TY - BOOK

T1 - Development of Methods to Evaluate Dynamic Fracture Toughness of Metallic Materials at Very High Loading Rates Under Limited Plastic Deformation Conditions

AU - Ikenna-Uzodike, Chiamaka

PY - 2024

Y1 - 2024

N2 - The measurement of mechanical properties of metallic materials at high strainrates has been challenging, notwithstanding the application of steel forintermediate and dynamic loading conditions. This is due to a lack ofsophisticated measuring tools which will require a very high-speed camera tocapture the stages of deformation, with little availability of recent machinescapable of testing at high strain rates when compared with testing at quasi-static strain rates.The quasi-static testing procedure has been well-established with differentinternational standards. Still, the dynamic testing procedures are very limited asthey are being modified from the quasi-static testing. It is quite challenging tocharacterize the dynamic fracture toughness owing to limitations in the existingstandards such as BS 7448-3:2005. With the effect of inertia during theexperiment of high strain rates, many oscillations are generated which masks the true path of the load-displacement curve.The concern spans from significant oscillations encountered with the stress-strain curve, making it difficult to obtain the dynamic mechanical properties of the material. Hence, it is difficult to include dynamic properties in the design ofstructures, and this results in catastrophic failure whenever the material failsunder dynamic loading, and thus safety is not satisfied. As dynamic deformationoccurs with limited plastic deformation, the material fails without warning likeshowing significant necking before failure.In this research, X65 steel material was investigated and characterized at quasistatic and dynamic conditions using several techniques like instrumented Charpy test, tensile testing (flat and round specimen), fracture toughness test, and drop weight test, which led to the proposed methods of determining high strain rates material properties. An EDM notched and fatigue pre-cracked Charpy-sized specimens were utilised in this investigation. Quasi-static fracture toughness testing was used to characterize the material properties at low strain rates, which were applied in the machine learning algorithm to predict the material’s fracture toughness.The finite element analysis was utilised to support the investigation of the stressand strain distribution in a single-edge notched bend (SENB) specimen at varyingloading rates to determine the effect of loading rates and crack driving force forthe dynamic fracture toughness measurement. ABAQUS was employed inperforming the FEM simulations. The ductile and damage model parameterswere determined from experimental data using the Johnson-Cook model.Analytical solutions were also implied through the application of irreversiblethermodynamics of dislocation evolution to predict the stress-strain curve at anelevated strain rate. Damage constants for FEM calculations utilising theJohnson-Cook model and the undelaying plasticity theory to capture the impactof the strain rate were both utilised. The thermal diffusivity method was appliedto characterize the material behaviour at high loading rates, as it is being affected by the change in temperature to undergo an adiabatic process at dynamic loading. The change in temperature at elevated loading rates was taken into consideration in dislocation density theory for the application of body-centered cubic materials.Due to the difficulty in determining data from the VHS Instron machine ondynamic fracture toughness, the low-blow Charpy test was implemented todetermine the varying strain rate properties to correlate with the simulated results. Results from the experimental results show that material strength is affected by rates of loading and increases with loading rates, whereas fracture toughness decreases with the loading rates.Finally, the machine learning approach was considered to predict the stressstrain curve and fracture toughness data. The training sets were derived fromexperimental data with certain features including the strain rate to train the model. The random forest and multilayer perceptron regressor algorithm were utilised in this work for its application with small data sets and to reduce overfitting. The results showed that it is promising to predict material properties from the machine learning algorithm to reduce the cost of material testing. However, this has a limitation from the available number of datasets, which need to be derived from experiments to increase the accuracy of the prediction of dynamic fracture toughness.

AB - The measurement of mechanical properties of metallic materials at high strainrates has been challenging, notwithstanding the application of steel forintermediate and dynamic loading conditions. This is due to a lack ofsophisticated measuring tools which will require a very high-speed camera tocapture the stages of deformation, with little availability of recent machinescapable of testing at high strain rates when compared with testing at quasi-static strain rates.The quasi-static testing procedure has been well-established with differentinternational standards. Still, the dynamic testing procedures are very limited asthey are being modified from the quasi-static testing. It is quite challenging tocharacterize the dynamic fracture toughness owing to limitations in the existingstandards such as BS 7448-3:2005. With the effect of inertia during theexperiment of high strain rates, many oscillations are generated which masks the true path of the load-displacement curve.The concern spans from significant oscillations encountered with the stress-strain curve, making it difficult to obtain the dynamic mechanical properties of the material. Hence, it is difficult to include dynamic properties in the design ofstructures, and this results in catastrophic failure whenever the material failsunder dynamic loading, and thus safety is not satisfied. As dynamic deformationoccurs with limited plastic deformation, the material fails without warning likeshowing significant necking before failure.In this research, X65 steel material was investigated and characterized at quasistatic and dynamic conditions using several techniques like instrumented Charpy test, tensile testing (flat and round specimen), fracture toughness test, and drop weight test, which led to the proposed methods of determining high strain rates material properties. An EDM notched and fatigue pre-cracked Charpy-sized specimens were utilised in this investigation. Quasi-static fracture toughness testing was used to characterize the material properties at low strain rates, which were applied in the machine learning algorithm to predict the material’s fracture toughness.The finite element analysis was utilised to support the investigation of the stressand strain distribution in a single-edge notched bend (SENB) specimen at varyingloading rates to determine the effect of loading rates and crack driving force forthe dynamic fracture toughness measurement. ABAQUS was employed inperforming the FEM simulations. The ductile and damage model parameterswere determined from experimental data using the Johnson-Cook model.Analytical solutions were also implied through the application of irreversiblethermodynamics of dislocation evolution to predict the stress-strain curve at anelevated strain rate. Damage constants for FEM calculations utilising theJohnson-Cook model and the undelaying plasticity theory to capture the impactof the strain rate were both utilised. The thermal diffusivity method was appliedto characterize the material behaviour at high loading rates, as it is being affected by the change in temperature to undergo an adiabatic process at dynamic loading. The change in temperature at elevated loading rates was taken into consideration in dislocation density theory for the application of body-centered cubic materials.Due to the difficulty in determining data from the VHS Instron machine ondynamic fracture toughness, the low-blow Charpy test was implemented todetermine the varying strain rate properties to correlate with the simulated results. Results from the experimental results show that material strength is affected by rates of loading and increases with loading rates, whereas fracture toughness decreases with the loading rates.Finally, the machine learning approach was considered to predict the stressstrain curve and fracture toughness data. The training sets were derived fromexperimental data with certain features including the strain rate to train the model. The random forest and multilayer perceptron regressor algorithm were utilised in this work for its application with small data sets and to reduce overfitting. The results showed that it is promising to predict material properties from the machine learning algorithm to reduce the cost of material testing. However, this has a limitation from the available number of datasets, which need to be derived from experiments to increase the accuracy of the prediction of dynamic fracture toughness.

U2 - 10.17635/lancaster/thesis/2283

DO - 10.17635/lancaster/thesis/2283

M3 - Doctoral Thesis

PB - Lancaster University

ER -