An Experimental Artificial Neural Network Model:Investigating and Predicting Effects of Quenching Process on Residual Stresses of AISI 1035 Steel Alloy  

作　　者：Salman Khayoon Aldriasawi Nihayat Hussein Ameen Kareem Idan Fadheel Ashham Muhammed Anead Hakeem Emad Mhabes Barhm Mohamad 

机构地区：[1]Mechanical Engineering Department,Kut Technical Institute,Middle Technical University,Baghdad 10074,Iraq [2]College of Agriculture,University of Kirkuk,Kirkuk 36001,Iraq [3]Computer Science Department,Kut Technical Institute,Middle Technical University,Baghdad 10074,Iraq [4]Department of Petroleum Technology,Koya Technical Institute,Erbil Polytechnic University,Erbil 44001,Iraq

出　　处：《Journal of Harbin Institute of Technology(New Series)》2024年第5期78-92,共15页哈尔滨工业大学学报（英文版）

基　　金：Kut Technical Institute for their funding supports。

摘　　要：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 Al_(2)O_(3) 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.

关 键 词：QUENCHING nanofluids residual stresses steel alloy artificial neural network MANOVA 

分 类 号：TG156.3[金属学及工艺—热处理]