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.
