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
Business Process Modeling as Complex Networks
In this early stage Project we want to translate the BPM notation into graph language in order to perform structural analysis of business processes together with classical functional analysis from Operations Research. The process network metrics will help us to the process analysis and optimization:
- Graph diameter: the maximum number of activities that must be carried out to end the processes.
- Shortest path: the minimum number of activities that must be carried out to end the processes.
- Average path length and clustering coefficient can give information about the parallelization and recursion degree of the processes.
- Indegree and Outdegree of the process components, identify the process sources and sinks, the ration between them influence the process controllability.
- Hubs: Critical activities.
- Clusters: Sets of very interrelated activities, will possible coincide with the designed process and can be used as a process design assessment tool.
- Structural attacks: removing critical activities would make the process collapse? Measure robustness of the process.
Ensemble learning using Attractor Neural Networks
One of the main trends of research in Machine Learning, along with “deep learning”, is the study of “networks of networks”. One central question of interest is comparing a net of nets with overall connectivity of similar degree with only one single net with the same size and check their different performances in terms of storage/load, computational complexity, etc. Attractor networks as associative memory models have many desirable properties. As dynamical systems and associative memory systems the attractor networks can be used as denoising and pattern completion. They have been used in neuroscience to model for example spatial working memory and in Engineering applications such as fingerprint recognition and automotive traffic content retrieval systems. However, one of their drawbacks is limited capacity of storage. Using an Ensemble of diluted Attractor Neural Networks for pattern retrieval we have increased the network storage capacity by a divide-and-conquer approach of subnetworks. With the Ensemble approach we can deal with Engineering applications to limited memory systems: embedded systems or smartphones. Also, applications with intensive storage needs can be approached.
