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The first of a two‐part series, this paper aims to discuss the design and development of an artificial intelligence‐based hybrid model to understand human perception of the tactile properties of textile materials and create an objective system to express those tactile perceptions in terms of measurable mechanical properties.

A forward engineering system using the Model Free Algorithm approach of the Artificial Intelligence Technique to predict the tactile comfort score is presented.

Human perception of tactile sensation is based on the weighted stimulus perceived by the human neural system.

Contribution to intelligent textile and garment manufacture.

The second of a two‐part series, this paper aims to explain the design and development of a hybrid system for reverse engineering.

A prediction engine to map the perception of tactile sensations using a neural network engine was developed. Since seventeen mechanical properties form the input ‐ and tactile compfort score is used as the output ‐ a direct reversal of the data set becomes impossible, hence, a hybrid approach was employed. The neural net is coupled with a genetic algorithm engine for the reversal process. The trained neural network acts as the objective function to evaluate the property set while the solution set is generated by Genetic Algorithm (GA) engine. Limitation of the GA and a means to overcome it is discussed. Application software based on the current research is also presented.

Human perception of tactile sensations is non‐linear in terms of the mechanical properties of textile materials.

The paper deals with reverse engineering and discusses application software based on the current research.

Traditionally, statistical time series methods like moving average (MA), auto‐regression (AR), or combinations of them are used for forecasting sales. Since these models predict future sales only on the basis of previous sales, they fail in an environment where the sales are more influenced by exogenous variables such as size, price, color, climatic data, effect of media, price changes or campaigns. Although, a linear regression model can take these variables into account its approximation function is restricted to be linear. Soft computing methods such as fuzzy logic, artificial neural networks (ANNs), and genetic algorithms offer an alternative taking into account both endogenous and exogenous variables and allowing arbitrary non‐linear approximation functions derived (learned) directly from the data. In this paper, two approaches have been investigated for forecasting women's apparel sales, statistical time series modeling, and modeling using ANNs. Four years' sales data (1997‐2000) were used as backcast data in the model and a forecast was made for 2 months of the year 2000. The performance of the models was tested by comparing one of the goodness‐of‐fit statistics, R², and also by comparing actual sales with the forecasted sales of different types of garments. On an average, an R² of 0.75 and 0.90 was found for single seasonal exponential smoothing and Winters' three parameter model, respectively. The model based on ANN gave a higher R² averaging 0.92. Although, R² for ANN model was higher than that of statistical models, correlations between actual and forecasted were lower than those found with Winters' three parameter model.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.