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Metal additive manufacturing (AM) processes, such as laser powder bed fusion (L-PBF), can yield high-value parts with unique geometries and features, substantially reducing costs and enhancing performance. However, the material properties from L-PBF processes are highly sensitive to the laser processing conditions and the resulting dynamic temperature fields around the melt pool. In this study, we develop a methodology to measure thermal gradients, cooling rates, and solidification velocities during solidification of refractory alloy C103 using in situ high-speed infrared (IR) imaging with a high frame rate of approximately 15,000 frames per second (fps). Radiation intensity maps are converted to temperature maps by integrating thermal radiation over the wavelength range of the camera detector while also considering signal attenuation caused by optical parts. Using a simple method that assigns the liquidus temperature to the melt pool boundary identified ex situ, a scaling relationship between temperature and the IR signal was obtained. The spatial temperature gradients (dT/dx), heating/cooling rates (dT/dt), and solidification velocities (R) are resolved with sufficient temporal resolution under various laser processing conditions, and the resulting microstructures are analyzed, revealing epitaxial growth and nucleated grain growth. Thermal data shows that a decreasing temperature gradient and increasing solidification velocity from the edge to the center of the melt pool can induce a transition from epitaxial to equiaxed grain morphology, consistent with the previously reported columnar to equiaxed transition (CET) trend. The methodology presented can reduce the uncertainty and variability in AM and guide microstructure control during AM of metallic alloys.

In this paper, we propose a distributed solution to design a multi-hop ad hoc network where mobile relay nodes strategically determine their wireless transmission ranges based on a deep reinforcement learning approach. We consider scenarios where only a limited networking infrastructure is available but a large number of wireless mobile relay nodes are deployed in building a multi-hop ad hoc network to deliver source data to the destination. A mobile relay node is considered as a decision-making agent that strategically determines its transmission range in a way that maximizes network throughput while minimizing the corresponding transmission power consumption. Each relay node collects information from its partial observations and learns its environment through a sequence of experiences. Hence, the proposed solution requires only a minimal amount of information from the system. We show that the actions that the relay nodes take from its policy are determined as to activate or inactivate its transmission, i.e., only necessary relay nodes are activated with the maximum transmit power, and nonessential nodes are deactivated to minimize power consumption. Using extensive experiments, we confirm that the proposed solution builds a network with higher network performance than current state-of-the-art solutions in terms of system goodput and connectivity ratio.

In this paper, we propose a solution to the distributed topology formation problem for large-scale sensor networks with multi-source multicast flows. The proposed solution is based on game-theoretic approaches in conjunction with network coding. The proposed algorithm requires significantly low computational complexity, while it is known as NP-hard to find an optimal topology for network coding deployed multi-source multicast flows. In particular, we formulate the problem of distributed network topology formation as a network formation game by considering the nodes in the network as players that can take actions for making outgoing links. The proposed solution decomposes the original game that consists of multiple players and multicast flows into independent link formation games played by only two players with a unicast flow. We also show that the proposed algorithm is guaranteed to determine at least one stable topology. Our simulation results confirm that the computational complexity of the proposed solution is low enough for practical deployment in large-scale networks.

In this paper, we aim to find a robust network formation strategy that can adaptively evolve the network topology against network dynamics in a distributed manner. We consider a network coding deployed wireless ad hoc network where source nodes are connected to terminal nodes with the help of intermediate nodes. We show that mixing operations in network coding can induce packet anonymity that allows the inter-connections in a network to be decoupled. This enables each intermediate node to consider complex network inter-connections as a node-environment interaction such that the Markov decision process (MDP) can be employed at each intermediate node. The optimal policy that can be obtained by solving the MDP provides each node with optimal amount of changes in transmission range given network dynamics (e.g., the number of nodes in the range and channel condition). Hence, the network can be adaptively and optimally evolved by responding to the network dynamics. The proposed strategy is used to maximize long-term utility, which is achieved by considering both current network conditions and future network dynamics. We define the utility of an action to include network throughput gain and the cost of transmission power. We show that the resulting network of the proposed strategy eventually converges to stationary networks, which maintain the states of the nodes. Moreover, we propose to determine initial transmission ranges and initial network topology that can expedite the convergence of the proposed algorithm. Our simulation results confirm that the proposed strategy builds a network which adaptively changes its topology in the presence of network dynamics. Moreover, the proposed strategy outperforms existing strategies in terms of system goodput and successful connectivity ratio., Comment: IEEE Transactions on Mobile Computing (Early Access)

This paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth bottlenecks. We first show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples about the possible of our algorithms that can be deployed in sensor networks and distributed imaging applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our low complexity solution for delivery of correlated data sources.

Recent research efforts have shown that the popular BitTorrent protocol does not provide fair resource reciprocation and may allow free-riding. In this paper, we propose a BitTorrent-like protocol that replaces the peer selection mechanisms in the regular BitTorrent protocol with a novel reinforcement learning (RL) based mechanism. Due to the inherent opration of P2P systems, which involves repeated interactions among peers over a long period of time, the peers can efficiently identify free-riders as well as desirable collaborators by learning the behavior of their associated peers. Thus, it can help peers improve their download rates and discourage free-riding, while improving fairness in the system. We model the peers' interactions in the BitTorrent-like network as a repeated interaction game, where we explicitly consider the strategic behavior of the peers. A peer, which applies the RL-based mechanism, uses a partial history of the observations on associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations with other peers, which would maximize the peer's long-term performance, thereby making foresighted decisions. We have implemented the proposed reinforcement-learning based mechanism and incorporated it into an existing BitTorrent client. We have performed extensive experiments on a controlled Planetlab test bed. Our results confirm that our proposed protocol (1) promotes fairness in terms of incentives to each peer's contribution e.g. high capacity peers improve their download completion time by up to 33\%, (2) improves the system stability and robustness e.g. reducing the peer selection luctuations by 57\%, and (3) discourages free-riding e.g. peers reduce by 64\% their upload to \FR, in comparison to the regular \BT~protocol., Comment: 12 pages, 14 figures