| Author Name | Affiliation | Postcode | | Salman Khayoon Aldriasawi* | Mechanical Engineering Department,Kut Technical Institute,Middle Technical University,Baghdad ,Iraq | 10074 | | Nihayat Hussein Ameen | College of Agriculture,University of Kirkuk,Kirkuk ,Iraq | 36001 | | Kareem Idan Fadheel | Mechanical Engineering Department,Kut Technical Institute,Middle Technical University,Baghdad ,Iraq | 10074 | | Ashham Muhammed Anead | Mechanical Engineering Department,Kut Technical Institute,Middle Technical University,Baghdad ,Iraq | 10074 | | Hakeem Emad Mhabes | Computer Science Department,Kut Technical Institute,Middle Technical University,Baghdad ,Iraq | 10074 | | Barhm Mohamad | Department of Petroleum Technology,Koya Technical Institute,Erbil Polytechnic University,Erbil ,Iraq | 44001 |
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| Abstract: |
| The present study establishes a new estimation model using an artificial neural network (ANN) to predict the mechanical properties of the AISI 1035 alloy. The experiments were designed based on the L16 orthogonal array of the Taguchi method. A proposed numerical model for predicting the correlation of mechanical properties was supplemented with experimental data. The quenching process was conducted using a cooling medium called “nanofluids”. Nanoparticles were dissolved in a liquid phase at various concentrations (0.5, 1, 2.5, and 5 % vf) to prepare the nanofluids. Experimental investigations were done to assess the impact of temperature, base fluid, volume fraction, and soaking time on the mechanical properties. The outcomes showed that all conditions led to a noticeable improvement in the alloy"s hardness which reached 100%, the grain size was refined about 80%, and unwanted residual stresses were removed from 50 to 150 MPa. Adding 5% of CuO nanoparticles to oil led to the best grain size refinement, while adding 2.5% of Al2O3 nanoparticles to engine oil resulted in the greatest compressive residual stress. The experimental variables were used as the input data for the established numerical ANN model, and the mechanical properties were the output. Upwards of 99% of the training network"s correlations seemed to be positive. The estimated result, nevertheless, matched the experimental dataset exactly. Thus, the ANN model is an effective tool for reflecting the effects of quenching conditions on the mechanical properties of AISI 1035. |
| Key words: Quenching, nanofluids, residual stresses, steel alloy, artificial neural network, MANOVA. |
| DOI：10.11916/j.issn.1005-9113.2023090 |
| Clc Number:TG156.3 |
| Fund: |
