A feedforward multi-layer neural network for machine cell formation in computer integrated manufacturing

Abstract

Neural-network applications have been one of the better alternatives either for simulating massive data in parallel, or embedding human subjective decisions into existing quantitative models, thereby spawning a qualitative model. This paper introduces a linear classifier with a classical feedforward neural network in forming machine cells or groups for Computer Integrated Manufacturing. The proposed method, through experiment, has been proven to outperform conventional methods such as Part Family Analysis (PFA) and BLOCPLAN, among others. A single-layer perceptron, along with multi-layer feedforward network where applicable, have been employed in forming the part families. The underlying philosophy is the Group Technology (GT). The developed models and algorithms are illustrated with a numerical example.