An Intelligent Modeling System to Predict Mechanical Strength Characteristics of Selective Inhibition Sintered Parts using Fuzzy Logic Approach

Abstract

Selective inhibition sintering (SIS) is a powder-based additive manufacturing technique that produces near net shape components through prevention of selected regions of powder particles from sintering. In SIS, due to its non-linear phenomenon, predicting functional relationship between input process parameters and output responses are cumbersome. Therefore, in the present work, an effort has been made to build an intelligent fuzzy-logic expert system for analyzing the mechanical strength characteristics includes tensile and flexural strengths of SIS processed high strength polyethylene (HDPE) parts. The experiments are conducted using response surface methodology based four-factor, three-level box-behnken design through considering SIS process variables such as layer thickness, heater energy, heater feedrate, and printer feedrate. A mamdani based fuzzy rule model is developed to predict the responses. Comparative studies of experimental and fuzzy results suggested that the obtained average error of mechanical strength characteristics using fuzzy system is in good agreement with experimental results. Thus, the developed fuzzy model can be effectively used to model SIS process to predict part strength characteristics in automated manufacturing environments to reduce the complexity of process planning activities.