Determining optimal quality distribution of latex weight using adaptive neuro-fuzzy modeling and control systems

This paper introduces a systematic approach for the design of an adaptive neuro-fuzzy inference system (ANFIS) for latex weight control of level loop carpets. In high production volume of some industries, manual control could lead to undesirable variations in product quality. Therefore, process parameters require continuous checking and testing against quality standards. One way to overcome this problem is to use statistical process control by which a complete elimination of variability may not be possible. Fuzzy logic (FL) control is one of the most significant applications of fuzzy logic and fuzzy set theory. Fuzzy if-then rules (controllers) were developed in a systematic way that formed the backbone of the neuro-fuzzy control system. The developed ANFIS was able to produce crisp numerical outcomes to predict latex weights. The neuro-fuzzy system behaved like human operators. ANFIS outcomes were encouraging because they provide a more efficient and uniform distribution of latex weight and seemed to be better than the other statistical process control tools. FL controllers provide a feasible alternative to capture approximate, qualitative aspects of human reasoning and decision making processes.

► An ANFIS model was designed for controlling the quality of latex weight of level loop carpets. ► The quantity of latex is a critical parameter and has negative impact on the final product quality. ► The developed ANFIS model reduces the variations and provides a better latex distribution on the carpet surface. ► Fuzzy linguistic values were employed for product quality assessment. ► The method is able to improve the quality of final product.