scholarly journals Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ismail Hossain ◽  
Imtiaz Ahmed Choudhury ◽  
Azuddin Bin Mamat ◽  
Abdus Shahid ◽  
Ayub Nabi Khan ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Expert System ◽  
Prediction Model ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Fuzzy Expert System ◽  
Knitted Fabrics ◽  
Bursting Strength

The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES) is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties.

Fuzzy Knowledge Based Expert System for Prediction of Color Strength of Cotton Knitted Fabrics

2016 ◽  
Vol 11 (3) ◽  
pp. 155892501601100
Author(s):  
Ismail Hossain ◽  
Altab Hossain ◽  
Imtiaz Ahmed Choudhury ◽  
Abdullah Al Mamun
Keyword(s):  
Expert System ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Parameters Optimization ◽  
Model Parameters ◽  
Color Strength ◽  
Knitted Fabrics ◽  
Knowledge Based ◽  
Study Laboratory ◽  
Fuzzy Knowledge

The present study is intended to develop an intelligent model for the prediction of color strength of cotton knitted fabrics using fuzzy knowledge based expert system (FKBES). The factors chosen for developing the prediction model are dye concentration, dyeing time and process temperature. Besides, such factors are nonlinear and have mutual interactions among them; so it is not easy to create an exact correlation between the inputs variables and color strength using mathematical or statistical methods. In contrast, artificial neural network and neural-fuzzy models require massive amounts of experimental data for model parameters optimization which are challenging to collect from the dyeing industries. In this context, fuzzy knowledge based expert system is the most efficient modeling tool which performs exceptionally well in a non-linear complex domain with lowest amount of trial data like human experts. In this study, laboratory scale experiments were conducted for three types of cotton knitted fabrics to verify the developed fuzzy model. It was found that actual and predicted values of color strength of the knitted fabrics were in good agreement with each other with less than 5% absolute error.

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meysam Azimian ◽  
Mahdi Karbasian ◽  
Karim Atashgar ◽  
Golam Kabir
Keyword(s):  
Expert System ◽  
Fuzzy Inference ◽  
Real Life ◽  
Fuzzy Model ◽  
Fuzzy Expert System ◽  
Inference System ◽  
Content Type ◽  
Reliability Centered Maintenance ◽  
Expert Team ◽  
Effective Decision Making

PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

Fuzzy Modeling of the Magnetostrictive Hysteresis for TbDyFe Actuator

2005 ◽  
Vol 475-479 ◽  
pp. 3315-3318
Author(s):  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Hui Bin Xu ◽  
Cheng Bao Jiang ◽  
Hu Zhang
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Prediction Model ◽  
Maraging Steel ◽  
Kalman Filtering ◽  
Fuzzy Clustering ◽  
Fuzzy Modeling ◽  
Fuzzy Identification ◽  
Reliable Tool ◽  
Mechanical Properties Prediction

A mechanical properties prediction model for cobalt-free maraging steel was built upon the experimental data by fuzzy identification method. A method of fuzzy identification based on fuzzy clustering and Kalman filtering is proposed. The results showed that good correlations between the predicted result and the experimental data. The technique proposed could be served as a reliable tool for cobalt-free maraging steel mechanical properties control and design.

scholarly journals Modelling the Effect of Resin-Finishing Process Variables on the Dimensional Stability and Bursting Strength of Viscose Plain Knitted Fabric Using a Fuzzy Expert System

Tekstilec ◽  
2021 ◽  
Vol 64 (2) ◽  
pp. 119-135
Author(s):  
Ismail Hossain ◽  
◽  
Md. Hasib Uddin ◽  
Altab Hossain ◽  
Mohammad Abdul Jalil ◽  
...  
Keyword(s):  
Expert System ◽  
Fuzzy Expert System ◽  
Textile Fibre ◽  
Curing Time ◽  
Process Variables ◽  
Significant Deterioration ◽  
Bursting Strength ◽  
Finishing Process ◽  
Single Jersey ◽  
Resin Treatment

The application of cross-linking resin is an effective method for improving and controlling dimensional sta¬bility, such as the shrinkage of viscose single jersey knits. However, such treatment often leads to a significant deterioration in the bursting strength of treated fabrics. In this regard, resin treatment using a softening agent can be an additional potential solution for retaining the bursting strength of treated fabrics. Resin treatment is one kind of chemical finishing process that inhibits cellulosic textile fibre swelling during wetting, provides fibre resistance to deformation and prevents shrinkage. The key objective of this study was to model the effect of resin-finishing process variables for predicting the shrinkage control and bursting strength of viscose single jersey knitted fabrics. The MATLAB (Version 8.2.0.701) fuzzy expert system was used to model the optimum resin and softener concentrations, as well as the best curing time for the prediction of maximum shrinkage control with a minimum loss in fabric bursting strength. The optimal process variables were found to be a resin concentration of 75 g/l, a softener concentration of 45 g/l and a curing time of 225 seconds. The fuzzy expert model developed in this study was validated using experimental data. It was found that the model has the ability and accuracy to predict fabric shrinkage and bursting strength effectively in the non-linear field.

A FUZZY EXPERT SYSTEM FOR SOLVING REAL-OPTION DECISION PROCESSES

2001 ◽  
Vol 06 (02) ◽  
Author(s):  
C.A Magni ◽  
G. Mastroleo ◽  
G. Facchinetti
Keyword(s):  
Expert System ◽  
Real Option ◽  
Decision Processes ◽  
Fuzzy Expert System

Contribution to the Research and Development of Radiation Chambers in Steam Reforming. Problem-Oriented Application of a Fuzzy Expert System

1992 ◽  
Vol 57 (10) ◽  
pp. 2125-2134 ◽  
Author(s):  
Petr Stehlík ◽  
František Babinec
Keyword(s):  
Carbon Dioxide ◽  
Mathematical Model ◽  
Expert System ◽  
Service Life ◽  
Research And Development ◽  
Steam Reforming ◽  
Methane Conversion ◽  
Chemical Processes ◽  
Fuzzy Expert System ◽  
Present Contribution

An application of a fuzzy expert system intended for estimating some parameters of steam reforming can also be one of the examples of an ever increasing utilization of expert systems in practice. The present contribution deals with the method making use of a verified mathematical model for simulating thermal chemical processes in reforming furnace radiation chamber in order to create knowledge base. This base includes linguistic values of selected independent and dependent variable quantities. Examples given illustrate an evaluation of dependent variable quantities (methane conversion into carbon dioxide and monoxide, reaction tube service life) by means of the said expert system based on queries.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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