Proactive quality paint thickness control using ANFIS

Automotive industries try to develop their technologies to improve quality and to minimize scrapes and wastes. In the paint shop vehicle, quality control generally relies on inspection. Industries try to develop their technologies to improve quality via proactive quality control. This paper investigates the predictability of the paint thickness to reduce defects, using ANFIS. A description of the automobile paint spray process is introduced and the inputs (as effective factors in paint spray process) are identified for each thin layer on a plate. A 50×80 sheet of metal is considered as a sample. In the present paper two models of ANFIS are presented. First model shows prediction of film thickness by using 6 inputs for bell, air layers, 7 input variables for dry film thickness or final paint thickness and 6 output points for three layers. Second model is predicting of paint appearing uniformity by average and standard deviation of film thickness.