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The visual inspection of product surfaces

N. Baudet , J.L. Maire, M. PilletLaboratoire Système et Matériaux pour la Mécatronique, Université de Savoie, 5 Chemin de Bellevue, 74944 Annecy le Vieux, France

a r t i c l e i n f o

Article history:Received 30 September 2011

Received in revised form 9 August 2012Accepted 9 August 2012Available online 21 August 2012

Keywords:Visual inspectionPerceived qualitySensory analysisManufactured products

a b s t r a c t

Many companies practice the human visual inspection of their production. In some cases, this inspectiontakes the aesthetics aspects of the product surface into account. However, only a few companies have

developed methods for this type of inspection. In this article, how a sensory analysis test can be appliedfor the visual inspection of product surface is described. A synthesis of the specications for an effectivevisual inspection of products with high added-value is detailed. These specications can also be adaptedfor any type of product, including food products and packaging.

2012 Elsevier Ltd. All rights reserved.

1. Introduction

The consumer’s perception of the quality of a product dependslargely on thequality of itssurfaces. It is particularlythecasefor lux-uryproducts, buthas nowadaysbecomeso foran increasing numberof products.For food products forexample, the texture ( Pereira,Ma-tia-Merino, Jones, & Singh, 2006 ) or theappearance( Munkevik,Hall,& Duckett, 2007 ) signicantly inuence what the consumer pur-chases. In the automotive industry, the tactile perception ( Giboreau,Navarro, Faye, & Dumortier, 2001 ) and thehaptic perception ( Souff-let,Calonnier, & Dacremont, 2004 ) of theproduct surface areimpor-tant in consumer choice. Obviously, the visual perception of thesurface is also very important ( Creusen & Schoormans, 2005 ).To en-sure that each manufactured product meets the expected visualcharacteristics, a visual inspection must be carried out.

The bibliography concerning the visual inspection methods isvery important. It includes the inspection conditions ( Garaas &Pomplun, 2008; Garret, Melloy, & Gramopadhye, 2001; Jebaraj,Tyrrell, & Gramopadhye, 1999 ), the training techniques ( Chabuk-swar, Gramopadhye, Melloy, & Grimes, 2003; Nickels, Melloy, &Gramopadhye, 2003; Rao, Bowling, Khasawneh, Gramopadhye, &Melloy, 2006; Rebsamen, Boucheix, & Fayol, 2010 ), different waysof controlling ( Gilden, Thornton, & Marusich, 2010; Lee & Chann,2009; Schütte, Dettmer, Klatte, & Lauring, 1999 ) or methods to de-tect defects ( Hassan & Diab, 2010; Zamuner, 2011 ).

However, visual inspection is often described as the way todetect a product’s functional anomalies (for example, the specic

color of a fruit which gives one the indication of its ripeness). Thissaid, visual inspection also sometimes includes also an aestheticobjective (for example, the specic color of a fruit which doesnot seduce the consumer). Sometimes, even the two objectives(functional or aesthetic) are complementary. For example, the ex-tra carbon dioxide adds brightness to a white wine and increasesits longevity. Thus, brightness can be inspected to ensure thatthe product will keep its functional properties over time. Sincethe intensity of the brightness is also a signicant aesthetic crite-rion in consumer choice, visual inspection can also be carried outto ensure that the product meets aesthetic requirements. ‘‘ The suc-cess of the food industry depends on the consumer ’s continuing con-dence that the appearance of the product is a true indicator of thesubsequent acceptability of the eating quality of the food ’’ (Caballero,Trugo, Finglas, et al., 2003 ).

The visual inspection is often carried out by one single inspectorwho assesses thequalityof theproduct by referring to either a setof standard products or to his own experience. When he detects ananomaly, he has to scrap the product. However, in some cases, hehas to decide if the anomaly is critical or not. He needs to evaluatethe intensity of this anomaly and its impact on the quality of theproduct. Unfortunately, this process is poorly formalized ( Debrosse,Pillet, Maire, & Baudet, 2010 ), particularly in the case of aestheticobjectives. The evaluation of the anomaly is verysubjective becauseit depends on the inspector’s level of knowledge, his know-how andhis perception of the importance of the anomaly.

Therefore, a method must be developed to reduce the variabilityin the results of visual inspection for any product (if possible). It isthe main aim of our research program, called INTERREG IV, whichbrings together the University of Savoy and Lausanne Federal Poly-technic School (EPFL) and several Swiss and French companies.

0950-3293/$ - see front matter 2012 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.foodqual.2012.08.006

Corresponding author. Tel.: +33 4 50 09 65 99.E-mail addresses: [email protected] , [email protected] (N. Bau-

det).

Food Quality and Preference 27 (2013) 153–160

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Fig. 1 gives some examples of the products whose visual inspectionmust be improved (S.T. Dupont and Fournier).

The method that we propose, and that is described in this paper,is based on the use of sensory analysis methods.

2. Visual inspection of surface appearance

2.1. Different types of surface deviation

For a visual inspection with an aesthetic objective, the processused to decide if the product complies is much more complex thanfor a functional objective. The nonconformity on an aesthetic crite-rion can be dened by an unacceptable deviation between an idealpart and the real part. We have dened three types of deviation:

– A deviation from a reference: it is expressed by differences in thecharacteristics of the product/component (e.g.: color, texture,etc.).

– A deviation from the intent of the designer: it is expressed by thedifferences in the conguration of components, from one com-ponent to the one next to it (e.g. an contrast in color betweentwo components).

– A local deviation: it is expressed by the presence of an anomaly:mark, pollution, heterogeneity or distortion (e.g. a slight dent onthe surface).

During visual inspection, the inspector has to detect a deviationin order to identify which type it is, then describe it and nallyquantify its intensity. Sensory analysis can be used to help him.Sensory tests have proved useful in the food industry to expressthe differences in human perception (taste, touch, hearing, smelland sight) in an identical way as if one was using a measuringinstrument. Sensorial analysis is classically used to develop newproducts or to compare products with those of the competitors.

It can also be used as a quality control tool to check sensory char-acteristics. Few studies have been noticed about the use of sensoryanalysis for visual inspections. Usually these studies take into ac-count the overall appearance, in terms of shape, color, etc., as Etaioet al. (2010), Etaio et al. (2011) and Pérez Elortondo et al. (2007)made to assess visual quality of the wine and cheese, respectively.These studies do not give further information when a defect ispresent, or, they take the defect into account just to specify the gi-ven value, on a scale, about the overall appearance. However, sen-sory analysis seems to be perfectly suitable to describe a deviationon a surface and to quantify its intensity.

2.2. Sensory tests

There are two main groups of sensory tests: analytic and hedo-nic. Hedonic (or affective) tests are intended to identify the con-

sumer preferences for two or more products and to givesubjective information about how well these products are likelyto be accepted ( Lawless & Heymann, 2010 ). Analytical tests are in-tended to identify and/or evaluate differences between two ormore products.

As said previously, the purpose of visual inspection is to assessdifferences and not preferences. Therefore, analytical tests are usedfor visual inspection. These tests can be divided into two subgroups(Depledt, 2009 ):

– Discrimination testing which is used to determine the probabil-ity that a very slight difference can be perceived (triangular test,duo-trio test, two out of ve test, paired comparison test, n-Alternative Forced Choice method, A-Not-A test, Sortingmethod, etc.).

– Descriptive analysis which is used to quantify a difference whenit is undoubtedly perceived. There are three main methodsassociated with this analysis: ranking, scaling and proling.

In visual inspection, the deviation is generally clearly perceived.Thus, descriptive analysis is used for visual inspections. Table 1 de-tails the characteristics of each approach.

Ranking cannot be used for visual inspection because the differ-ence between products is expressed using relative values. How-ever, these approaches can be used during inspector training inorder to verify the capacity of inspectors to perceive and rank a dif-ference in products according to a sensory attribute.

Scaling can be used for visual inspection in the case of a devia-tion from a standard (e.g. a difference between the color of the con-trolled product and the color of the model product) and in the caseof deviation from the intent of the designer (e.g. a difference be-tween the color of two adjacent components of a product). In thecase of local deviation, these approaches can be used if the differ-

Fig. 1. Visual inspection in S.T. Dupont and Fournier.

Table 1

Approaches used in sensory descriptive analysis.

Approaches Dimension Aim

Ranking Monoattribute

To classify three or more simultaneouslypresented products with regard to a specicattribute (e.g. sweetness, hardness, etc.)Multi

productsScaling Mono

attributeTo evaluate one or more products (notnecessarily simultaneously presented) on ascale of intensity with regard to a specicattribute

Mono/Multiproducts

Prol ing Multiattributes

To evaluate all the attributes of one or moreproducts (not necessarily simultaneouslypresented) in order to build up a sensorial

prole

Mono/Multi

products

154 N. Baudet et al. / Food Quality and Preference 27 (2013) 153–160



ence is perceived on the same design (e.g. a scratch on a mono-col-or product).

When a local deviation is perceived by the inspector, it is notpossible to compare this product with another anomaly-free prod-uct, or to compare the perceived anomaly with another one. That isespecially true when the inspection applies to several types of products whose shape designs and colors are different. In this con-

text, the same anomaly seen on two different products, or the sameanomaly in two different places on the same product, will not nec-essarily lead to the same decision. For example, Fig. 2 shows howthe same scratch (same depth, width and length) may lead to dif-ferent inspection results. In cases ‘a.’ and ‘d.’, the product is ac-cepted (the scratch is located, respectively in the direction of thedécor and close to the edge of the product). However it will be re-fused in cases ‘b.’ and ‘c.’.

In the case of local deviation, a prior characterization of theanomaly is therefore necessary. Proling methods can be used,not to obtain a sensorial prole of the product, but to obtain a sen-sorial prole of the anomaly detected on the product (i.e. the char-acterization of the anomaly using a set of attributes). The questionis: ‘‘How does one know which attributes can be used to prole ananomaly?’’

3. Sensory prole of an anomaly

3.1. Types of anomaly

Before determining the attributes of a given anomaly, it is nec-essary to clearly dene what can be considered as an anomaly.

The eye of an inspector is able to identify shapes, colors andedges. Therefore, he detects an anomaly when he perceives a localdeviation of color, edge or shape. For this local deviation, two cat-egories of anomalies can be identied: the progressive anomaliesand non-progressive anomalies. A progressive anomaly has anintensity that will evolve over time and that will be critical forthe customer (e.g. corrosion detected during control which willspread signicantly over time). The detection of a progressiveanomaly during visual inspection leads the inspector to reject theproduct immediately. On the other hand, a non-progressive anom-aly retains the same intensity (e.g. a scratch detected during theinspection the length of which never develops over time). In thiscase, the inspector has to decide whether the piece should be re- jected or not. Sensory prole methods are therefore only used fornon-progressive anomalies.

It is obviously impossible to identify all of the anomalies thatcan occur on a product (even if some companies try to list all theanomalies that inspectors could nd). For example, are a 1-mmscratch and a 2-mm scratch two separate anomalies? Or are theythe same anomaly? We therefore decided to list all the differenttypes of anomalies rather than all the possible anomalies. In Guerra

(2009) , we listed four types of non-progressive anomalies: marks,stains, deformations and particles. More generally, for visualinspection, all anomalies can be classied as:

– Mark: something that damages the surface (scratches, scuffs,dent, etc.).

– Heterogeneity: anything that makes the surface lose its homoge-neity (a stain, a color difference, etc.).

– Pollution: anything undesirable that is added to a surface (a hair,a dirt particle, a black stain, etc.).

– Distortion: anything that changes the shape of the surface (anoverly-polished surface, non-regular line of light, etc.).

The advantage of this classication is to minimize the vocabu-

lary used to describe anomalies. Another advantage is that it canbe used for all types of products.

3.2. Anomaly attributes

If possible, anomaly attributes must be chosen in accordancewith NF ISO 11035. This standard denes an attribute like ‘‘ the termreferring the subject to an element of product perception ’’ (ISO.,1995 ). It must have properties such that an assessment can bemade using a scale of intensity and must meet a number of princi-ples, including that of being relevant (suitable for describing anom-alies) and accurate (which can be understood by the inspectors).Attributes must also be discriminating (allowing one to differenti-ate between the anomalies) and, wherever possible, independentof each other (each covering different aspects of the event).

When an inspector controls a product, he focuses his attentionrstly on the anomaly, and then on its environment. To build upthe list of attributes, we therefore consider:

– The factual description of an anomaly, which refers to how todescribe intrinsic characteristics of the anomaly.

– The context of the anomaly, which includes the local contextand general context (how the anomaly is perceived in its overallenvironment).

The factual description includes two parameters:

a. The viewing conditions, which refer to the conditions inwhich the anomaly is perceived, and which regroups veattributes:s Distance or magnication: it characterizes the distance

from which the anomaly is perceived. This attributedepends on the product being controlled (e.g. an anomalyvisible from up to 1 m for furniture or an anomaly onlyvisible using a magnifying glass for a watch component).

s Orientation or light effect: it characterizes the viewingangles one has on the anomaly. Fig. 3 shows the move-ments that the inspector has to do during visual inspec-tion. Three distinct phases occur during the movement,called ‘‘light effects’’ ( Guerra, 2009 ):

Black effect: the light beam arrives perpendicular to

the part surface and this is reected perpendicularly.The inspector perceives a matt surface ( Fig. 3a).

(a) (b)

(c) (d)Fig. 2. The same anomaly in two different decors (a) parallel to the décor; (b) acrossto the décor; (c) in the middle of a polished décor; (d) close to the edge).

N. Baudet et al. / Food Quality and Preference 27 (2013) 153–160 155



Shiny effect: the incident light beam returns a reectedbeam so that the subject is placed in the path of thebeam. The inspector is dazzled by the part surface(Fig. 3b).

Mirror effect: the incident light beam reaches the sur-face tangentially. The inspector sees his reection inthe part surface ( Fig. 3c).

s Light intensity: the visibility of the anomaly depends onthe light intensity (in the control room: 1000 lx recom-mended; 200 lx, etc.).

s Time: it represents the time to nd the anomaly duringthe detection period (e.g. visible immediately or visibleafter a thorough scan).

s Direction: it characterizes the orientation of the part inwhich the anomaly is visible (e.g. the anomaly is visibleonly when the part is oriented vertically).

Among the ve attributes related to viewing conditions, time isthe least reproducible. It can vary from one inspector to anotheraccording to the detection procedure and also sometimes to the‘‘luck’’ in nding the anomaly.

The light intensity attribute is hardly used because the worksta-tions are rarely equipped to be able to change the light intensity ina uniform and constant way. So during the detection and evalua-tion steps of the product, the light intensity is often constant (forthe partners of the INTERREG program, the intensity is xed at1000 lx).

The use of distance attribute depends on the size of the anomalyin relation to the company’s standard. In the watch industry forexample, the use of a magnifying glass is common and the anomalywill be more critical if it is visible with the naked eye. Sometimes,

the distance is xed and the inspection is done from a standarddistance.On the other hand, the orientation is an attribute that is often

used to characterize the viewing conditions of the anomaly.

b. The anomaly description, which refers to all its physicalcharacteristics; includes two attributes:s Size (e.g. length, width, thickness, etc.).s Shape (e.g. round, elongated, etc.).

The context of the anomaly includes two parameters:a. The local context, which refers to how the anomaly is per-

ceived in its immediate environment, itself including twoattributes:s Shape contrast: It characterizes the shape of the anomaly

relative to other shapes (c.f. Fig. 2 above).

s Color contrast: It characterizes the color of the anomalyrelative to other colors in the decor (e.g. an anomalywhose color is very different from the other colors).

b. The general context, which refers to how the anomaly is per-ceived in its overall environment (e.g. the anomaly is visiblewhatever the nal conguration of the product).

All identied attributes can be used for any type of anomaly(mark, pollution, distortion or heterogeneity). However, the def-inition of the level of each attribute’s intensity depends on thetype of anomaly and the level of quality reached by thecompany.

3.3. Types of standard

The choices of attributes to be used for visual inspection, aswell as the denition of each level of each attribute, are veryimportant. However, it is also important to choose how the com-pany wants to share these denitions. Three main types of stan-dards can be used.

The choice of the standard depends on the product and on thecharacteristics of the product to control ( Costell, 2002 ):

– Product standard: it includes acceptable variation limits for eachsensory attribute of the ingredient or raw material used for theproduct. This standard offers the advantage of being easilyobtained, maintained and reproduced.

– Mental standard: it is developed by one or more experts whodene the desired level of the sensory characteristics of theproduct to be reached. These experts have demonstrated theirability to recognize and evaluate the sensory properties of theproduct.

– Written standard: here, it is the written denitions of a givencritical attribute that drive consumer acceptance. It includes adenition of the key attributes, the perceptible variations of which depend on the raw material and on the manufacturingprocess, the common defects and unacceptable characteristics.

In the case of visual inspection whose purpose is aesthetic, theproduct standard is often difcult to use. This is especially truewhen the results of the evaluation of the anomaly depend on itsposition on the product ( Fig. 2). It is indeed difcult to build up acollection of products (or pictures of products) which reects allthe possible product anomalies. In addition, even if it can be built,this collection is difcult to maintain over time (the managementof this collection can, for example, cause other anomalies). When

the product changes over time (e.g. a perishable product), it isimpossible to maintain a product standard.

(a) (b) (c)

Fig. 3. Light effects (a) black effect; (b) shiny effect; (c) mirror effect).




4.1. Exploration step

Many factors impact the performance of the exploration step.Table 3 lists some of these factors ( Debrosse et al., 2010 ).

The BP X 10-042. (2006) manual written by AFNOR recom-mends the use of workstations to prevent unwanted light. Theworkstation also guarantees that the exploration conditions areidentical for all inspectors (same light system, same light, sameobservation background color, etc.) and thus limits some of thevariability in the visual inspection results. However, it is not al-ways possible. In Mobalpa Fournier SA for example, these condi-tions cannot be applied because parts can vary widely in size andthe control has to be carried out at several different stages in theproduction process. There is not only a nal inspection, and it isimportant to note that the evaluation step is performed underthe same conditions as the detection step.

Then the lighting system must be adapted to minimize thesefactors. The NF X 35-103 standard (1990) and the Safety data sheetED 85 (Vandevyver, 2005 ) give some principles to respect. Table 4lists some of the specications for light system.

On factors like manpower, the training of inspectors is decisivein order to improve this step. During training, it is possible to ex-plain to them what an anomaly is made up of, what the different

types of anomalies are, how to detect a progressive anomaly, etc.Giving the inspectors some feedback on how they perform theexploration task, in order to make them more committed to thistask is also an important part of the learning process ( Megaw,1979 ).

For the exploration step, it is also possible to dene an appropri-ate strategy, such as ( Nickels et al., 2003 )

– A random exploration strategy: each zone of the part is likely tobe explored, possibly several times.

– A systematic exploration strategy: each zone of the part is con-trolled once but systematically.

Arani, Karwan, and Drury (1984) and Wang, Lin, and Drury(1997) showed that the systematic exploration strategy signi-cantly increased the performance of detection. Indeed, it limits therisk of forgettingsome areasto exploreand atthe same time reducestheexploration time, each area being explored only once. This is thestrategy which has been chosen to dene an observation standard,such as the example given in Fig. 5. This standard includes:

– The ‘‘where’’ and the ‘‘what’’, which dene the zones to observeand the elements to inspect.

Table 3

Inuencing factors in the exploration step.

Factors

Material Size, weight, color, texture, etc.Manpower Visual acuity, age, experience, training, fatigue, concentration,

motivation, etc.Mean Lighting system, workstation, magnication, etc.Method Observation standard, magnication, light effects, time, etc.

Conditions Unwanted light, noise, dust, natural lighting, etc.

Table 4

Light system specication for visual inspection.

Area lighting 750–1000 luxColor rendering

index (CRI)>85

Color temperature 4000 KOrientation Perpendicular to the work plan, but can be adapted to

facilitate the task (but avoid dazzle)Louver use Yes (parabolic louver or opaque depending on the

inspected product)

Fig. 5. Example of a detection procedure.




– The ‘‘how’’, which denes what path the inspector’s eyes shouldfollow during exploration. This denition of the path preventsthe inspector from focusing exclusively on certain anomaliesand/or focusing only on those most recently detected.

– The ‘‘when’’, which denes at what stage of the manufacturingprocess the inspection takes place.

– The ‘‘how long’’, which denes an exploration time for eachzone.

4.2. Evaluation and decision

The most critical point of the visual inspection process is how tomake the link between evaluation and decision. In order to make a

decision, the inspector must have a method that correlates theintensity of each attribute of the anomaly with the quality stan-dard of the product.

Several methods are proposed in literature, for example the cri-teria/level table, the tree-like evaluation table or the correctedhierarchical evaluation table ( Baudet, Pillet, & Maire, 2011 ). Fig. 6shows the table we used in our research program with one of our industrial partners (Mobalpa Fournier SA).

The inspector reads the table from left to right, adds up all theattribute values in order to obtain the anomaly’s nal intensity va-lue (the attribute values can also be weighted).

This value allows him to decide whether the product should beaccepted or not. For example, in the case of Fig. 6, if the anomaly hehas detected is visible at arm’s length, if it is visible from all angles,

if it causes a break in the form, if it is the same color as the productand if it is on a visible part when the furniture is mounted andclosed (+1), the inspector decides to refuse the product (nalintensity = 3 + 1 + 1 + 0 + 1).

All the tables make a linear correlation between the attributes’values and the quality standard of the product. However, when anon-linearity exists, a neural network can be used. An exampleof the use of this method is described in ( Baudet, Pillet, & Maire,2012 ).

5. Conclusion

In this work, we have proposed how sensory analysis can be ap-plied to the visual inspection of product surfaces (mainly when the

product has an anomaly (scratch, pit, etc.), that can inuence aes-thetic perception. Thus, for local deviation, sensory proling is the

most adequate test to be used. Inspectors can assess how its attri-butes inuence the quality perception of the product, using ananomalyprole. However,visual inspectionis carried out by severalpersons, so thechoice of a good standard is necessary. We proposedto usethe written standard,sinceit is thebest way to communicate,to maintain and to share quality standard of the company.

Sensory analysis is only a tool to improve visual inspection. If acompany wants to control this process, it must control all the steps(exploration, evaluation and decision). Exploration requires goodconditions to improve and facilitate the detection of anomalies.When a workstation cannot be set up, the light system must atleast be adequate. When an exploration method had to be created,systematic strategies proved to be the best solution to develop an

observation standard. Inspector training must be implemented, asit is important to explain what an anomaly is, inform the inspec-tors of the different types of anomaly that exist and how to detectthem and to motivate them.

The evaluation and decision steps are closely linked. To improvethese steps, a company must choose the right evaluation attributesand then develop a method which correlates evaluation attributeswith the quality standard. Inspector training on the evaluationattributes and the use of the method is obviously necessary.

The proposed methodology for visual inspection has beenproved to be very robust. It can be used for any type of product(lighters, watches, furniture, packaging, etc.) when its surface hasa local deviation. It allows one to assess the inuence that theanomaly causes on the aesthetic perception of the product. For this

methodology, the choice of evaluation attributes and their levelscan vary from product to product but, in general, the parameterswill always be the same (factual description, local and generalcontext).

Acknowledgments

Financial support of this work was provided by the EuropeanINTERREG VIA France-Switzerland Program. The authors wish toacknowledge the reviewer for allowing the improvement of thiswork.

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Viewing conditionsContext

Local Global

TotalintensityDistance Orient ation

Shapecontrast

Colorcontrast

Location

D O S C L

3 Closelyvisible

0 (zero)Visible under

certainangles

-1 (minus one)Anomaly is in

the samedirection ofthe decor orline shape

0 (zero)Anomaly has

the samecolor as thesurface

-1 (minus one)Not visible in

assembledfurniture closedand open

1

A c c e t e d

2

3

4 Visible atarm’s length

0 (zero) Visiblein assembled

furniture open

40 (zero)

Uniform decor 5+1 (plus one)Visible under

all angles

+1 (plus one)Anomaly

causes a colorcontrast

+1 (plus one)Visible inassembled

furniture closed

6R e e c t e d

5 Visible froma distance

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same directionof decor

7

8

Fig. 6. Corrected hierarchical evaluation table.

N. Baudet et al. / Food Quality and Preference 27 (2013) 153–160 159



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