Adaptive fuzzy associative memory for online quality control

In this paper, an online quality inspection is presented based on the adaptive fuzzy associative memory (AFAM) theory. The AFAM along with vision technology enables us to inspect the quality of each component online. Throughout the process, four different types of classification exist, namely, desired, stretched, squeezed and deformed foam barrier. The learning vector quantization (LVQ) is applied to train the system based on the defined clusters according to the trainees. After ending a course of training, a bank of fuzzy associative memory (BFAM) is constructed. To perform online quality inspection, the composition applies to the input fuzzy vector and BFAM.