Predicting the Seam Strength of Notched Webbings for Parachute Assemblies Using the Taguchi's Design of Experiment and Artificial Neural Networks

Önal, Levent; Zeydan, Mithat; Korkmaz, Mahmut

doi:10.1177/0040517508099921

Cited by 22 publications

(14 citation statements)

References 21 publications

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“…The validation of the Taguchi's method was not ensured in most of the earlier studies Kumar et al, 2006aKumar et al, , 2006bKumar et al, , 2006cSalhotra et al, 2006) and the relative difference was observed to be very high, which was up to 42% in few of the studies (Onal, Zeydan, Korkmaz, & Meeran, 2009). To ensure the efficiency of prediction of Taguchi's method, eighteen different multilayered fabrics were validated in this study.…”

Section: Validation Of Taguchi's Methodsmentioning

confidence: 96%

Analysis of thermal properties of multilayered fabrics by full factorial and Taguchi method

Shabaridharan¹,

Das²

2013

The Journal of The Textile Institute

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The present paper reports the thermal and evaporative resistances of multilayered fabric ensembles meant for cold weather conditions. A three-layered structure was used to simulate the multilayered fabric ensemble. Knitted, through air-bonded nonwoven and coated fabrics were used as inner, middle and outer layer fabrics, respectively. Inner and middle layer fabrics were varied in mass per unit area in three different levels. Three different coated fabrics, namely, polyester polymer-coated fabric and two different polytetrafluoroethylene-coated fabrics which vary in pore size and porosity were used in outer layer. Taguchi's L 9 orthogonal array, meant for three variables and three levels were used to form nine different multilayered fabric ensembles. Signal to noise ratio was studied to predict and optimize the thermal properties of multilayered fabric ensembles. Full factorial design was used to study the effectiveness of predicting thermal properties of multilayered fabrics using Taguchi's approach. Regression equations were developed and contour plots were drawn to analyse the effect of chosen parameters on thermal properties of fabrics. In both the methods, the percentage contributions of each factor for thermal and evaporative resistances of multilayered fabrics were studied using sum of squares method. It was found that the Taguchi's method reduces the number of experiments significantly and predicts the thermal properties of multilayered fabrics with good prediction performance. Mass per unit area of inner layer was found to have no significant effect on thermal and evaporative resistances of multilayered fabric ensembles. Mass per unit area of through air-bonded nonwoven fabric was found to be the most significant parameter in determining the thermal resistance of multilayered fabrics. Mass per unit area of middle layer and pore size of coated fabrics were found to contribute almost equal level in deciding the evaporative resistance of multilayered fabrics.

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Section: Validation Of Taguchi's Methodsmentioning

confidence: 96%

Analysis of thermal properties of multilayered fabrics by full factorial and Taguchi method

Shabaridharan¹,

Das²

2013

The Journal of The Textile Institute

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“…After that, the final weights and biases can be arrived at. The output value will be close to the target value [23,24]. The criterion for the error function adopted in the network is as follows: (7) where T j and Y j respectively represent the target output and the predicted output of jth output neuron in the output layer.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Recognition of fault process parameters for diameter uniformity variation in melt spinning

Kuo

Huang

et al. 2014

Fibers Polym

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This study focused on the abnormal condition of diameter variation in melt spinning, and attempted to recognize the machine processing parameter that deviates from the set value. The experiment used polypropylene as the experimental material. The machine processing parameters included nine factors, including temperatures in three extruder barrel sections, temperature of metering pump, die temperature, spinneret temperature, speed of an extruder screw, speed of metering pump and speed of take-up roll. The biaxial laser sensor measured the yarn diameter instantly, and the optimum parameter combination for minimum diameter variation was obtained by Taguchi method and analysis of variance (ANOVA). The degree of influence of various processing parameters on the diameter variation was determined. The optimum parameter combination was used as parameter setting value, and the processing parameters of various factors were changed for experiment, so as to obtain the signals of abnormal condition. Two methods were used for feature extraction. The minimum entropy of wavelet transform signal was used as eigenvector, and two features were selected by analyzing the statistical process control chart, which are skewness and kurtosis. The abnormal processing parameters and normal condition were recognized effectively and accurately by using the three eigenvalues and back-propagation neural network. With enough training samples, the recognition success rate was as high as 100 %. For complete abnormity diagnosis of melt spinning machine, a two-factor classification process was established by using the single-factor classification result and backpropagation neural network. The experimental results proved that the proposed method can recognize various abnormal conditions successfully and effectively.

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“…Therefore, both models were effectively predicting the seam performance of woven fabrics. Onal et al, (2009) studied the effect of factors on seam strength of webbings made from polyamide 6.6 which were used in parachute assemblies as reinforcing units for providing strength by using both Taguchi's design of experiment (TDOE) as well as an artificial neural network (ANN), then compared them with the strength physically obtained from mechanical tests on notched webbing specimens. It was established from these comparisons, in which the root mean square error was used as an accuracy measure, that the predictions by ANN were better predictions of the experimental seam strength of jointed notched webbing in accuracy than those predicted by TDOE.…”

Section: Applications To Fabricsmentioning

confidence: 99%

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

Chiu¹,

Fun²,

Ip³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Predicting the Seam Strength of Notched Webbings for Parachute Assemblies Using the Taguchi's Design of Experiment and Artificial Neural Networks

Cited by 22 publications

References 21 publications

Analysis of thermal properties of multilayered fabrics by full factorial and Taguchi method

Analysis of thermal properties of multilayered fabrics by full factorial and Taguchi method

Recognition of fault process parameters for diameter uniformity variation in melt spinning

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

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