Automated Selection of Steam Pressing Regimes for Blended Cloth Items by Fuzzy Modeling
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Fibre Chemistry, Vol. 52, No. 3, September, 2020 (Russian Original No. 3, May-June, 2020)
AUTOMATED SELECTION OF STEAM PRESSING REGIMES FOR BLENDED CLOTH ITEMS BY FUZZY MODELING V. V. Sigacheva
UDC 687.023
Mixed cloths of natural and synthetic fibers are most often used to fabricate garments. Because steam pressing regimes according to standards and manufacturer’s data for some materials or others [1, 2] differ in steam temperature, flow rate, and pressure, these parameters must be considering for pressing items and designing adaptive control systems. Various manufacturers recommend pressing temperatures for the same fiber that differ by 30 °C and more. Therefore, the selection of a steam pressing temperature for mixed cloth is rationally evaluated using fuzzy modeling. Steam pressing of garments can produce a large variety of bulk item forms.
Fuzzy modeling to select temperature regimes as a function of cloth composition is an algorithm for producing clear outputs based on unclear conditions. It consists of several steps. The first and basic step, on the basis of which all other fuzzy output procedures are performed, is the compilation of a system (base) of fuzzy production rules in which the conditions and outputs of separate rules are formed as fuzzy statements regarding input and output linguistic variables [3]. Definition of the input linguistic variable is a rather complicated problem because of the multi-component nature of blended fibers. Therefore, the ratio of pressing temperature for cloth of each fiber to the smallest recommended temperature for fibers included in the mix of suit and jacket fabrics is proposed to be used as the input variable based on an analysis of all data for recommended steam pressing (SP) temperatures [4]. As a result, a numerical scale of coefficients (K) was obtained and transformed into the input linguistic variable composed of factors K = {Kn, Ks, Kb}, which varied in the range 1-2.5 with piecewise-linear functions μ(K) varying from 0 to 1. The output parameter was taken as the SP temperature “Temp,” which was transformed during the fuzzy modeling into a linguistic variable varying from 90 to 250 °C. Figure 1 presents the function obtained by modeling results in MATLAB Fuzzy Logic Toolbox. Figure 1 shows that the SP temperature regimes had three parts with practically constant temperatures. The first and left part corresponded to fabrics with low softening temperatures, for which pressing with an iron heated to ~100 °C was adequate. The second horizontal part included fabrics containing fibers with SP temperatures from 130 to 170 °C. Practically all suit and jacket fabrics fell in this part. The effect at lower SP temperatures was achieved by increasing the steam flow rate and pressing time. This part of practically constant temperature should be used to develop new types of fibers for jacket and suit groups. The third part referred to heat-resistant fabrics. However, increasing the temperature above 220 °C for SP made no sense. Thus, fuzzy modeling with large uncertainties
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