Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass
and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new
experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg m3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.
Tohid Mirzababaie Mostofi: Faculty of Mechanical Engineering, University of Eyvanekey, Eyvanekey, Iran
Mostafa Sayah-Badkhor: Faculty of Mechanical Engineering, University of Eyvanekey, Eyvanekey, Iran
Mohammad Rezasefat: Faculty of Mechanical Engineering, University of Eyvanekey, Eyvanekey, Iran
Hashem Babaei: Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran
Togay Ozbakkaloglu: Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA
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