Development of Methods to Evaluate Dynamic Fracture Toughness of Metallic Materials at Very High Loading Rates Under Limited Plastic Deformation Conditions
Creators: name: Ikenna-Uzodike, Chiamaka
Publisher: name: Lancaster University
Year: 2024

description: The measurement of mechanical properties of metallic materials at high
 strain
 
 rates has been challenging, notwithstanding the application of steel for
 
 intermediate and dynamic loading conditions. This is due to a lack of
 
 sophisticated measuring tools which will require a very high-speed camera
 to
 
 capture the stages of deformation, with little availability of recent
 machines
 
 capable 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
 different
 
 international standards. Still, the dynamic testing procedures are very
 limited as
 
 they are being modified from the quasi-static testing. It is quite
 challenging to
 
 characterize the dynamic fracture toughness owing to limitations in the
 existing
 
 standards such as BS 7448-3:2005. With the effect of inertia during the
 
 experiment 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 of
 
 structures, and this results in catastrophic failure whenever the material
 fails
 
 under dynamic loading, and thus safety is not satisfied. As dynamic
 deformation
 
 occurs with limited plastic deformation, the material fails without
 warning like
 
 showing 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 stress
 
 and strain distribution in a single-edge notched bend (SENB) specimen at
 varying
 
 loading rates to determine the effect of loading rates and crack driving
 force for
 
 the dynamic fracture toughness measurement. ABAQUS was employed in
 
 performing the FEM simulations. The ductile and damage model parameters
 
 were determined from experimental data using the Johnson-Cook model.
 
 
 Analytical solutions were also implied through the application of
 irreversible
 
 thermodynamics of dislocation evolution to predict the stress-strain curve
 at an
 
 elevated strain rate. Damage constants for FEM calculations utilising the
 
 Johnson-Cook model and the undelaying plasticity theory to capture the
 impact
 
 of the strain rate were both utilised. The thermal diffusivity method was
 applied
 
 to 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 on
 
 dynamic fracture toughness, the low-blow Charpy test was implemented to
 
 determine 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 from
 
 experimental 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.