Determination of dynamic behaviour of materials is a serious challenge in mechanics of materials. In this investigation, a new approach is proposed to obtain stress–strain curves of metals from dynamic indentation test. This approach is based on a combined experiment, simulation, and optimization techniques. In the experiment side, a conical penetrator is shot against the material as the target. The load–indentation depth curve is obtained from the dynamic indentation test. The indentation test is simulated using Ls-dyna and the numerical load–indentation depth is obtained from the simulation. The stress–strain curves are defined by Johnson–Cook material model. From optimization of the difference between the experimental and numerical load–indentation depth curves, the constants of the material model are identified. The material model is validated also by stress–strain curves obtained from quasi-static test conducted using Instron and dynamic tests conducted using Split Hopkinson Bar. The results show a close agreement between the model prediction and the experimental stress–strain curves for different strain rates.
Stress-strain curves of materials normally change with strain rate and temperature and are normally defined by material models. In this study, a new technique was developed for determining the constants of material models. This technique was based on dynamic indentation test, numerical simulation using Ls-dyna code and artificial neural network. An indenter of tapered shape was shot against the materials as the target by a gas gun. The experiments were carried out for four strain rates and four temperatures. The target was made of pure copper. The penetration depth-time and load-time histories were captured by a LVDT and a piezoelectric load-cell, respectively and the load-penetration depth curve (P-h) was obtained. This curve is characterized by five parameters which are determined for each indentation test. On the other hand, the indentation test was simulated using Ls-dyna hydrocode. From the simulations, the P-h curves were obtained using Johnson-Cook (J-C) and Zerilli-Armstrong (Z-A) material models and the characterizing parameters of the numerical P-h curves were also identified. Finally, an artificial neural network (ANN) was trained by the numerical P-h curves parameters as the input and the constants of J-C and Z-A models as the output. The trained neural network was then tested by the experimental p-h curves parameters as the input and the constants of J-C and Z-A models as the output. Moreover, a number of dynamic compression tests were performed using the well-known Split Hopkinson Bar at the same strain rates and temperatures used for indentation tests and the stress-strain curves of material were obtained. A reasonable agreement was observed between the stress-strain curves predicted by neural network and the Split Hopkinson Bar. The proposed method does not need sophisticated instrumentation and in fact, the load-time and indentation depth-time histories are directly converted to stress-strain of material using an artificial neural network.
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