Prediction of chemical composition and mechanical properties in powder metallurgical steels using multi-electromagnetic nondestructive methods and a data fusion system

[Display omitted] •Change in wt% of C, Cu and P varies outputs of magnetic methods with linear trends.•A predictive data fusion (DF) is designed to improve the prediction accuracy.•DF inputs are outputs of hysteresis loop, Hall effect and eddy current methods.•DF outputs are wt% of C, Cu, P, strength, elongation and hardness of PM steels.•As a powerful DF method, OWA was used and the error of prediction reduced up to 6% Variation in chemical composition is one the most important parameters that affects the microstructure and mechanical properties of powder metallurgical (PM) steel parts. Therefore, utilization of nondestructive methods to determine the chemical composition and microstructural/mechanical properties of a given PM steel sample could be a subject of importance. In the present study, capability of three electromagnetic nondestructive methods, including hysteresis loop, Hall effect and eddy current methods has been investigated to determine metallurgical properties in powder metallurgical steel parts. In order to study the variations in the chemical composition, three groups of specimens with different contents of carbon (0–0.9 wt%), copper (0–4.5 wt%) and phosphorus (0–0.45 wt%) were prepared. Microstructure and mechanical properties of the samples were evaluated using microscopic observations and tensile/hardness testing, respectively. In the next step, the relationships among the parameters extracted from the electromagnetic methods and weight percent of alloying elements have been established. Finally, to simultaneously predict the accurate weight percent of carbon, copper and phosphorus, as well as mechanical properties, the electromagnetic outputs were fed into a data fusion system. The new proposed data fusion system is an interval-based ordered weighted averaging method that uses some approximation components to first produce different approximations for each of the outputs and then fuse these approximations to increase accuracy. The study shows that multi-electromagnetic methods coupled to a powerful predictive data fusion system could be used to determine the metallurgical characteristics of the PM steel parts with high precision and reliability.