Contributions of multi-objective optimization techniques to maximize thermal stability and strength of materials obtained by machining
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The microstructure of pure Copper processed by machining manufacturing process such as turning and milling is explored with the aim to create highly refined grain structures to achieve the highest strength while postponing the potential possibilities for future recrystallization which inherently lead to non-thermally-stable materials. These two targeted properties are often conflicting requirements: an improvement in one (strength) generally leads to a deterioration in the other (thermal stability). Hence, it is not straight forward to follow a procedure that would set both the properties at their individual best: all that can be achieved is a tradeoff, a compromise between the two objectives. Here we attempt to obtain a set of optimum solutions using multi-objective optimization algorithms as well as the Kuhn-Tucker optimality conditions. Additionally, we verify the solution empirically by creating the sample condition. The resulting microstructure is characterized via electron microscopy confirming the theoretical result. The thermal stability of the optimal solution is verified as well. Finally, we studied the kinetics of crystallization on the optimal solution using the Johson–Mehl–Avrami–Kolmogorov (JMAK) theory.
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Contact: Sepideh Abolghasem
Statistical modeling of materials microstructure in machining-based manufacturing processes
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Capturing microstructure evolution in machining experiments such as turning and milling can help in controlling and predicting materials responses as well as the final properties of the engineering metallic components. We examine different metallic materials through machining. The cutting conditions are characterized and the microstructure characteristics are quantified utilizing orientation imaging microscopy (OIM). Finite element models are performed to obtain the central machining conditions for various ranges of paramters. We develop models to study microstructure transformations via two approaches. The first one develops statistical mappings between the machining parameters and the microstructural characteristics. We perform a particular designed experiments method seeking a linear mixed-effects model for the two principal machining parameters, cutting angle and the velocity. The statistical model is applied to identify the factors that most significantly contribute to variability in the mean grain size and orientation angle among the gains. The second approach incorporates the effect of cutting tool edge radius using functional regression method. Building on this, the model estimates the final grain size given the distribution of the tool edge radius. . The premise of the ongoing research is that machining under realistic conditions is characterized by a stochastic system wherein the temporal evolution of the tool-tip geometry and the distributions of the bulk microstructure would interact in complex ways, while leading to the evolution of final microstructures. Identifying this evolution would likely involve a Bayesian framework to link the effect of tool-wear to the modification of machining conditions.
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Contact: Sepideh Abolghasem