Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

The artificial neural network methodology presented in this paper was trained to predict the green shear strength of compacted samples made from iron powder. Iron powders of three different morphologies admixed with three types of lubricants in different amounts were considered. Green compacts were pressed uniaxially in a square floating die. The more or less cubic slugs were sheared to fracture perpendicularly and parallel to the direction of compaction. From the maximum loads at the start of decohesion the green shear strengths were calculated. Compaction parameters together with corresponding shear strength records were used as sets of data for the training process. The performance of the network was verified by putting aside one set of data and testing the network against it. Comparison of the predicted and experimental data confirmed the accuracy of the model.

► A method was proposed to measure the green strength of compacted iron powder.
► ANN was applied to estimate the shear strength of green compacts.
► Experimental design parameters were optimized by ANN to obtain a specified strength.
► Comparison of predicted and experimental data confirmed the accuracy of the model.