A virtual reality-based experimentation environment for the verification of human-related factors in assembly processes

*Corresponding author. Tel.: #30-61-997-262; fax: #30-61-997-744.

E-mail address: [email protected] (G. Chryssolouris).

Robotics and Computer Integrated Manufacturing 16 (2000) 267}276

A virtual reality-based experimentation environmentfor the veri"cation of human-related factors in assembly processes

George Chryssolouris*, Dimitris Mavrikios, Dimitris Fragos, Vassiliki KarabatsouDepartment of Mechanical Engineering & Aeronautics, Laboratory for Manufacturing Systems, University of Patras, Rio, Patras 26110, Greece

Received 3 April 2000; accepted 24 April 2000

Abstract

This paper investigates the use of virtual reality (VR)-based methods for the veri"cation of performance factors related to manualassembly processes. An immersive and interactive virtual environment has been created to provide functionality for realistic processexperimentation. Ergonomic models and functions have been embedded into the VR environment to support veri"cation andconstrain experimentation to ergonomically acceptable conditions. A speci"c assembly test case is presented, for which a semi-empirical time model is developed employing statistical design experimentation in the virtual environment. The virtual experimenta-tion results enable the quanti"cation and prediction of the in#uence of a number of process parameters and their combination at theprocess cycle time. ( 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Virtual reality; Virtual assembly; Ergonomics; Process modelling

1. Introduction

Manufacturing industries aim strategically at reducingproduct development cycle times and cost [1]. A majorobstacle in achieving this objective is the need to buildseveral physical prototypes for the veri"cation of humanrelated factors in the design of an assembly operation. Incomplex manual tasks, human involvement is very criti-cal as it in#uences the feasibility, the cycle time, theworking comfort and safety of an operation. Neverthe-less, building of physical prototypes increases develop-ment cycle times and cost. Thus, there has been a strongneed for integrating human factors in the design andveri"cation of industrial processes using advanced simu-lation techniques.

The main approach used so far to simulate the humanoperator and verify the performance aspects of manualoperations is the computer employed desktop 3D simula-tion techniques. These techniques replace human oper-ator with an anthropometrical articulated representationof a human being, called `mannequina [2]. A number ofergonomic simulation tools have been developed using

this approach [3]. JACK by Transom Technologies Inc.enables designers to de"ne, manipulate, animate, andperform human factor analysis of physical tasks ina working environment. SAFEWORK by Safework Inc.,employs mannequin for workplace design and ergonomicanalysis, enabling performance of postural analysis,reach and access studies and vision analysis. ROB-CAD/MAN by Tecnomatix Technologies provides six bi-omechanical human models for the simulation andanalysis of manual activities at a workstation or assem-bly line. ERGOMAS by MTM Association for Standardsand Research provides the ability to animate humanmotions, in order to evaluate workstations in terms ofvisibility, reachability and handlability. ENVISION/ERGO by Deneb Robotics is a simulation-based ergo-nomics tool for workplace analysis and assembly simula-tion, which enables engineers to prototype motionsequences for generic processes. MDHMS by McDonnellDouglas Aerospace, links biomechanical human modelsto design geometry, providing functionality to assembleor maintain virtual models of equipment.

The use of mannequins for ergonomic analysis ofmanufacturing processes cannot though provide anadequately accurate human simulation. This is due to thefact that the mannequins are programmed to `movea andàcta in standard ways. Thus, this approach can neithermimic the intuitive natural motions of the operator, nor

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take into account the randomness of the "ttingpaths during process execution. The result could be amisleading veri"cation of the process feasibility, oran inaccurate cycle time estimation. Moreover, thegeneration of human tasks using the mannequins'technology is quite complex. It involves programming ofmovements and actions by de"ning a large number ofprocess and biomechanical parameters and constraints.Thus, the full exploitation of this technology requiresexpert users.

Virtual reality (VR) techniques have been alsoexplored during the last few years for industrial processesveri"cation. As a result a number of VR-based systemsoriented to assembly processes simulation have beenpresented in the scienti"c literature [4}7]. The objectiveof the IVY system has been to allow designers to exploitinherent knowledge of assembly factors within an inter-active virtual environment, in order to evaluate and vis-ualise candidate assembly plans. Similarly, VADE hasbeen developed to demonstrate the feasibility of creatingvalued design information, such as tolerancing, optimalcomponent sequencing and assembly/disassembly pro-cess plans, using VR tools. The VEDA simulation modelenables estimation of ease of part handling and partinsertion in assembly processes providing multi-modalsimulation through visual, auditory, and haptic inter-faces. VEDAM, another relevant system, provides aninteractive virtual environment for design and manufac-turing applications, which directly links to CAD/CAMsystems.

Nevertheless, these demonstrators seem to be ratherpreliminary work, as they do not provide yet clear andjusti"able results for quantitative and unambiguousmeasurements of process characteristics. Support for hu-man factors veri"cation is limited to qualitative estima-tions of performance characteristics, without provision ofany functionality for quantitative analysis.

2. The virtual assembly work cell

Within the concept of providing a novel virtual envi-ronment with functionalities applicable to real industrialpractise, a virtual assembly work cell has been developed[8]. Its aim has been to support process experimentationconcerning factors, which cannot be described analyti-cally, and therefore do not a!ect the process in a prede-termined way. It is a VR based environment for theveri"cation of subjective or random aspects of humaninvolvement. More speci"cally, it enables

f the qualitative evaluation of human-process-relatedfactors, such as the perception of the working environ-ment, the interface with the work cell layout, thereachability of mounting locations, and the handlabil-ity of product components and tools, and

f the quantitative estimation of human-process charac-teristics related to critical performance issues, such asthe lifting capacity, the energy expenditure and themanual task cycle time.

Using the virtual assembly work cell, process designerscan carry out in a rapid, cost-less and safe way virtualexperiments for assembly processes. These experimentscan be used to identify the feasibility of the process interms of the ergonomic aspects of human involvement.Alternatively, they enable designers to optimise theperformance of a manual task. Moreover, the experi-ments can be used to build experimental models forinterrelating critical human-process aspects in thevirtual world.

The novel aspect of this environment lies in the use ofan approach to the simulation of assembly processes,based on immersive and interactive virtual reality tech-niques. This approach enables the integration of the realhuman/worker within the simulated assembly layout.Thus, a more realistic interaction, taking into accountboth the intuitive and random character of human move-ments, is possible within this layout. Moreover, in orderto account for the need of quantitative estimations, func-tions currently embedded only to mannequins' techno-logy applications, have been adapted for use within theimmersive environment. Using appropriate display andinteraction VR peripherals, the user can immerse in avirtual assembly work cell of `real-worlda scale. In thisenvironment, the simulation follows the intuitive way ofworking that an operator uses in the assembly process.Components can be picked from transfer lines or storagelocations, moved in space and placed in the respective"tting location onto the product sub-assembly. The usercan also operate virtual "xing tools and joint elements to"x the component in place if required. The simulationenvironment provides realistic interaction capabilitiesand supporting functions to assist the process perfor-mance or simplify complex process aspects. Moreover,quanti"ed data are generated to provide an enhancedoverview on the process ergonomics. The simulationconcept of the virtual assembly work cell environment isshown in Fig. 1.

2.1. Process functionalities

The simulation environment enables the user toìmporta all required model data and prepare easily thework cell layout according to the speci"c needs of theprocess to be simulated. Models of jigs, "xtures, work-benches and conveyors are imported and placed atthe desired locations. The product subassembly, thepart(s) to be assembled, as well as the required tools andjoint elements are then loaded and located withinthe work cell layout. This procedure is carried out indesktop mode.

268 G. Chryssolouris et al. / Robotics and Computer Integrated Manufacturing 16 (2000) 267}276

Fig. 1. The simulation concept of the virtual assembly work cell.

By shifting to the immersive mode, a minimum ofparameters, speci"c to the physical characteristics of theuser, are either directly recorded, such as the height, orde"ned through the appropriate interface, such as theweight, the sex and the strength level. Apart from thephysical interaction, these parameters are the only input,which is required from the user for the simulation to becarried out. The user can then start the process, which fora single assembly task includes the steps presented inFig. 1. A number of process speci"c functions supportthis operation within the virtual assembly work cellenvironment:

f Interactive functions: Advanced gesture recognitionfeatures have been developed in order to provide alter-native realistic capabilities for manipulation of virtualobjects, according to their shape and functionality. Inorder to enhance realism, both hands may be used forlifting and other complex tasks.

f Magnet function: A function has been developed torelease the part to be assembled from the user's hand(the magnet function), as soon as a good positional androtational orientation has been achieved. This orienta-tion is very close to the exact "nal mounting position.The function facilitates the "tting procedure, takingunder consideration the limitations for highly accurate

manipulation encountered in virtual environments.The "eld of the `magneta can be adjusted to accountfor various levels of "tting precision.

f Collision detection: Dynamic clash detection isprovided within the simulation environment betweenstatic parts and either moving parts or the user'shands. In this way, visual and acoustic alerts enablethe user to verify the feasibility of the process, in termsof the reachability of picking and mounting locationsand the handlability of parts.

f Acceleration recording: In order to account for the lackof the sense of an object's weight within the virtualenvironment, the system records in real time the objectmotion's acceleration and provides an estimation ofthe corresponding moving force in the real world.According to the selected level of the user's strength,limits are imposed to the maximum moving force thatmay be virtually àpplieda by the user. Colour and/orsound e!ects provide warnings in case moving forceexceeds the limit of the corresponding strength level.

2.2. Quantitative analysis capabilities

In order to account for the required simulation output,special functions and models have been embedded to thevirtual assembly work cell providing accurate processdata. In this concept, a functional timer enables the userto record process time data. During a process executionby the user, the "tting time t

5, namely the time required to

manipulate the component in order to place it in the"tting location, and the idle time t

*, namely the time

required to pick parts and handle tools and joint ele-ments, are recorded in real time. Standard time data areapplied according to the type of the "xing procedure, inorder to take under consideration the "xing time t

&of the

component. Thus, the total process time ¹ is estimatedusing

¹"t5#t

*#t

&. (1)

Two speci"c ergonomic models [9] for recommendedweight limit (NIOSH lifting equation) and energy expen-diture (GARG equations) have been adapted for usewithin an immersive and interactive environment, andembedded in the virtual assembly work cell (Table 1).

For the estimation of task variables (Table 2) and theidenti"cation of the lift type (Table 3), the position andorientation co-ordinates of the subject's hands and headare employed. The co-ordinates are recorded in real timeby respective trackers (Fig. 2). Due to the limited numberof the trackers used, the validity of the co-ordinatesrecording, with reference to the ergonomic models re-quirements, is subject to the assumption that the headand trunk of the user are rotated as if they were a singlebody part. Besides the estimation of the ergonomic para-meters, the system alerts the user for problematic values,taking into account the recommendations of the models.

G. Chryssolouris et al. / Robotics and Computer Integrated Manufacturing 16 (2000) 267}276 269

Table 1The ergonomic models used in the virtual assembly work cell

NIOSH equation GARG equations

RWL"LC )HM )VM )DM )AM )FM )CM For stoop lift:where E"0.0109 BW#(0.0012 BW#0.0052¸#0.0028 S )¸)F

RWL: recommended weight limit (kg) For squat lift:LC: load constant"23 kg E"0.0109 BW#(0.0019 BW#0.0081¸#0.0023 S )¸)FHM: horizontal multiplier For arm lift:VM: vertical multiplier E"0.0109 BW#(0.0002 BW#0.0103¸!0.0017 S )¸)FDM: distance multiplier whereAM: asymmetric multiplier E: energy expenditure (kcal/min)FM: frequency multiplier BW: body weight (lb)CM: coupling multiplier S: worker's sex (female"0, male"1)

F: lifting frequency (lifts/min)LI"¸/RWL L: load weight (lb)where

LI: lifting indexL: load weight (kg)

Table 2Estimation of NIOSH task variables within the immersive environment

NIOSH equation task variables

H"DC]h]sin /D#J(zHA

!zHE

)2#(xHA

!xHE

)2 whereH: horizontal location (cm)

<"yHA

V: vertical location (cm)A: asymmetric angle (degrees)

A"0 F: lifting frequency (lifts/min)h: user's height

F"60/¹ C: constant ratio of distance between head and middle of human bodyand height+0.4666/: inclination angle of worker's trunk (degrees)zHA

: z co-ordinate of the hand tracker (cm)zHE

: z co-ordinate of the head tracker (cm)zF: z co-ordinate of the mid-point between inner angle bones (cm)

xHA

: x co-ordinate of the hand tracker (cm)xHE

: x co-ordinate of the head tracker (cm)xF: x co-ordinate of the mid-point between inner angle bones (cm)

yHA

: y co-ordinates of the hand tracker (cm)"VyHE

: y co-ordinates of the head tracker (cm)yF: y co-ordinate of the mid-point between inner angle bones (cm)"0

T: total process time (s)

2.3. System conxguration

The virtual assembly work cell has been developedupon a commercial application platform (dVise V.6/Division Ltd.) for virtual environments, using C## inIRIX 6.5 operating system. The system currently runs ona Silicon Graphics ONYX2 Workstation with an In"niteReality 2 Graphics Pipeline. The VR peripherals used tosupport the immersive and interactive experimentationwith the virtual assembly work cell include a VirtualResearch FS5 helmet, a Virtual Technologies 18 sensorCyberGlove with CyberTouch and GesturePlus, a Divis-

ion 3D Mouse, and a Polhemus Fastrack trackingsystem.

3. Test case } experimentation

In order to demonstrate the capabilities of the virtualassembly work cell to support experimentation in thecontext of process design, a test case for a speci"c assem-bly process has been carried out. The test case refers tothe assembly of a high-speed boat propeller. The processinvolves picking the propeller from a storage bench,


Table 3Identi"cation of GARG equations lift type within the immersive envi-ronment

GARG equations lift type

IF angle /'103PStoop liftELSE IF (user height!y

HE)'10 cmPSquat lift

ELSEPArm lift

Fig. 2. Recording of the horizontal (H) and vertical (V) locations.

moving it in space, mounting it onto a subassembly of theboat's machine, and "xing it using a nut and an airwrench. The objective has been to employ a statisticaldesign of experiments approach in order to

f quantify and predict the in#uence of a number ofprocess parameters and their combination at theprocess cycle time, and

f develop a process speci"c semi-empirical time model,

using realistic experimentation carried out only withinthe virtual environment. The applied methodology isbased on matrix experiments using orthogonal arrays[10].

The performance measure for the experiments hasbeen the total process time ¹. The major factors, whichhave been considered in#uencing the performancemeasure in the speci"c process, are the subject strength(SS), the work cell layout (WCL), the pick-up structureheight (PSH) and the mounting structure height (MSH).These factors and their chosen levels are listed in Table 4.The levels of the factor SS are qualitative estimations ofthe subject's strength according to the corresponding sex

and build. The levels of the factor WCL refer to alterna-tive layout designs involving di!erent relevant locationsof the pick-up and mounting structures, the toolingworkbench and the subject (Fig. 3). The levels of thefactors PSH and MSH are the chosen quantitative valuesfor the possible heights of the pick-up and mountingstructures respectively. The orthogonal array selected forthe experimentation is the ¸@

16(45). Following techniques

suggested in [10], the "fth column of the array is neglect-ed, while Level 4 for the factors SS, PSH and MSH issubstituted by Level 2. The resulting array used for theexperimentation is presented in Table 5.

During the performance of each experiment, themanual tasks have been carried out using naturalintuitive movements, limited only by tracking systemcapabilities. Variations of posture characteristics, such asthe lift type, and critical handling distances have beenrecorded in real-time by the system, in order to be usedfor the estimation of the recommended weight limit andthe energy expenditure. The ergonomic parameters havebeen used as constraints in order to assure that allthe experimental values of the performance measuretaken into account correspond to ergonomically accept-able ways of working. Several snapshots during theprocess performance are shown in Figs. 4}7.

4. Results

Based on the timing functions, experimental resultshave been produced for the performance measure of theexperimentation, namely the total process time ¹.Moreover, based on the trackers records, the necessary


Table 4Process factors and their levels

Factor Levels

1 2 3 4

Subject strength (SS) Low Medium High }

(female, 160 cm height,58 kg weight)

(male, 175 cm height,68 kg weight)

(male, 184 cm height,72 kg weight)

}

Work cell layout (WCL) Layout 1 Layout 2 Layout 3 Layout 4(Fig. 3) (Fig. 3) (Fig. 3) (Fig. 3)

Pick-up structure height (PSH) 115 cm 110 cm 90 cm }

Mounting structure height (MSH) 80 cm 100 cm 120 cm }

Fig. 3. The alternative layout designs employed in the experimentation.

Table 5

Factor levels for each experiment

Expt. no. Levels of factors

SS WCL PSH MSH

1 1 1 1 12 1 2 2 23 1 3 3 34 1 4 4 45 2 1 2 36 2 2 1 47 2 3 4 18 2 4 3 29 3 1 3 4

10 3 2 4 311 3 3 1 212 3 4 2 113 4 1 4 214 4 2 3 115 4 3 2 416 4 4 1 3

Fig. 4. A process layout.

estimations have been carried out for the ergonomicconstraints of the experimentation, namely the lifting andenergy checks. The experimental results are presented inTable 6. Using the experimental results, along with the


Fig. 5. Fitting the propeller.

Fig. 6. Fixing a nut.

Fig. 7. Input and output data.

necessary calculations, an analysis of means (ANOM)diagram has been created (Fig. 8). The diagram showsgraphically the average values of the performancemeasure for each level of the four experimentation fac-tors. From this diagram, the levels of the factors that givethe best process time ¹ may be easily identi"ed (Table 7).

In order to determine the relative importance of thefour process factors and quantify their e!ect on the pro-cess time ¹, an analysis of variance (ANOVA) has beencarried out. The results of the analysis in terms of thedegrees of freedom (DF), the sum of squares (S), the meansquare (MS) and the variance ratio (F) are presented inTable 8. The variance ratio provides a quanti"ed indica-tion about the e!ect of each factor on the performancemeasure. The percentage signi"cance of each factor isshown in Fig. 9.

The formation of an additive (superposition) model,approximating the relationship between the performancemeasure ¹ and the process factors SS, WCL, PSH andMSH, has been a further objective of the virtual experi-mentation. The model is of the following form [10]:

¹(SSi, WCL

j, PSH

k, MSH

l)

"k#ssI#wcl

j#psh

k#msh

l. (10)

In Eq. (10), k is the overall mean of the performancemeasure (65.3 s). The terms ss

i, wcl

j, psh

k, and msh

lrepresent the deviations from k caused by setting thefactors SS, WCL, PSH and MSH at levels SS

i, WCL

j,

PSHk, and MSH

l, respectively. These deviations, derived

from ANOM and being the coe$cient values for theadditive model, are provided in Table 9.

Three veri"cation experiments have been conductedunder the optimum conditions, in order to compare theobserved mean value of the process cycle time with theone predicted by the additive model. The results ofthe veri"cation procedure are presented in Table 10. Thepredicted value of the performance measure (¹

011) is

calculated using Eq. (10) and the coe$cient values ofTable 9 corresponding to the optimum factor levels(shaded values). The variance (s2

13%$) and the correspond-

ing two-standard deviation con"dence limits for the pre-diction error (PE

#-) are estimated as suggested in [10].

Following the performance of the veri"cation experi-ments, the observed mean value of the process cycle time(¹

010) has been used in order to estimate the prediction

error (PE).

5. Discussions

As demonstrated in the description of the test case, thesimulation functionalities of the virtual assembly workcell have provided the possibility to carry out the virtualexperimentation under the same conditions that wouldhave been taken under consideration in the real world.A working environment, human subjects, product com-ponents, jigs, workbenches, and tools have been em-ployed. The subjects have been trained to the use of theperipherals and the virtual environment prior to experi-mentation, in order to become familiar with the execu-tion of the virtual process. The process itself has been


Table 6Experimental results

Results Experiments

1 2 3 4 5 6 7 8

Fitting time (s) 20 15 14 17 12 18 19 14Fixing time (s) 3 3 3 3 3 3 3 3Idle time (s) 56 51 47 42 50 49 45 42Total time (s) 79 69 64 62 65 70 67 59

Lift typeOR

Arm Arm Arm Arm Arm Arm Arm SquatH

OR(cm) 36.9 48.4 45.4 49.5 33.8 53.9 35.1 54.9

<OR

(cm) 125.6 118.5 97.9 121.1 120.4 121.7 119.5 100.1RWL

OR(kg) 11.2 9.68 11 9.2 13.7 8.6 12.3 9

LIOR

0.66 0.77 0.68 0.81 0.54 0.87 0.61 0.83Lifting check

OROK OK OK OK OK OK OK OK

Lift typeDES

Squat Squat Arm Squat Arm Squat Squat SquatH

DES(cm) 43 33.8 37.8 32.4 44.4 47.3 34.9 37.8

<DES

(cm) 75.9 95 114.8 94.1 115.6 98.3 78.8 93.4RWL

DES(kg) 11.4 15 12.5 15.4 10.6 10.6 14.2 13.4

LIDES

0.66 0.50 0.60 0.48 0.70 0.71 0.53 0.56Lifting check

DESOK OK OK OK OK OK OK OK

Energy (kcal/min) 1.68 1.7 1.58 1.7 1.79 2 2.06 2.1Energy check OK OK OK OK OK OK OK OK

Results Experiments

9 10 11 12 13 14 15 16

Fitting time (s) 12 9 13 13 17 15 13 14Fixing time (s) 3 3 3 3 3 3 3 3Idle time (s) 56 43 45 41 51 58 46 40Total time (s) 71 55 61 57 71 76 62 57

Lift typeOR

Arm Arm Arm Arm Arm Squat Arm ArmH

OR(cm) 61.6 41.7 40 47.5 33.6 42.4 42.9 47.6

<OR

(cm) 93 111.9 116 117.2 120.2 99.5 118.6 122.5RWL

OR(kg) 8.2 11.5 11.8 9 13.8 11.7 10.9 9.7

LIOR

0.9 0.65 0.63 0.83 0.54 0.64 0.69 0.77Lifting check

OROK OK OK OK OK OK OK OK

Lift typeDES

Squat Squat Squat Squat Squat Squat Squat SquatH

DES(cm) 46.6 54.2 60.6 38.5 46.2 44.4 35 37.5

<DES

(cm) 93.8 111 93.8 69.6 96.5 78.3 95.2 117RWL

DES(kg) 10.9 8.8 8.4 12.6 10.9 12 14.4 12.5

LIDES

0.69 0.84 0.89 0.59 0.69 0.62 0.52 0.60Lifting check

DESOK OK OK OK OK OK OK OK

Energy (kcal/min) 2.14 2.2 2.2 2.2 2.03 2 2.1 2.1Energy check OK OK OK OK OK OK OK OK

carried out in the virtual environment in an immersiveand interactive way, approximating the physical andintuitive movements of an operator. During the experi-mentation, ergonomic constraints have been applied andquanti"ed cycle time data have been recorded. Thus,a good representation of real experimentation has beenachieved, enabling the ful"lment of its objectives in avirtual environment.

As far as the experimentation itself is concerned, theprocessing of the performance measure results has pro-

vided a clear view on the in#uence of the selected processparameters. In the ANOM diagram produced (Fig. 8),the behaviour of the performance measure according tothe levels of each factor can be observed. The cycle timedecreases upon an increase of the subject's strength, whileobjects' handling becomes easier and faster. The work celllayout 4 (Fig. 3) proves in practise to be the most timee$cient, enabling more smooth sequence of movements.High or low values for the pick-up structure height resultin increase of the cycle time. A mid-level value provides


Fig. 8. ANOM diagram.

Table 7Optimum factor values

Factor Level Value

Subject strength (SS) 3 HighWork cell layout (WCL) 4 Layout 4Pick-up structure height (PSH) 2 110Mounting structure height (MSH) 3 120

Table 8ANOVA table

Factor DF S MS F

Subject strength (SS) 3 120.69 40.23 3.68Work cell layout (WCL) 3 357.69 119.23 10.90Pick-up structure height (PSH) 3 54.19 18.06 1.65Mounting structure height (MSH) 3 185.19 61.73 5.64

Error 3 10.94Total 15

Table 9Coe$cient values for the additive model

Factor Levels

1 2 3 4

Subject strength (SS) 3.2 !0.1 !4.3 1.2Work cell layout (WCL) 6.2 2.2 !1.8 !6.6Pick-up structure height (PSH) 1.4 !2.1 2.2 !1.6Mounting structure height (MSH) 4.4 !0.3 !5.1 0.9

Fig. 9. The percentage signi"cance of each factor.

Table 10Results of the veri"cation procedure

Parameter Value

¹011

47.2 ss213%$

12.5 s2PE

#-$7.1 s

¹010

53 sPE 5.8 s

the best results. The cycle time increases upon a decreaseof the mounting structure height, while "xing requires lesscomfortable postures.

The results of ANOVA (Table 8) provided a quanti"edindication about the e!ect of each factor, in the speci"c

process, on the performance measure (Fig. 9). The workcell layout results as the most important factor. It provesimposing signi"cant variance on the required subjects'movements, resulting in respective e!ect on the cycletime. The mounting structure height has proved to be thesecond most in#uential factor. It a!ects signi"cantlythe posture and e!ort required for orientating andpositioning the propeller at the right position. Thesubject's strength has also an important e!ect on theprocess cycle time. The weight of the propeller suspendssubjects of rather poor strength to carry out the processin a time-e$cient way. The pick-up structure height hasa low e!ect on the performance measure in comparisonto the other factors. This is due to the fact that no specialhandling is required during the pick-up movement.


Based on the experimentation results, an additivemodel has been produced, in order to account for a pro-cess speci"c semi-empirical time model. Using this model(Eq. (2), Table 9), the process cycle time can be estimatedfor any combination of the process factor levels, includ-ing those that are not provided by the orthogonal arrayused for conducting the experiments. The veri"cationprocedure has showed that the prediction error lies with-in the acceptable error limits. Consequently, this additivemodel is considered adequate for describing the depend-ence of the cycle time on the process parameters selected.

6. Conclusions

The work presented in this paper refers to the concep-tualisation and development of a prototype virtual ex-perimentation environment, called the virtual assemblywork cell, which can be used as a planning tool forassembly processes. Based on virtual reality techniques,this environment enables an immersive and interactiveprocess performance. Process-speci"c simulation featuressupport this performance or simplify complex processaspects, taking under consideration the limitations ofreal-time virtual environments. Models and functionshave been embedded in order to account for the quanti"-cation of critical performance measures and constraints.

A realistic test case, namely the performance of theassembly of a high-speed boat propeller, has been de-scribed. The capability of the environment to supportvirtual experimentation has been demonstrated by carry-ing out a series of experiments based on the methodologyof matrix experiments using orthogonal arrays. Experi-mental results for the process cycle time have beenproduced, under ergonomically acceptable situations.Results have been processed using ANOM and ANOVA.The in#uence of a number of process parameters andtheir combination at the process cycle time has beenquanti"ed, and a process speci"c semi-empirical timemodel has been developed.

Although the functionalities provided allow for arealistic performance of an assembly process, the virtualassembly work cell does not support yet an exact repres-entation of reality or specialised process characteristics.Nevertheless, this work demonstrates the feasibility andusefulness of the novel virtual experimentation approachfor supporting the planning of assembly processes. Thisapproach emerges as a time and cost e!ective alternativeto physical experimentation, in cases where factors thatcannot be described analytically, such as the humaninvolvement, are under consideration. Moreover, the ca-pabilities provided for realistic and intuitive process per-formance, justify the advantages of this approach againstcurrent desktop simulation methods.

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[5] Gupta R, Whitney D, Zeltzer D. Prototyping and design forassembly analysis using multimodal virtual environments.Comput Aided Des 1997;29(8):585}97.

[6] Jayaram S, Connacher HI, Lyons KW. Virtual assemblyusing virtual reality techniques. Comput Aided Des 1997;29(8):575}84.

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[8] Chryssolouris G, Mavrikios D, Karabatsou V, Fragos D, Sarris I,Mourtzis D. A virtual reality based approach for the veri"cationof human related factors in assembly and maintenance processes.Proceedings (edited CDROM) of the International Conference onComputer Aided Ergonomics and Safety, 1999.

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A virtual reality-based experimentation environment for the verification of human-related factors in assembly processes