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India n Jou rnal or Fibre & Text il e Research Vol. 27 , Septeillber :WO:: , pp. 315-322
Engineering design of woven fabrics-A recent approach
B K Behera" &'S B Muttagi
Dcpartlllcnt orTextile Technology. Indian Inst itute or Tec hnology, New Delh i 11 0016. India
R{'C('il'('d 5 May 2000; r(,I 'is('d r('ccil '('d alld a('u'!J/('d 20 /)(,(,(, III/Ji'I' 200!
Thc rabric engineering is st ill large ly based on ex perience and tr ial and error. The Illajor task is to dcvelop cOlllprehcnsive and user- rri endl y program packages wi th database on fibre, yarn and fabric properLics by the application or rabric object ive Illeasu rcillent technology , a knowledge-based expert system to provide graphica l tool s and nUlllerical solutions for the rabrie designer and engineer to Illeet the needs of fabr ic lllanUfaclllrcrs. Compared to Illathematical mode ling, artiri cial neural network can be a powerrul too l to Illodel the non -lineariti es and cOlllpl ex ities involved in tl1<' predicti ons or rabric propricties. Art ificial neural network cillbedded expert syste m can be de vcloped to aid in engineering the design or woven fabric .
Keywords: Anificialneural network. Computer-aided designing. Fabri c engineering. Woven fabric
1 Introduction Des igning is a process of delineating a product to
meet the functional and aes theti c performance criteria with efficient usc of ava il ab le resource, and the engineering of the fabric is defined as the applied science deal ing with relationship between raw material and finished product'. The ultimate goal of theoreti cal and ex perimental studies of tex tile fibres, yarns and fabrics wou ld be the engi neering design of these materials to meet the spec i fi ed levels of mechanical and physica l performance. The essenti al requirement to engineer the fabr ic design is reli ab le and reproducible prediction of the complex structureproperty relat ionships between the constituent elements and the process parameters. At present, thi s seen ls to be difficult , particularly considering the decisive influence or fabric fin ishi ng process on fab ri c behav iour. Fabri c finishing is still depe ndent on empirical know ledge and accum ulated ex perience than on scientifie anal ysis. Moreover, many design factors in fab ric engineering are not properl y defined and quantifi able. Designi ng of fabrics is stiil based on traditi onal techniques, expe ri ence and intuition of the des igner.
Textile materia ls are highly flexible and difficult to be measured accurately li ke other eng ineering materi als. This inherent drawback creates matn
"To \\'holll all the correspondence shou ld be addressed. Phone: 659 1414 . 6562403 : Fax: 0091-01 1-6562403 : Email: [email protected] .e rnel. in
prob lem for accurate engineering of tex ti le fabrics. In addition to thi s manufacturing constraint, the end-use requirements of tex til e fabrics also invol ve lot of subjecti veness, Fi ni shed fabrie properties are mai nl y judged by the user's perception which varies from person to person , place to place and occasion to occasion . The objective evaluation of fabri cs for these subj ective requirements wou ld playa vit al role in fabric manufacturing in the years to come. Because the tradi tional properties li ke durabi I ity and aesthetics would become secondary requirement and the funct ional characterist ics such as handle, feel, comfort and need-based performance would dominate ed ucated conSUlllers choice. To achieve these requirements, the engineering des ign of apparel fabrics has to adopt sc icn: ifi c and accurate product development too ls .
Traditionall y on the basis of past ex perience ane! market survey, a fabric design is proposed, samples are manufactured and finishing techni ques are appli ed. The result is then assessed by expe rt or percei ved from consumer reaction. The procedure is ted ious and in vo lves time consutT'.ing sample preparation ail e! repetitive testing to evaluate the properties and go through these processes iteratively till the customer is satisfied, There is no standard or systematic design opt imiza ti on process for \voven fab ri c manu faclu ri Ilg in texti Ie industry. However, of late, with the introduction of informati on technolog;' into industrial manufacturing, the traditi onal combersome processes have been simpl ied. Th is
316 I 'DI AN J. FIBRE TEXT. RES., SEPTEMBER 2002
paper gives an overview of fabric englneen ng, starting fro m rul e-of-thumb mechani stic princip le to the use of arti ficial i ntell igence.
2 Traditional Predicting Tools 2.1 App roach
Fabric mechanics has been traditionall y approached concerned const itutive
in two ways: (i) mi cromechanicswith predicting the fundamental
rel ations of textile structure in terms of properties of its co nst ituents and their geo met ri c arrangements, and (ii ) macromech:.lI1ics - concerned with pred icting the complex defonnation of the materi al subject to co llection of forces imposed in practical situation 2.
To predict quantitativcly the mechanical performance of textile stru ctures, the mechani stic, geometric, energy and empirical methods havc genera ll y been adopted. These methods dcpcnd on principles of equ ilibrium forces and moments involving linear elas ti city. They all have major limitations in the engineeri ng des ign sensc. Many of them are linear approximations or are bascd on ove rsimplifi ed geometries. Typ ica ll y in these mcthods, fibre properties are specified by parameters of lincar or non- li near equations, geomet ry is defined by algebra ic and tri gonometric equations, and mechanics is analysed by differential or integral calculus.
Adopt ing energy methods for predi ct ion is bascd on either conservation or minimization of energy. Energy is a sca lar quantity and the indi vidual contributions to total energy can bc addcd, whercas forces or st resses must be summcd vectorall y. This lends it casier to make usefu l approximations to fabric structure th at do not cause major errors in cst imatcs of energy.
Emp irical methods are carried out under controllcd conditions relying on the principlcs of design of ex periments. Data gathering becomes time consuming and expens ive. Statisti cal methods, such as multiple regression , are not sui table for the multi variable parameter space investi gati on of the types under cons ideration , since they assume in advance th at the factors are both independent and linear. Both the assumptions are f:tlse. In other words, a method is needed that does not oversimplify the problems in order to reduce the mathematical complcx ity of the solut ion.
The ex tensive work of Picrce] in 1930s, based on traditional approach, prov ided the basic theory on which the fabric mechanics is now built. He analyzed,
for the first time, the basic equ ilibriu m structure of plain weave fabric in terms of the fo rce equilibrium and geo metrical model. Later, Grosberg.J analyzed the fab ri c tensi Ie, bendi ng, buck ling, shear and compressional properti es . These studies helped in understanding the mechani ca l behaviour of the fa brics. Fabric in sulation and thermal properties, clothing comfort and physio logica l studi es carried out by Baxter and Cass ie 5 identifi ed fabric thi ckness, lateral compression and surface smoothness as crit ical factors determini ng fabri c thermal and comfort characterist ics. Low-stress mcchanical properti es of fa bric like tcnsil e, shear, bcnding and formability (form ab ility =
fabric bending ri gid ity X fabri c longi tudin al comprcssibility), infl ucnce the t:li lorability and garmcnt appearance6
. Thcsc studi es lilade it poss ibl e to apply fundamental physical principles to pred ict the basic mechanica l properti es of ex tension, shear, bending and comprcss ion for woven fabrics. Thc consistcnt appearancc of fabr ic low-stress mechan ica l and surface properties, such as extension, bending, shear, comprcssion and surfacc smoothness, in studi es of fabric mechanics, handl e, thermal i nsulat ion , comfort and tai lorabi I ity clcar! y demonstrate the importance of these propcrti es for the spec ifi cation, predicti on and co ntrol of appare l fabric quality and pcrformance 7.
Even then the range of useful cngin eeri ng predict ions from these studics is very limited.
2.2 Limitations of Traditional Methods
Thc math emat ical/physical principles approach is now ncar its end as a way of making engi neering predictions. These mcthods arc strong ly mat hemati ca l based. Howcvcr, even if the broad simplifying assumptions are madc to make mathematics tractable, this approach typ ica ll y rcq uires many ycars to develop a model. Thcy are prob lcm spec i fic and any change requires a new ana lys is and ncw programs to so lvc cquations . They may not work we ll in practicc owing to the uncertainti es associatcd \\ ith rcal world dynamics. These proccdures arc not uscr friendly. However, th cy have over thc years contributed towards the understandin g of physical principles and sc ienccs involved in th e processes, and advanccmcnt and refi ncment of techn ology .
3 Recent Approaches in Fabric Engineering The need for computer assistance in dcsigning
ar iscs duc to thc complex dcsign fea tu rcs involved in fab ri c eng inecring. The process of dcsigning involves making cxhaustive scarch and examine each possiblc
BEl-IERA & MUTrAGI: ENGINEERING DESIGN OF WOVEN FABRICS- A RECENT APPROACH 317
structural alternati ve to meet the opti mum performance. There are many design objectives which may make conflicting demands on design process . Therefore, an effici ent heuri stic must guide the search for best des ign solutions. To automate the des ign process, the computational abiliti es and the flexibility of the computers to match the degree of des ign sophisti cation required by the fabri cs are desired. Computer-aided engi neeri ng so ftware too ls, such as finite element methods, equati on solvers, visuali zati on tools and 3-D modeling, help the designer balance the function al requirements of the product with aes theti cs.
The rapid growth in informati on technology in recent years has enabled the researchers to use artifi cial intelligence (AI ) for manufac turing co mplex products. The ai m of A I is the developments of paradi gms or algorithms that require machines to perform tasks that apparentl y requ !re cogniti on when performed by humans. The AI system must be capable of doing foll ow in g three things9
:
• Store knowl edge, • Appl y the know ledge stored to solve
problems, and • Acquire new knowledge th ro ugh ex perience.
The applicati on areas wi th in the fi eld of AI include ex pert systems (ES), art ificial neural networks (ANN ), sensor interpretati on, intelli gent robotics, natural language process in g, automati c programmin g, and co mmon sense reasoning. There arc fo ll owing two main directions of the development of CAD system for an art ificial intelligence approach 10:
• Setting up separate universa l subroutines, combined in a proper way, cou ld be appli ed to the solution of parti cul ar prob lems ari si ng in theoret ical and industri al research. Spec ifica ll y, art ificial neural network and expert system can be incorporated in the system to find sol ut io n to the problem, and
• Setting up database of information on fibre, yarn and fab ri c properties by the appli cati on of fabri c objec ti ve measurement technology, a know ledgebased system wh ich ut ili zes the poo l of avail abl e expert ise and histo ri ca l da ta, and a user graphic interface to in terac t with user queri es.
3.1 Ex pert System
The engi neering design or woven tex tile stru cture is a complex task and makcs ex tensive usc of empirical know ledge accum ulated over the ti me. Only few highly experienced experts in the industry know rule-of- thumb principles and methods of complex
structural synthesis under performance constraints. Therefore, if the knowledge and experti se of the experts in the field and from the literature are used to develop a sc ientifi c database, then compu ter software can be prepared with such acquired kn owledge, which, in turn , will provide ex pert advi ce t II the :-,roblem. Such a knowledge-based system is cal led expert system. It is a system that has been engineered to simulate a human ex pert. The ex pert system is well suited to database manipulations, sy mboli c reasoning and dec ision making. It has confli ct resolution principles so that the mutu all y antagonis t requircments are sati sfi ed to the greatest extent possible and with minimal compromi se. Expert system permits the use of different des ign knowledge representati onal modes, such as heuri sti c, procedural and factu al knowledge, and the reasoning methods working with each of these representational modes.
The technology of artificial in te lli gence (Al) and ex pert system (ES) enabl es the computers to be applied to less determini sti c des ign tasks, which require symboli c manipulation and reasoning in stead of routine number of process ing. The ES can be used to diagnose, repair, monitor, analyze, interp ret, consult , plan , design, instruct, ex plain , learn and conceptuali ze. Ex pert system is a powerful tool for so lving problems, like design, which arc used for taking dec isions. Des igning is typi ca ll y dec ision making process and therefore the appli cation kn ow ledge engineering and ES are suitab le for it. The ES is very suitable fo r database manipul ati on and decis ion mak ing. The mod ul ari ty of ES enab les to acco mmodate the changes or mod ifica tions eas il y by changing or add ing merely the facts and rul es in the knowledge ll
-13
.
To construct a successful ES, the problem domain must be we ll de fin ed and there must be atleast one hu ma n ex pert ack nowledged to perfo rm well with in the appli ca ti on area; however, add itional knowledge relevant to problem domain can be obtained from other sources . Experts must have spec ial know ledge, j udgement and ex peri ence. Al so, the ex pert mu st be able to ex plain and justify the spec ial know ledge and ex peri ence and the methods used to appl y them to particular problems. Further, there must also be adequ ate programming tools, idea ll y a set of ES so ft ware build ing too ls. The spec ifi c hi gh level programming languages, such as LISP and PROLOG, arc often cons ide red to be languages of ES development. The bas ic structures of ex pert system is shown in Fig. I.
318 IND IAN J. FIBRE TEXT. RES., SEPTEMBER 2002
I User I hlct s
"'~I---=-----c--E\pcrtis
I(noT",vledge l.::,ase
Expe1t systern
Fig. I- Basic structure or ex pert syste m
The various components of ES arc listed below: • Th e knOlI 'ledge base - the domain facts heuri st ics
associated with the problem area, which must be wel l bounded and narrow.
• The i'~fercl1cc cnginc - the control structure/strategy for utilizing the knowledge base in the so lution of the problem.
• Th c dyna//lic / global dalabasc - work ing memory for keeping track of the problem status , the input data for the particular problem, and the relevant hi story of what has thus far been done.
• A lI.\"er~fi'icndly illlelj"ace - to facilitate interaction of the system with the user an d provide an human window to its operations, preferably a natural language framework ; additionally. an explanation module should be included to allow the user for queries and the reaso ning process underlying the system's answers .
• The klloll'lcdge acqllisilion //Iodlllc - to facilitate the transfer and transformati on of problem so lving ex pertise from the knowledge sou rce to the know ledge base. Since knowl edge is centra l to intelligence, the performance of ES is primarily a fun ction of the size and quality of the knowledge base it possesses. The facts co nstitute a body of information th at is widely shared. publicly available and generall y agreed upon by expert s in th e field . The heuri stics are mostl y pri va te and little di scussed rul es of good jUdge ment/guess ing th at charac teri ze expert leve l decision making in the fi eld. The knowledge must be represented or orga ni zed into a suitable form so that it can be readily accessed by the inference engine.
To create a knowledge base for a given problem, the expert's knowledge must be formulated according to knowledge representation scheme employed by the ES shell. The latter with debu gg ing facility represen ts the knowledge acqui siti on mod ule.
The difference between ES and con ventional computer programmi ng can be represented in the fo llowing form :
Data + Algorithm ~ Program Knowledge + Inference ~ ES.
3. 1.1 Applicaf.ion of ES in Fahric Designing
Since its introduction in DENDRAL to analyze the structure of chemical s at Stanford University in 1965 , there ha ve been many applications in various field s such as MYCY , XCON and so on. The expert system has some ad vantages such as an increase in ava ilability and reliabil ity, reduction in cost and danger, exp lanation , fast response, unemotional and steady response at all times, permanence, etc.
The importance of artificial intelli gence for textile and garme nts has been emphas ized since late 1980's. In garment manufacturing, the application of artificial intelligence, such as ex pert systems, neural networks and neuro-fu zzy al gorithms, has been introduced and emphasi zed by Styliosl ~ to predict the sewabi lity of fabrics . Srini vasa n cl 01.1 5 developed knowledgebased soft ware systems called the fabric defects analysi s systems (FADS) for inspec ti on of visual fabric defects with location and nature. Cruycke t ll
compared the computer expert system for weav ing operations with the performance of human ex perts. Frei and Walliser l7 and Karaman tscheva cl (/1. t X
reported that a developed expert system is both a decis ion mak ing instrument and a re feren ce for the wool dye r. Gaile/ ') reported the colour expert system. Curiskis and Grant21l introduced th e rul e-based expert system for fibre identifi ca ti on. Chat. g and Lee"1 have presented knowledge-b,lsed construction of a garment manufacturing system to opti mize the ga rment manufacturing process . The system is operated under Mi croso ft Windows using Vi sual C++ programmin g language. Nex pert Object is chosen as deve lopmen t tool. The knowledge represent ati on is in hybrid type including rul e-and object-ba<;ed se man ti c network and frame. The know ledge is acquired through meetings with human experts, techni ca l reports and case studies . The knowledge engineer th n fonnulates the knowledge and rules from acquired knowledge. The acquired knowledge is class ifi ed into materials . processi ng, problems and total re views. I n the material properties, there arc four sub-groups, namely appearance, sewability and tailorabilty , formability and auxi I iary material s. Si mi larl y, in processi ng group there arc six sub-groups including quality control system. The database and the knowledge base arc desi gned using object diagram in which the organi c relationship between the attri butes of the material s and the processing parameters is defined . The structure of the semantic network and fram e is represented through object diagram in a hierarchi ca l network. Then the rul es in the form of IF .. hypothesis
BEIIERA & MUTTAGI: ENGINEER ING DESIGN OF WOVE FA llR ICS- A RITENT APPROACII 319
. .THEN DO ... ELSE DO .. are followed uSIng the rule editor of the development 100 1. Based on know ledge-base. the inference eng ine is designed by !-'etting tht' strategies using strategy editor, depending on the inferences from the rules and connection between object and rules. The user can interact with it using natural language or from the database. Through the inferencing by the expert sys tem. the advice can he obtained on the doma in problem .
Dastoor el al. X 22.2.1 developed a generali/ed knowledge-based CAD systelll, called FABCAD, for fabric design and analyt ical module for simulation of the uniaxial/biaxial load deformation behav iour or plain woven fabric. The overall operation of system has been structured as Design Phase and an Analytical Phase. working closely with each other. Design Phase suggests the des ign alternatives and formulate s candidate fabric structures to meet specified funct ional requ irement s throu gh Re\ ision Stage and Syn thesis Stage mod ul es of the Heuri sti c Design component. The Analysis Phase serves to va lidate the des ign alternati\'es by carrying ou t detailed predictive tests of fabric performance. The work demonstrates the capab ility of actuall y creating a CAD environment that preserves and utilizes th e expert's knowledge to aid in preliminary structural des ign and predictive eva luation of industrial fabri cs economi ca ll y.
Fan and Hunte r2~ described a worsted fabri c expert system (WOFAX ) to prclVide adv ice during fabri c design and to pred ict and eva luate the propert ies and performance of the designed fabric. The system has eight advisers to ad vice for determining fabric compositi on, weave, ya rn count and setl. weaving details, yarn type, twist, fibre specifications, and finishing procedures. After the fabri c is des igned , a neural network model that incorporates a fabric databank predicts its propert ies.
Zubzanda el ([/. 25, Dhingra el ([1. 2(,. and Mahar ('/ {tI.27
have suggested the expert system coupled with a structured dynamic database for engineering des ign of fab rics. It includes llleasuring of basic mechanical and physical properti es (fabric object ive evaluation) by using two different methods: (i) Kawabata Evaluation System fo r Fabrics (KES-F) based on interaction between subjccti \ el y established hand va lues and objec ti vely eva luatcJ low-stress mechan ical properties or fabrics. and (i i) Fabric Assurance by Si mple Testi ng (FAST) instruments de~igned to predict tailorability performance and appearance of garmen ts and de\'eloped by Coml11()Il\\'ealth Scientific and Industrial (h:;an l/.llIOI1 ,CSH<'O,. ~n;a tin g a structured dynamic
database of property-structure - proccs~ rL' lmionship!-' and corre lating fabric objecti\'e measurc ments II ith fabric qua lity and performance such as tahrw hand k and drape. di lllensional stabi I ity, garment "Iil·cdralicc and seam pucker, and clothi ng comfol1.
Matsuo and Suresh2x have rev iewed the plllKlilh>. underlying the design of fabrics ami prol1mLd compu ter-assisted tOla l-material -desi gn sv"tCIl1 t"
woven appdrel fabrics.
.',2 Art ilkial i\clIrai Networks (AN:\ )
Artificial neural networks (A NN) present an attractive alternativc for prcdicting modelin g. \ neural network is a massive ly parallel di'lribu lCd processor that has natural propen~ity for storing experimental knowledge and making it available for usc. It i<; a powerful non- linear regression algorithm and has proven to be an ideal tool to build model.., directly from non- linear data .
Art ifi cial neural network is an information processing tec hnology inspired by the studi es of brain and nervous system. ANN has following useful properti es and capabilities: uni versa l approximation (non-linea r input - output mapping), ability to learn. adaptability, ev idential response, contextual informati on, fault tolerance, uniformity of analysis and des ign , and neurob iological analogy .
3.2.1 Basic Principles of ANN
As shown in Fig. 2, an ANN is composed of arti ficia l neurons which are the processing clements. Eac" of the neurons reccives inputs, processes the inputs, and de li vers a single outp ut'i·2'i. An artificial network can be organ ized in many different ways, viz. si ngle-layer reed forward networks. multi-layer feed forward networks, recurrent networks, and lallice structures. But the major elements are: the processi ng clements, the cont ac ts among the processing clements, the inputs and thc outputs. and the we ights . Weights express the relati ve strengths given to input data before it is processed. Each neuron has an
Input layer of neurons
Hidden layer of neurons
>
Output
Fi g. ~-l'u JI~ c·on ncclcd kcd ror\\'~lId nct\\()rk \\itll () n ~ Iml llcn Ja) ~r and <l il t: output I"y~r
320 INDI AN J. FIBRE T EXT. RES ., SEPTEMBER 2002
act ivation va lue that is ex pressed by summing the input va lues multiplied by their weights. The activation value is translated to an output by going through a transformation fun cti on (activation funct ion). The output can be relatcd in a linear or nonlinear manner or via a threshold va lue. The activation function should be continuous and differentiable. One of the popular activation functions, called the sigmoid func tiol1 , is given below:
Y = 1/(1 +e-Y)
Learning is the process by which the free parameters (wcights and threshold va lue) of neural network are adapted through a continuous process of simul ation by the environment in which the network is embeddec . ANN learns from hi stori ca l cases. The learning produces the required values of the weights which make the computed outputs equ al/close to desired outputs. Fig. 3 shows thc development process of ANN.
Common learning algorithms are: error correction learning, Boltzmann learning, Hebbian learning, Thorndike's law of effect, and competitive learning9
•
The value of ANN includes its usefullness for pattern recogllltlon, learning, class ification , generali zation , abstraction, and interpretation of the incompletc and noisy inputs. Systems that learn are more natural interfaces to the real world than the systems that must be programmed, and the speect considerations indicate to take the advantage of parallel proccss ing implementations.
3.2.2 ANN Application in Fabric Designing
Fan and Hunter30 used neural network model in their worsted fabri c ex pert system (WOFAX) for predicting fabric properties based on fibre, yarn and fabr ic constructional parameters. In thi s model, the error back propagation algorithm with bipolar sigmoid activation function for hidden layer and binary sigmoid activation functi on for output layer was used. Eva luation of model shows good predictability of fabric performance.
Besides fabric engineering, the ANN has been successfully applied in yarn manufacturing, testing and quality control and also in many other areas. Rajmanickam et al. 31 have analyzed four modeling methodolog ies, namely mathematical , empirical, computer simu lation and ANN, for predicting the strength of air-jet spun yarn . They conc luded that the ANN model is best suited for predicting yarn tensile properties as compared to other models.
.-------~ Collect data I ..
Separate into tra ining and test sets I ~
Define network structure I J
Select a learning algorithm I t
. I Set parameter va lues I ~
I Transfer data to network inputs I ~
"--+1 Start training and determ ine and revise weights I ~
I Stop and test I
I Im plementation I
Fig. 3-Development process 0 [' ANN
Zhu and Ehtridge32 studi ed the multiple regress ion model for pred icting yarn irregularity and showed that the ANN has better predictability than multi-linear regression . Based on AFIS measurements to predict yarn irregularity, five network con figurations were selected. They were trained on 150 ex perimental data and tes ted on 27 data. The R2 value (coefficient of determination) between expected and predicted ya rn irregularity is 0.79-0. 8804.
Van Langenhove et al. 33 have reported the use of ANN to assess the set marks using fabric33 image processing for surface characterization. Ramesh et al. 3~ have found ANN approach better than other methods for the prediction of yarn tensil e properties. Sett et a1. 35
. 36 have app lied self-organi zing AN for fault detecti on and wear in carpets.
ANN is trained and put in cascade to predict yarn properti es using the fibre propert ies and machine parameters37
. Cheng and Adams3x have used HVI test results to train ANN to predict yarn strength from fibre properties. Shou Tsai et al. 39 have applied ANN to classify fabric defects using imag ing technique. Barrett et al.~o have used ANN to both classify the fabric type and the number of pli es being sewn with 97.6% accuracy . Error back propagation training algorithm ANN has been used to objectively evaluatc seam pucker rating4 1 and a good corre lationship was found between objective and subjective pucker ratings.
BEHERA & MU1TAGI : ENG INEERING DES IGN OF WOVEN FABRICS--A RECENT APPROACH 32 1
The burstin g strength of cotton plain knitted fab ri cs is predicted before manufacturing using intelli gent techniques of neural network and neurofu zzy approaches42
. Among many parameters that affect the fabric burstin g strength , the fabric weight , ya rn breaking st rength, and yarn break ing elongation are used as input elements for the predi ct ions . In this research , both the multi-layer feed -forward neural network and adapti ve net work-based fu zzy inFerence system, a combinat ion 01' a radi al basis neural network and the Sugeno-Takagi fuzzy system are studi ed .
To determine the tota l hand value of knitted fab ri cs, fuzzy theory and neural networks were used by Park el al. 43
. In one method, the neural network system trained with a back propaga ti on algorithm performs fun ctional mapping between mechanical properties and the resulting total hand va lues of the fu zzy predicting method. In the second method, a fuzzy neural network system uses the fu zzy membership fun cti on, weighted factor vector, and error back propagation algorithm. The principal mechani cal properties of stretchiness, bulkiness, fl ex ibility, distortion , we ight , and surface roughness of the knitted fabri cs are correlated with ex perimentally determined Kawabata total hand values and Fuzzy transformed overall hand values . Fuzzy and neural networks agree better with the subjec ti ve test results than the KES-FB system.
ANN is es tab li shed as a powerful and reliable too l to predict the complex non -lineariti es in fabric interac tions. In almost all ANN appli cations, the error back propagation learning algorithm has most successfull y been employed.
3.3 Neural Ne!works and Expert Systems
Whil e in some cases ANNs can perform task berter/faster than ES, in most in stances the two technologies are not in competition and may even co mplement each other. In pri nci pie, the ES represents a log ica l and sy mboli c approach , whereas ANN lI ses numeric and assoc iati ve process ing.
ES performs reasoning by pre-establi shed rules for a well-defined and narrow domain . They combine knowledge base of ru les and domai n speci fic fac ts with information from the user about problems. Ideally, the reasoning can be explained and the knowledge bases can be easil y modifi ed, independentl y of the inference engine, as new rules become known. They are especially good for closedsystem appli ca tions where the inputs are literal and
prec ise, and lead to logical outputs. They are useful for interacting with the user to defiroe a spec ific problem and to bring in the facts peculiar to prob lem bein g solved. The major limitation of ES IS
l-..:nowledge acqui siti on. ANN relies on training data to the system.
Knowledge is represented in numeric weights and therefore the rul es and the reasoning process are not easily ex plainable. ANN can be preferabl e to ES when rul es are not known either because it is too complex or no human expert is ava il ab le. If training data can be generated, the system may be able to learn enough information to fun ction as an expert system and thus faster and easier to maintain. The data-driven property of ANN allows adj ustment of changing environments and events. The ANN analyzes the data sets to identify patterns and relationships that may subsequentl y lead to rule for ES .
Complementary nature of ANN and ES allows novel applications and solutions to the more complex problems when two technol og ies are combined. In the embedded system confi guration , the ES and A are integrated components of the sa me system , where ANN could represent the knowledge base impl icitly as connection weights so that the weights repre en t branches in the logic of the rul e base so that the lines of the reasoning can be exp lained.
4 Conclusions The fabric engineeri ng is still largely based on
experience and tri al and error methods. The designi ng process can not yet be completely expressed in algorithmic form . Knowledgeab le ex perts to provide design principle heuri sti cs are limited. Development of ES will be advantageous in dec ision making to provide assistance in fab ric design fornwl ation . System has to be developed to prov ide sc ien ti fic databases, over-a ll structure-functi on relationship, optimi zat ion procedures, suitable computer algorithm and standardi zation of these algorithms.
Compared to modeling fro m first principles and other techniques, ANN can be a powerful tool to model the non-lineariti es and complex iti es in volved in predictions of fabric properti es. App li cation of fabric objecti ve measurement technology enables quantitative spec ifi cati ons of fabri c properti es and performance, whi ch permits rational app li cation of engineering principles and optimization of propert ies. This database of information on fibre and fabric properties can be used to train A N. It will enable to
322 INO IAN J. FIHRE Tl:Xr. RES ., SEP'T'EMBER 2002
pred ict the properti es of the fabri c to be dc:-.i gncd and help in optimi zation of design process ,
The ES can be integrated with A to advice dnd guide the fab ri c desig ner. Thi s integrated dppro(1ch will improve the design efficienc y, flexibility and quickcn the design optimi zation process.
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