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Volume 7, Issue 2, Fall 2011 Approach Development for Surveillance Assistance: Spinning Mill Application Walid Chaouch*1,2, Mohamed Ben Hassen1 and Faouzi Sakli1 Textile Research Unit, High Technology Institute, Ksar-Hellal, Tunisia ingenieurtextile@yahoo.fr 1 Textile Research Unit, High Technology Institute, Ksar-Hellal, Tunisia 2 Laboratoire de Physique et Mécanique Textile Université de Haute Alsace, Mulhouse cedex, France ABSTRACT Monitoring the state and the efficiency of cleaning and carding equipment is a goodmethod of determining if the equipment is operating properly to get a better understanding aboutthe actual machine performance. The paper attempts to develop and prove different empiricformulas to determine the partial efficiency index of spinning machines (machines of blowroomand cards) such as: Cleaning efficiency, Nep increase efficiency, Nep removal efficiency, Percentshort fiber efficiency, Tenacity efficiency and Length efficiency. The implicit goal is to be able toevaluate the global efficiency index of various spinning machines by taking into account thesedifferent partial efficiency indexes. In addition, we proposed establishing an approach "DynamicMethod put in Scale (DMS)" which is an attempt to combine two techniques: objective reasoningand mathematical formulas to solve many complex system problems, especially when a system isdifficult to model and control by a human operator or expert. We used the DMS to determine theglobal index efficiency, in order to solve a spinning problem by evaluation of the cleaners andsimulating the efficiency for the different process stages during the treatment.Keywords: Spinning, Blow room, Card room, Global efficiency, Dynamic Method with Scale(DMS).I. Introduction more intensively in order to achieve an equal visual level of cleanliness [1-8]. Since the industrial revolution and the Consequently, many complicationsfactory systems for the manufacture of can occur during the spinning. Therefore, itcotton yarns and fabrics more than 200 years is important to understand in details whatago, there has been need to describe the happens throughout the machines efficiencyquality of the raw material and the global in order to improve the quality of yarn [2, 9-efficiency of machines. In the meantime, 10]. So, the spinner must be in a position tocotton production had been increasing follow the state of machines, particularlyconsiderably since mechanical harvesting with the intention of forecasting thehad been introduced, and the production of spinnability of the fibre and the specifiedginning machines and spinning plants was yarn quality without waiting for results onincreased accordingly. Thus, the spinner had finished products [6].to clean the cotton more aggressively andArticle Designation: Refereed 1 JTATM Volume 7, Issue 2, Fall 2011
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Although, various methods found in processed and the number fuzzy variablesthe literature are very interesting, the global associated with each parameter. One ofefficiency index of various spinning consequences of this subjectivity is the riskmachines are not fully understood [3-6, 11- of no-convergence of the defuzzification12]. Therefore, these previous studies are algorithm the undefined unforeseen measureconsidered insufficient and unable to give a [15].total description of machine results because We need to develop another method toof the other parameters such as raw emulate our ability to reason and make usematerials have not been assessed [12]. For of approximate data to find precisesuch high levels of complexity, fuzzy logic solutions. In this paper, we present ancould be used. Indeed, the fuzzy logic approach "Dynamic Method with Scaleprovides an alternative solution to non-linear (DMS)" based on the techniques of fuzzycontrol since it simulates reality better. Non- subsets. The DMS makes it easier tolinearity is handled by rules, membership conceptualize and implement controlfunctions, and the inference process systems and enable engineers to configureresulting in improved performance, simpler systems quickly without extensiveimplementation, and reduced design costs experimentation. This method was applied[13-16]. to determine the global efficiency index in Nevertheless the fuzzy logic suffer order to estimate the behaviour of cottons atfrom some limitations with respect to the cleanliness for a better follow-up of thesensitivity. The primary difficulty in using cleaners, and to avoid complaints formulatedfuzzy logic is that the programmer must first in the reception of the finished article. Forthoroughly understand the intricacies of and this, it is necessary to determine the inputbe able to precisely define a problem, and variable (partial efficiency index) and outputthen must be able to evaluate and fine-tune variable (global efficiency index).the results [17]. So; fuzzy logic systems arenot entirely analytic and thus involve fine- II. Presentation of the machinestuning, are unstable. Because of the rule-based operation, although defining the rule Spinning industry is composed ofbase quickly becomes complex if too many different rooms (in our study, we areinputs and outputs are chosen for a single interested of the blowroom and card room).implementation since rules defining their Blowroom installation (each cleaning line)interrelations must also be defined [14]. The contains three cleaners connected in series tonumber and complexity of rules depends on obtain the desired cleaning result (Figure 1).the number of input parameters that are to beArticle Designation: Refereed 2 JTATM Volume 6, Issue 4, Fall 2010
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Figure 1. Presentation of the machines Pot: Stocking of the card sliver The cleaners MSR, Axiflo and CVT4, while increasing short fiber content andeliminate impurities and increase the neps. Carding and combing reverse this bynumber of neps in the cotton (Cotton fiber removing a percentage of the short fibersneps are defined to be entanglements of and neps. The basic purpose of this study isseveral fibers, are not a genetically to use the AFIS (To measure the neps weoccurring property in seed cotton). Cotton used the USTER® AFIS neps (cnt/gr)) andprocessing machines that mechanically work the HVI to improve performance of thethe cotton fiber from bale to yarn are spinning process. Since the variousdesigned with the intent of minimizing fiber mechanical processes modify the state of thedamage. Thus the material is passed over fibers, we must first determine the differentseveral rolls, each of them running at a partial efficiency index of spinninghigher speed and having finer clothing. machines.Passage from one cleaner to the next takesplace in the transfer mode ensuring a gentle III. Determination of the global indextreatment of the fibers. Due to the increasingspeed the material is subjected to ever- The raw materials and thehigher centrifugal forces, i.e. the cleaning technological process influenced the finaland opening efficiency increases from yarn quality. With machine settings andcleaner to cleaner. Impurities can be speeds optimized, a comparison of the fiberextracted from the fiber stock at the mote properties of stock-in compared with stock-knives, provided on the periphery of the out provides valuable information forrolls [11]. achieving further optimization. However the Nevertheless, opening, cleaning and yarn quality is improved when the cotton isblending equipment shorten the staple length easy to clean (good cleanability) and whenArticle Designation: Refereed 3 JTATM Volume 6, Issue 4, Fall 2010
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the cleaning efficiency of the machine is cleanability (C)) andor if the machine has aimproved. Therefore, the spinners are lower cleaning efficiency. However thegenerally confronted to the problems of degree of cleaning Dcl is improved when theevaluation of the state and the efficiency of cotton is easy to clean (good cleanabilitytheir machines without waiting for finished (C)) andor when the cleaning efficiency ofproducts result. The variation of the cleaning the machine is improved (equation 1) [12,results do not limit itself to the influence of 18].the impurities elimination efficiencyaccording to the machine’s types and work Dcl 10 C T Mclconditions such as quality of card’s clothing, Equation 1 Tispeeds, regulations, etc. Thus, it is very Dcl Ttotal 100interesting to determine characteristic thatinforms on the easiness or the difficulty to Ti: Percentage of eliminated impurities.rid trash from cotton in order to foresee his Ttotal: Percentage of impurities at the input offuture behavior in cleaning and offer a machine.preview on the cleaning efficiency ofmachines, to predict the results of cleaning “High cleaning efficiency” is regardedprocesses [12]. as a positive attribute, so, 100% say a goal We needed the global index to give an (100%) of zero resultant impurities (Ti =idea about the state of functioning of Ttotal), therefore 0% announces that there isdifferent machines. This global index is no elimination of impurities, so Ti = 0%)influenced by several fiber properties and [18]. Another important factor is the trashoften solved by considering the different content T of the cotton at the input ofpartial efficiency index of spinning machine, the degree of cleaning for dirtymachines such as: Cleaning efficiency, Neps cotton is more elevated than clean cotton onincrease efficiency, Neps removal the same machine and in the sameefficiency, Percent short fibers efficiency, conditions.Tenacity efficiency and Length efficiency. III-2: Neps increase efficiency and NepsIII-1: Cleaning efficiency (Mcl) removal efficiency (MNi and MNr) Raw cotton contains various One of the main problems of yarnimpurities as leaf, bark, and seed coat quality is its neppiness. Neppinessparticles. Impurities content from bale to influences the appearance of yarn, andsilver should decrease through the opening. consequently the quality of fabricsIndeed, the requirements of sliver quality especially dyeing uniformity [2,11-12].impose that the cotton must be intensively Neps are created by the mechanical handlingcleaned during ginning, spinning mill and and cleaning of the cotton fiber, nepscarding [2-8]. On the other hand, the amount increase throughout the ginning, openingof these contaminations is useful and cleaning process. Although there is ainformation for finding more efficient decrease during carding and combing, bothcleaning processes and predicting the quality are designed to align fibers and removeof the finished products. imperfections such as neps [19-21]. It is a The degree of cleaning Dcl is recognized, however, that certain cottonsinfluenced not only by the lint tend to nep more easily than others whencharacteristics in intermediate products, but they are mechanically handled underalso by the mechanical handling of the fiber, identical conditions. Degree of increasingbecause cotton has a lower degree of (blowroom) of neps DNi, can be defined bycleaning Dcl if it is difficult to clean (poor the following formula [21]:Article Designation: Refereed 4 JTATM Volume 6, Issue 4, Fall 2010
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CN i Equation 2DN i MN i Nbn ep so u t Nbn ep sinDN i 100 ; Nbneps out ≥ Nbneps in Nbn ep sinCNi: increase ability of nepsCNr: removal ability of nepsNbneps in: number of neps per gram at the input of machineNbneps out: number of neps per gram at the output of machine In the case of blowroom we note an neps (Nbneps out = Nbneps in). According toincrease of neps. The Degree DNi gives an Uster [22], it is possible to classify theidea on the neps increasing, thus, 0% of DNi degree of neps increasing, determined by(is regarded as a positive attribute) equation (2), as indicated in Table 1[21].significant zero increase in correspondingTable 1. Classification of DNi% Class (%) Interpretation ≥ 80 Very bad 60-80 Bad 40-60 Average 20-40 Good ≤ 20 Very good Thus, the more cotton has a tendency less there is a formation of neps. So DNi istoward the formation of neps (CNi is big) the reduced (blowroom) [21]. Degree ofmore DNi is raised. But the better the removal (card room) of neps DNr, can beconditions of the machine (MNi is big), the defined by the following formula [21]: DN r MN r CN r Equation 3 Nbnepsin Nbneps outDN r 100 ; Nbneps out ≤ Nbneps in NbnepsinMNi: neps increase efficiencyMNr: neps removal efficiency In the case of card room we note a Nbneps in). According to Uster [22], it isdecrease of neps, 100% say a goal (100%) possible to classify the degree of nepsof zero resultant neps (0 announces that removal, determined by equations (3), asthere is no elimination of neps, so Nbneps out = indicated in Table 2 [21].Article Designation: Refereed 5 JTATM Volume 6, Issue 4, Fall 2010
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Table 2. Classification of DNr% Class (%) Interpretation ≥ 90 Very good 80-90 Good 70-80 Average 60-70 Bad ≤ 60 Very bad The more the neps are easily To give a total description, it would beeliminated (CNr is big) and the card is good interesting to analyze the changes ofbecause it eliminates more neps (MNr is big), physical properties for the successivethe more DNr is better. Therefore, yarn spinning process machines. In order tonepiness is a result of quality, processed raw express the degree of physical properties Dimaterials and the technological process [21]. (percent short fibers efficiency (Dfs),Although, the global efficiency depends tenacity efficiency (Dte) and lengthessentially on the cleaning and neppiness efficiency (Dle), the theoretical relationresults, we have to do a complete global between the cotton ability and machineefficiency study of spinning machines efficiency by analogy with the equation (1),(machines of blowroom and cards) to know concerning the elimination of impurities waswhat physical properties modification could derived.arise to the cotton. These physical propertiesare translated at the tenacity, short fibre The following formulas can be defined:count and length. Dfs Qfs Mfs Equation 4III-3: Percent short fibers efficiency, Tenacity efficiency and Length Dte Qte Mte Equation 5 efficiency (Mfs, Mte and Mle) Dle Qle Mle Equation 6 Despite technology, rigorousmechanical processing remains a necessity Dfs: Degree of percent short fibers.in order to successfully open and clean, Dte: Degree of tenacity.because the removal of impurities and neps Dle: Degree of length(case of the card room) is usually Qfs: Quality index of percent short fibers.accompanied by shortening of the length Qte : Quality index of tenacity.distribution, fibre breaking, fibre failure and Qle: Quality index of length.other damages (increasing of short fibers,decreasing of tenacity and decreasing III-3-1: The degrees (Dte, Dle and Dfs)length) [9-10]. Therefore, all mechanicalprocessing is a compromise between quality These parameters provide measures ofimprovement (disentanglement and cleaning degradations in fiber quality during theof fibres) and damage, which is defined as cleaning treatment. The result of thedegradations of fibre’s quality. This is physical property variations, defined by Dinecessary in order to establish a spinning (Percent short fibers, tenacity and length),mill’s benchmark data. can be calculated by the following formulas: SFCinput Dfs SFCoutput 100 ; SFC output ≥ SFC input SFC input (SFC output): Percentage of short fibers at the input (output) of machine.Article Designation: Refereed 6 JTATM Volume 6, Issue 4, Fall 2010
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“High Degree of percent short fibers” is obtained. Also, the greater the efficiency ofregarded as a positive attribute, so 100% of machines (Mi) and the quality of rawDfs significant zero materials (Qi) are bad, the more cotton has aincrease in corresponding short fibers (SFC tendency toward the formation of shortinput = SFC output). Therefore, more the fibers (Dfs is low) after cleaning (EquationDfs is low, the more short fibers are 4). Tenacity (length) input IV. Principle of “Dynamic Method put Dte ( Dle) Tenacity (length)output 100 in Scale (DMS)”Tenacity (length) output ≤ Tenacity (length) IV-1: General Presentationinput DMS can control any types of linearIt becomes 100% when there is no decrease systems that would be difficult or impossiblein corresponding tenacity, or when Tenacity to model mathematically. This method(length) output = Tenacity (length input. When enables engineers to make use ofthe cotton has a lower tenacity, Dte, (or information from expert human operatorslength Dle), if it is difficult to clean or the who have been performing the taskmachine has a lower efficiency (Mi) manually. The DMS method can be(Equations 5 and 6). decomposed in three steps: classification, inference and valorisation.III-3-2: The qualities (Qte, Qle and Qfs) The basic idea consists to increasing the fuzzy subsets permitting to achieveGive an idea about the future behavior of the stages of classification and valorisation. Thisraw material (Qi: Tenacity, length and increase permits a better precision of thepercent short fibers) during the mechanical output set evolution according to input sets.treatment. Also, the using of level-headedness coefficient provides a simple way to arriveQi = R x C at a definite conclusion based on input information.R: The tenacity value in the input machineC: Cleanability IV-2: Classification Stage The more the cotton is easy to clean IV-2-1: Determine the control system(good cleanability : C is big) andor the inputtenacity is good (R is big), the more thequality (Qi) is better after cleaning. With DMS the first step is to Because all these factors usually vary understand and characterize the systemsimultaneously, it is very difficult to assess behaviour. The primary objective of thistheir individual influences on the spinning construct is to map out the universe ofprocess. In order to get meaningful results of possible inputs (classification) whilethe functioning state of different cleaners, it keeping the system sufficiently underis important to develop a method to estimate control. The classification function (Ki) is athe global efficiency index and by taking graph of the participation magnitude of eachinto account the different partial efficiency input. It associates a degree of membershipindex of spinning machines such as: factor (µ) (degree of influence) with each ofCleaning efficiency, Neps increase the inputs that are processed in order toefficiency, Neps removal efficiency, Percent define functional overlap between inputs.short fibers efficiency, Tenacity efficiencyand Length efficiency. Note: Ki Є [1, n]Article Designation: Refereed 7 JTATM Volume 6, Issue 4, Fall 2010
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n: Number of classes corresponds to the membership (µ) is determined by plugging input variable (Vi) (for example in the selected input parameter into the Figure 2, n=100) horizontal axis and projecting vertically to the upper boundary of the classificationIV-2-2: Stage of inference function (Figure 2).Inference is a process that combines real By knowing, the two classes (Ki-1(Vi) andvalues (Vi) (e.g., partial efficiency index of Ki(Vi)) and their classification functions (µcleaner machine) with stored classification (Vi, Ki-1(Vi)) and µ(Vi, Ki(Vi)) (Figure.2),function data and a degree of membership to we can determine the input scaled value Rproduce scaled input values. The degree of (Vj) as follows:R (Vj) = Ki (Vi) µ(V i , Ki(Vi)) + K i - 1 (Vi) µ(V i , Ki - 1(Vi)) Equation 7j Є [1, m]m: number of input variableVi Є [Min, Max]Min; Minimum value of input variableMax: Maximum value of input variable Figure 2. The features of a classification functionIV-2-3: Stage of valorisation m Equation 8 In the last stage the valorisation Z R(Vj ) b(Vj )combines all its inputs, by using a formula, j 1to obtain an output response Z. Indeed, theformulas use the level-headedness m: number of input variablecoefficient b(Vj), determined from theexpert human, as weighting factors todetermine their influence on the final outputconclusion. Once the input scaled valueR(Vj) and the level-headedness coefficientb(Vj) associated to every input variable aredefined, the output response Z can bedefined by the following formula:Article Designation: Refereed 8 JTATM Volume 6, Issue 4, Fall 2010
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IV- 3: Mathematical development conditions are in their domain, whereas wide triangles provide looser control (Figure 2).We suggest a general mathematicalreasoning from the DMS method to define Then, the division D was calculated by thethe equation of: following equation: Every line segment, which delimits a corresponding elementary subset. Max Min A real value (R) put in scale after D n projection Min; Minimum value of input variableIV-3-1: Presentation Max: Maximum value of input variable n: In order to apply DMS to a particular Number of classes corresponds to the inputsystem, the programmer must first make a variable (Vi) ( Figure 2). For every value Vi"classified model," a set describing the Є [Min, Max] there is a projection on ansystem and how to handle it. Making such a increasing segment and another decreasing.model involves analyzing a problem andsetting up the proper degree of membership IV-3-2: Equation of the increasingto define it, a process called valorisation. segments (ΔK)The steps in building our system consists ofgraphical triangles that can help visualize For K Є [1, n], ΔK: y = a x + bK:and compute the input-output action. Figure 2, illustrates the features of the This is the general increasing segmenttriangular classification function, which is equation (these line segments have theused due to its mathematical simplicity. same sloping because they are parallel)These functions work fine and are easy to (Figure 3).work with [17]. Indeed, narrow trianglesprovide tight control when operatingSoTherefore: a 1 DOr, y1 = a x1 + bK bK = y1 - a x1Therefore, K - 1 D Min MinbK = 0 - 1 K D D For, K Є [1, n] ΔK : x Min x Min Equation 10 y 1 K- 1 K D D DFor, x Є [ (K-1) × D + Min , K× D + Min]Article Designation: Refereed 9 JTATM Volume 6, Issue 4, Fall 2010
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Figure 3. The increasing (ΔK) decreasing segments (▼K)IV-3-3: Equation of the decreasing segments (▼K) For K Є [0, n-1]▼K: y = a x + bK :This is the general decreasing segment equation (these segments have the same sloping becausethey are parallel) (Figure 3). y2 - y1 0 1So a x2 - x1 K 1 D Min K D MinTherefore, a 1 DOr: y2 = a x2 + bK bK = y2 - a x2 So: K 1 D Min MinbK = 0 =1 K D D For, K Є [0, n-1] ▼K : x Min Min - x Equation 11 y= - +1+K+ = +1+K D D DFor, x Є [K × D + Min, (K+1) × D + Min]IV-3-4: Equation of the value put in scale after projection (R)For every value V Є [Min, Max], V - MinKΔ = E ( )+1 and DArticle Designation: Refereed 10 JTATM Volume 6, Issue 4, Fall 2010
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V - Min K▼ = E ( ) E is the integer part D KΔ and K▼ are the classes associated to the real value V Thus, the value put in scale R (V) can be calculated according to the following equation (Equation 7): V Min Min V R (V) = KΔ × ( 1 KΔ) + K▼ × ( 1 K▼) D D ΔK(V) ▼K(V) V - Min For S = , D R (V) = E (S) + 1 × S + 1 - E (S) + 1 + E (S) × 1- S + E (S) After simplification V - Min V Min Equation 12 R (V ) so R(V ) n D Max Min DMS is a very suitable method to summarizes the different partial efficiency estimate the cleaning efficiency of index of spinning machines (cleaners and machines. In accordance with equations (8 cards) and limits for each value (maximal and 12), it is possible to quantify the global value and minimal value) as well as their index of spinning machines. Table 3 level-headedness. Table 3. Different partial efficiency indices Minimal Maximal Level-Input variable Symbol Equation Room value value headedness Cleaning blowroom 0,1 2 Mcl Dcl = 10 ×C ×T × Mcl (7) 0,35efficiency Carding 2 8 0Neps increase CN i MNi DN i (8) blowroom 3 0,3efficiency MN i ,3Neps removal Carding 1 MNr DNr = MNr × CNr (9) 4 0,3efficiencyPercent short blowroom Mfs Dfs = Mfs × Qfs (10) 2 5 0,15fibers efficiency CardingTenacity Dte = Mte × Qte (11) blowroom Mte 3 4,5 0,1efficiency CardingLength Dle = Mle × Qle (12) blowroom Mle 3 4,5 0,1efficiency Carding Article Designation: Refereed 11 JTATM Volume 6, Issue 4, Fall 2010
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The coefficient attached to a partial efficiency of cleaner and even a cleaningefficiency index as its weight in the line.determination of global index involving In the present paper we determinedweighting. The basis for proposing the the global efficiency index by taking intoweighting coefficients are data on the final account the different partial efficiency indexyarn quality. So, this level-headedness of spinning machines, while using theenables us to make use of information from proposed empiric formulas, in order to get aexpert human operators (spinner) who have better understanding about the actualbeen performing the task manually. machine performance. Then, the correlation This research was carried out in between the partial and global efficiency ofSITEX Company (Industrial Society of different cleaners were determined by usingTextiles in Tunisia), thus these level- the DMS method.headedness are proposed by the expert DMS a powerful new way is used inspinner of this company. The spinners give system control and design analysis, becausemore importance to: cleaning (0.35) and the it shortens the time for engineeringrate of neps (Neps increase efficiency (0.3) development. It is, however, best applied toin the case blowroom and Neps removal systems with uncertainties:efficiency (0.3) in the case of card room) With DMS we can describe the output as athan physical properties (tenacity (0.1) , function of any number of inputs linked withshort fibre count (0.1) and length (0.1)) level-headedness coefficient. This methodThese coefficients can be exchanged provides a way to approach a controlaccording to the requirements of the problem. To make the data analysis easierspinning mill. for the operator, we developed, in the further In the following, the authors present studies, software, which enable thethe results of the mechanical processing for calculation of both partial and globaldifferent levels of mill cleaning, in order to efficiencies of every spinning machine.judge and compare the efficiency ofdifferent cleaners and cleaning lines. VI. ReferenceThereafter, in the further studies, we will tryto develop software, named EFFITEX, to 1. Krifa, M. (2001, May). Filière coton:help the operator use the DMS method and contraintes et enjeux. L’industriesupervise the state of machines. This Textile, 1331, p 23.software eases data analysis and gives the 2. Van der Sluijs, M.H.J and Hunter, L.spinner the indications needed for decision (2000). Neps in Cotton Lint. Textilemaking. Progress, p 1. 3. Leifeld, F. (1988). Importance de laV. Conclusion qualité du ruban de carde pour le comportement lors du travail et la In order to produce competing qualité du fil avec des procédés deproducts on international scale, the spinner filage non conventionnels. Internationalneed a new method at his disposal for the Textile Bulletin (Filature), 4, p. 35.assessment of the raw material and of this 4. Leifeld, F. (1992). Nettoyage du cotonmachines, optimization the spinning process, dans le processus d’égrainage ou deforecasting running conditions in the mill filature, International Textile Bulletinand engineering the required yarn quality (Filature), 3, p 4.based on the raw material data. Therefore, it 5. Leifeld, F. (1990). Développement desis very interesting to develop feature installations de nettoyage, au vu descharacteristics, which give information caractéristiques changeantes de laabout the future behavior of cotton in matière première, International Textilecleaning and gives a preview on the cleaning Bulletin (Filature), 1, p 13.Article Designation: Refereed 12 JTATM Volume 6, Issue 4, Fall 2010
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6. Artzt, P. Gresser, G and Maidel, (1995). 14. Kosko, B and Isaka, S. (1993, July). H. Influence sur la qualité de fil de la Fuzzy Logic. Scientific American, 269, teneur en impuretés végétales et de p 76. l’aptitude à l’épuration des cottons. 15. Yager, R.R. (1999). On ranking fuzzy International Textile Bulletin (Filature numbers using valuations. International et Tissage/ Maille), 2, p 14. Journal of Intelligent Systems, 14, p7. Peters, G and Söll, W. (2000). 1249. Determination of trash and dust content 16. Robinson, G.M. (1991, November 28). in yarns, Melliand International, 6, p 36. Fuzzy Logic Makes Guesswork of8. Frey, M. Schneide, U. (1989). Computer Control. Design News, 47, p. Possibilities of Removing Seed Coat 21. Fragments and Attached Fibre in the 17. Barton, J.J. (1993, April). Putting Fuzzy Spinning Process, Melliand Textilber, Logic into Focus. Byte, 18, p. 11. Int Textile Rep, 70, p 315. Chaouch, W. Ben Hassen, M. Sakli, F.9. Frydrych, I. (1992). A new approach for (2007). Evaluation of cleaning predicting strength properties of yarn. efficiency in blowroom & carding. The Textile Res. J, 62, p 340. Indian Textile Journal, p54.10. Mogahzy, Y.E.El. (1990). Roy 18. Krifa, M. Frydrych, R and Gozé, E. Broughton, JR., And W. K. Lynch “A (2002) Seed Coat fragments: The Statistical Approach for Determining consequences of carding and the impact the Technological value of cotton Using of attached fibres, Textile Research HVI Fiber Properties. Textile Research Journal, 72, p. 259. Institue, 40, p 5175. 19. Frydrych, I. Matusiak, M. Swiech, T.11. Schlichter, S and Kuschel, A. (1995). (2001). Cotton maturity and its Recent Findings on the Cleanability of influence on nep formation, Textile Cotton, Melliand Textilberichte Research Journal, 71, p. 595. (English); 76, E49. 20. Chaouch, W. Ben Hassen, M. Sakli, F.12. Leifeld, F. (1988). The influence factor (2006). Blowroom Machines and “C” of cotton in the cleaning process, Carding: Neps Increase and Neps Removal Efficiency, Melliand Melliand Textilberichte, 69, E162. International, 1, p. 22.13. Bernardinis, L.A. (1992, April 23). 21. Uster AFIS PRO, Advanced fiber Clear Thinking on Fuzzy Logic. information system, application Machine Design, 64, P46. handbook, Zellweger Uster, Inc, 2001.Article Designation: Refereed 13 JTATM Volume 6, Issue 4, Fall 2010
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