Knowledge based expert system application for automative piston material selection

Knowledge based systems (also known as computer-based expert systems) can be constructed by obtaining knowledge from a human expert and transforming it into a form that a computer may use to solve engineering problems. The aim of this paper is to select the optimum material for the operation of automotive piston emphasizing on the substitution of this cast iron by lightweight material using the knowledge-based systems (KBS). The system is capable of selecting the most suitable material and ranks the materials with respect to their properties. Seven candidate material are proposed for the automotive piston application, such as cast iron, titanium alloy, aluminum alloy, ceramics and three different composite materials such as 20% SiC reinforced Alcomposite (AMC1), 20% SiC reinforced Al-Cu alloy (AMC2) , 7.5 wt% WC and 7.5 wt% TiC reinforced Ti-composite (TMC). Mechanical properties including, friction coefficient, wear rate, thermal conductivity and specific gravity as well as the relative cost were used as the key parameters in the material selection stage. Scale properties and unit cost of material are used for calculating the performance index and figure of merit respectively for ranking and optimum material selection using knowledge based expert system.