Machine learning assisted prediction and regulation of extrusion force and exit temperature of high-strength aluminum alloy
Extrusion force and exit temperature are the key parameters that affect the ex-trusion cost and product quality of aluminum alloys.Based on the finite element numerical simulation data and artificial neural network modeling,this paper studied the evolution rule of extrusion force and exit temperature of high-strength aluminum alloy under different extru-sion process parameters(billet temperature,die temperature,extrusion speed),and realized the accurate prediction of extrusion force-displacement curves and exit temperature-displacement curves.The prediction errors were 2.84%and 1.30%,respectively.The sup-port vector regression algorithm and input variable screening were used to accurately predict the peak extrusion force and the maximum and minimum exit temperature of the extrusion process,and the prediction errors were 0.82%,1.18%and 1.88%,respectively.Based on the prediction model,the genetic algorithm could be used to quickly determine the appro-priate extrusion process parameters when the target exit temperature was given,or the appro-priate extrusion speed could be quickly determined when the temperature of the billet and the die fluctuated.
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万方数据
