.

REFERENCES

[1] M. P. Groover and E. W. Zimmers,CAD/CAM: Computer-Aided Designand Manufacturing. Englewood Cliffs, NJ: Prentice-Hall, 1984.

[2] U. Rembold, B. O. Nnaji, and A. Storr,Computer Integrated Manufac-turing and Engineering. Wokingham, U.K.: Addison-Wesley, 1993.

[3] G. Salvendy,Handbook of Industrial Engineering. New York: Wiley,1982.

[4] N. Singh, Systems Approach to Computer-Integrated Design and Man-ufacturing. New York: Wiley, 1996.

[5] I. Zeid, CAD/CAM Theory and Practice. New York: McGraw-Hill,1991.

[6] S. Chakrabarty and J. Wolter, “A structure-oriented approach to as-sembly sequence planning,”IEEE Trans. Robot. Automat., vol. 13, pp.14–29, Feb. 1997.

[7] A. Delchambre,Computer-Aided Assembly Planning. London, U.K.:Chapman & Hall, 1992.

[8] L. S. H. de Mello and A. C. Sanderson, “Two criteria for the selection ofassembly plans: Maximizing the flexibility of sequencing the assemblytasks and minimizing the assembly time through parallel execution ofassembly tasks,”IEEE Trans. Robot. Automat., vol. 7, pp. 626–633,Oct. 1991.

[9] L. Laperriere and H. A. ElMaraghy, “GAPP: A generative assemblyprocess planner,”J. Manuf. Syst., vol. 15, no. 4, pp. 282–293, 1996.

[10] K. T. Seow and R. Devanathan, “A temporal framework for assemblysequence representation and analysis,”IEEE Trans. Robot. Automat.,vol. 10, pp. 220–229, Apr. 1994.

[11] N. Ye and D. A. Urzi, “Heuristic rules and strategies of assembly plan-ning: Experiment and implications in the design of assembly decisionsupport system,”Int. J. Prod. Res., vol. 34, no. 8, pp. 2211–2228, 1996.

[12] U. A. Seidel, “MI system for assembly sequence planning: Humanfactors consideration,” inDesign for Manufacturability, M. Helanderand M. Magamachi, Eds. London, U.K.: Taylor & Francis, 1992.

[13] T. Cao and A. C. Sanderson, “Task decomposition and analysis ofrobotic assembly task plans using Petri nets,”IEEE Trans. Ind. Electron.,vol. 41, no. 6 pp. 620–630, Dec. 1994.

[14] C. L. P. Chen, “Automatic assembly sequences generation by pattern-matching,”IEEE Trans. Syst., Man, Cybern., vol. 21, pp. 376–389, Apr.1991.

[15] C. L. Chen and C. A. Wichman, “A systematic approach for designand planning of mechanical assemblies,”AI EDAM, vol. 7, no. 1, pp.19–36, 1993.


[16] L. S. H. de Mello and A. Sanderson, “A correct and complete algorithmfor the generation of mechanical assembly sequences,”IEEE Trans.Robot. Automat., vol. 7, pp. 228–240, Feb. 1991.

[17] Y. F. Huang and C. S. G. Lee, “A framework of knowledge-basedassembly planning,” inProc. IEEE Int. Conf. Robotics and Automation,1991, pp. 599–604.

[18] J. L. Hwang and M. Henderson, “Applying the perception to 3-D featurerecognition,” in Proc. 1992 NSF Design and Manufacturing GranteesConf., pp. 703–707.

[19] E. Knoll, “Spatial reasoning in assembly planning,” inProc. 1992 NSFDesign and Manufacturing Grantees Conf., 1089–1097.

[20] E. Knoll and R. Mohammad, “Modeling and reasoning for assemblyplanning,” in Proc. 1992 NSF Design and Manufacturing GranteesConf., pp. 721–725.

[21] , “Spatial reasoning about three-dimensional mechanical assem-blies,” in Proc. 1994 NSF Design and Manufacturing Grantees Conf.,pp. 73–74.

[22] R. Mohammad and E. Knoll, “Automatic generation of exploded viewsby graph transformation,” inProc. 1993 NSF Design and ManufacturingGrantees Conf., pp. 1425–1426.

[23] S. Lee, “Backward assembly planning,” inArtificial Intelligence Appli-cations in Manufacturing, A. Famili, D. S. Nau, and S. H. Kim, Eds.Menlo Park, CA: AAAI Press, 1992, pp. 61–102.

[24] A. C. Lin and T.-C. Chang, “3-D MAPS: Three-dimensional mechanicalassembly planning system,”J. Manuf. Syst., vol. 12, no. 6, pp. 437–456,1993.

[25] H. C. Liu and B. O. Nnaji, “PAM: A product modeler for assembly,” inProc. 1994 NSF Design and Manufacturing Grantees Conf., pp. 69–70.

[26] Y. Liu and R. Popplestone, “Symmetry groups in solid model-basedassembly planning,” inArtificial Intelligence Applications in Manufac-turing, A. Famili, D. S. Nau, and S. H. Kim, Eds. Menlo Park, CA:AAAI Press, 1992, pp. 61–102.

[27] J. Wolter, “Automatic generation of assembly plans,” inProc. IEEE Int.Conf. Robotics and Automation, 1989, pp. 62–68.

[28] J. Wolter, S. Chakrabarty, and J. Tsao, “Methods of knowledge rep-resentation for assembly planning,” inProc. 1992 NSF Design andManufacturing Grantees Conf., pp. 463–468.

[29] R. D. Caldwell, N. Ye, and D. A. Urzi, “Re-engineering the productdevelopment cycle and future enhancements of computer-integratedmanufacturing environment,”Int. J. Comput. Integrated Manuf., vol. 8,no. 6, pp. 441–447, 1995.

[30] D. F. Baldwin, T. E. Abell, M.-C. M. Lui, T. L. DeFazio, and D. E.Whitney, “An integrated computer aid for generating and evaluatingassembly sequences for mechanical products,”IEEE Trans. Robot.Automat., vol. 7, pp. 78–94, Feb. 1991.

[31] S. Leong and C. McLean, “Application of simulation tools in manufac-turing engineering,” NIST Document, Manufact. Syst. Integrated Div.,Gaithersburg, MD, 1996.

[32] S. Ressler, “Applying virtual environment to manufacturing,” NISTDocument 5343, Manufact. Syst. Integrated Div., Gaithersburg, MD,1994.

[33] R. D. Wilson, “Minimizing user queries in interactive assembly plan-ning,” IEEE Trans. Robot. Automat., vol. 11, pp. 308–312, Apr. 1995.

[34] U. Roy, P. Banerjee, and C. R. Liu, “Design of an automated assemblyenvironment,”Comput.-Aided Des., vol. 21, no. 9, pp. 561–569.

[35] W. Barfield and R. Robless, “The effects of two- or three-dimensionalgraphics on the problem-solving performance of experienced and novicedecision makers,”Behavior Inform. Technol., vol. 8, pp. 367–385, 1989.

[36] J. E. Fowler and M. E. Luce, “Systems integration for manufactur-ing applications program 1995 annual report,” NIST Document 5839,Manufact. Syst. Integrated Div., Gaithersburg, MD, 1996.

[37] J. Hartman and J. Warnecke, 1996.The VRML 2.0 Handbook. Reading,MA: Addison-Wesley, 1996.

[38] D. Pape, C. Cruz-Neira, and M. Czernuszenko, 1997, “CAVE User’sGuide,” Elect. Visualization Lab., Univ. Illinois, Chicago.

[39] SAS Institute Inc.,SAS/STAT User’s Guide, Version 6, 4th ed. Cary,NC: SAS Institute, 1990.

Nong Ye was born in Beijing, China, in 1964.She received the B.S. degree in computer sciencefrom Peking University, Beijing, the M.S. degreein computer science from the Chinese Academyof Sciences, Beijing, and the Ph.D. degree in in-dustrial engineering from Purdue University, WestLafayette, IN.

Since 1998, she has been an Associate Professorwith the Department of Industrial Engineering, Ari-zona State University, Tempe. From 1994 to 1998,she was an Assistant Professor with the University

of Illinois, Chicago. From 1991 to 1994, she was an Assistant Professor withWright State University, Dayton, OH. Her research interests are in informationtechnology, system engineering and its support to engineering design andintegration, distributed virtual enterprises, and information security.

Dr. Ye is a senior member of the Institute of Industrial Engineers. She serveson the editorial boards of theInternational Journal of Human–ComputerInteraction and theInternational Journal of Cognitive Ergonomics.

Prashant Banerjee received the B.Tech degreefrom the Indian Institute of Technology, Kanpur, In-dia and the M.S. and Ph.D. degrees in industrial en-gineering from Purdue University, West Lafayette,IN.

He is currently an Associate Professor in the De-partment of Mechanical Engineering, University ofIllinois, Chicago, while also serving as the Directorof the Industrial Virtual Reality Institute (IVRI), ajoint research and development operation comprisedof the University of Illinois, Chicago, Northwestern

University, Chicago, IL, and Argonne National Laboratory, Argonne, IL. Hiscurrent research interests include virtual reality-based factory design, partdesign and assembly design models, immersive display interfaces, and linearand nonlinear design optimization models. His research has been supportedby NSF, NIST, and ONR and by companies such as Caterpillar, Searle, andMotorola,.

Dr. Banerjee is currently serving as Department Editor ofIEE Transac-tions and as an Associate Editor of IEEE TRANSACTIONS ON ROBOTICS AND

AUTOMATION.

Amarnath Banerjee received the M.Sc.(Tech) de-gree in computer science from the Birla Institute ofTechnology and Science, Pilani, India. He currentlyis pursuing the Ph.D. degree in Industrial Engineer-ing in the Department of Mechanical Engineering,University of Illinois, Chicago.

His research interests include telecollaboration invirtual environments, simulation of object behaviorin virtual reality, and task integration.

Fred Dech, photograph and biography not available at the time of publication.

a comparative study of assembly planning in traditional and virtual environments

Documents