Numerical analysis of friction stir welding process

Friction stir welding (FSW), which has several advantages over the conventional welding processes, is a solid-state welding process where no gross melting of the material being welded takes place. Despite significant advances over the last decade, the fundamental knowledge of thermomechanical processes during FSW is still not completely understood. To gain physical insight into the FSW process and the evaluation of the critical parameters, the development of models and simulation techniques is a necessity. In this article, the available literature on modeling of FSW has been reviewed followed by details of an attempt to understand the interaction between process parameters from a simulation study, performed using commercially available nonlinear finite element (FE) code DEFORM. The distributions of temperature, residual stress, strain, and strain rates were analyzed across various regions of the weld apart from material flow as a means of evaluating process efficiency and the quality of the weld. The distribution of process parameters is of importance in the prediction of the occurrence of welding defects, and to locate areas of concern for the metallurgist. The suitability of this modeling tool to simulate the FSW process has been discussed. The lack of the detailed material constitutive information and other thermal and physical properties at conditions such as very high strain rates and elevated temperatures seems to be the limiting factor while modeling the FSW process.     

Energy and Trust Management Framework for MANET using Clustering Algorithm

In General, Mobile Ad-Hoc Network (MANET) has limited energy resources, and it cannot recharge itself. This research goal focuses on building a power management scheme that saves energy in the MANET. Due to power instability, there is a chance that cluster heads fail and function incorrectly in cluster-based routing. As a result, instability occurs with the cluster heads while collecting data and communicating with others effectively. This work focuses on detecting the unstable cluster heads, which are replaced by other nodes implementing the envisaged self-configurable cluster mechanism. A self-configurable cluster mechanism with a k-means protocol approach is proposed to designate cluster heads effectively. The proposed k-means procedure is based on periodic irregular cluster head rotations or altering the number of clusters. We also propose a trust management mechanism in this research to detect and avoid MANET vulnerabilities. Because of the continuously changing topology and limited resources (power, bandwidth, computing), the trust management algorithm should only use local data. Consequently, compared to traditional protocols, the proposed approach with the k-means procedure and its experimental results show lower power usage and provide an optimal system for trust management.

     

A queue based mutual exclusion algorithm

A new elegant and simple algorithm for mutual exclusion of N processes is proposed. It only requires shared variables in a memory model where shared variables need not be accessed atomically. We prove mutual exclusion by reformulating the algorithm as a transition system (automaton), and applying simulation of automata. The proof has been verified with the higher-order interactive theorem prover PVS. Under an additional atomicity assumption, the algorithm is starvation free, and we conjecture that no competing process is passed by any other process more than once. This conjecture was verified by model checking for systems with at most five processes.     

3D computer vision based on machine learning with deep neural networks: A review

Recent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain's visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we hope to also provide inspiration for future advances in automated visual processing.     

Nonatomic dual bakery algorithm with bounded tokens

A simple mutual exclusion algorithm is presented that only uses nonatomic shared variables of bounded size, and that satisfies bounded overtaking. When the shared variables behave atomically, it has the first-come-first-served property (FCFS). Nonatomic access makes information vulnerable. The effects of this can be mitigated by minimizing the information and by spreading it over more variables. The design approach adopted here begins with such mitigating efforts. These resulted in an algorithm with a proof of correctness, first for atomic variables. This proof is then used as a blueprint for the simultaneous development of the algorithm for nonatomic variables and its proof. Mutual exclusion is proved by means of invariants. Bounded overtaking and liveness under weak fairness are proved with invariants and variant functions. Liveness under weak fairness is formalized and proved in a set-theoretic version of temporal logic. All these assertions are verified with the proof assistant PVS. We heavily rely on the possibility offered by a proof assistant like PVS to reuse proofs developed for one context in a different context.     

SMARTINT: using mined attribute dependencies to integrate fragmented web databases

Many web databases can be seen as providing partial and overlapping information about entities in the world. To answer queries effectively, we need to integrate the information about the individual entities that are fragmented over multiple sources. At first blush this is just the inverse of traditional database normalization problem—rather than go from a universal relation to normalized tables, we want to reconstruct the universal relation given the tables (sources). The standard way of reconstructing the entities will involve joining the tables. Unfortunately, because of the autonomous and decentralized way in which the sources are populated, they often do not have Primary Key–Foreign Key relations. While tables may share attributes, naive joins over these shared attributes can result in reconstruction of many spurious entities thus seriously compromising precision. Our system, SmartInt is aimed at addressing the problem of data integration in such scenarios. Given a query, our system uses the Approximate Functional Dependencies (AFDs) to piece together a tree of relevant tables to answer it. The result tuples produced by our system are able to strike a favorable balance between precision and recall.     

Micro- and nano-scale deformation behavior of nylon 66-based binary and ternary nanocomposites

The primary aim of this paper is to provide an insight on the effect of the location of organoclay on the micro- and nano-scale deformation processes in melt-compounded nylon 66/organoclay/SEBS-g-MA ternary nanocomposites prepared by different blending sequences. In addition, the deformation processes of the ternary nanocomposites were compared to the binary nanocomposites (nylon 66/organoclay and nylon 66/SEBS-g-MA) and neat nylon 66. The incorporation of SEBS-g-MA particles toughened nylon 66 markedly; but the flexural modulus and strength were both reduced. Conversely, the use of organoclay increased the modulus but decreased the fracture toughness of nylon 66. Nylon 66/SEBS-g-MA/organoclay ternary nanocomposites exhibited balanced elastic stiffness and toughness. Stress-whitening studies of the fracture surfaces in terms of gray level were also performed and an attempt was made to correlate the optical reflectivity characteristics with fracture toughness. It was concluded that the capability of SEBS-g-MA particles to cavitate was decreased by the presence of organoclay in the SEBS-g-MA phase, resulting in reduced toughening efficiency. The best micro-structure for toughness and other mechanical properties is thus to maximize the amount of exfoliated organoclay in the nylon 66 matrix rather than to have it embedded in the finely dispersed SEBS-g-MA particles.     

Damage Tolerant Magnesium Metal Matrix Composites: Influence of Reinforcement and Processing

A growing impetus to enhance our understanding of the behavior of magnesium-based alloys for use in weight critical applications resulted as a consequence of the low density of magnesium. In an attempt to enhance the applicability of magnesium for a wide spectrum of performance-critical applications, the addition of reinforcement to the alloy was considered as an economically affordable and potentially viable scientific alternative. In this paper are reported the results of a study aimed at understanding the influence of saffil alumina short fiber reinforcement on microstructural development of a squeeze-cast magnesium alloy. Preliminary results confirm promise of the reinforced alloy, which retains hardness, strength, and stiffness better at elevated temperatures compared to the unreinforced counterpart. However, impact strength and toughness of the reinforced alloy are inferior. The importance of the matrix alloy in governing the overall mechanical response of the composite microstructure is discussed based on fractographic observations. The importance of volume fraction of the reinforcing phase on properties of the composite microstructure is highlighted.     

Scheduling on Unrelated Machines under Tree-Like Precedence Constraints

We present polylogarithmic approximations for the R|prec|C _max and R|prec|∑ _j w _j C _j problems, when the precedence constraints are “treelike”—i.e., when the undirected graph underlying the precedences is a forest. These are the first non-trivial generalizations of the job shop scheduling problem to scheduling with precedence constraints that are not just chains. These are also the first non-trivial results for the weighted completion time objective on unrelated machines with precedence constraints of any kind. We obtain improved bounds for the weighted completion time and flow time for the case of chains with restricted assignment—this generalizes the job shop problem to these objective functions. We use the same lower bound of “congestion + dilation”, as in other job shop scheduling approaches (e.g. Shmoys, Stein and Wein, SIAM J. Comput. 23, 617–632, 1994). The first step in our algorithm for the R|prec|C _max problem with treelike precedences involves using the algorithm of Lenstra, Shmoys and Tardos to obtain a processor assignment with the congestion + dilation value within a constant factor of the optimal. We then show how to generalize the random-delays technique of Leighton, Maggs and Rao to the case of trees. For the special case of chains, we show a dependent rounding technique which leads to a bicriteria approximation algorithm for minimizing the flow time, a notoriously hard objective function. A preliminary version of this paper appeared in the Proc. International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), pages 146–157, 2005. V.S. Anil Kumar supported in part by NSF Award CNS-0626964. Part of this work was done while at the Los Alamos National Laboratory, and supported in part by the Department of Energy under Contract W-7405-ENG-36. M.V. Marathe supported in part by NSF Award CNS-0626964. Part of this work was done while at the Los Alamos National Laboratory, and supported in part by the Department of Energy under Contract W-7405-ENG-36. Part of this work by S. Parthasarathy was done while at the Department of Computer Science, University of Maryland, College Park, MD 20742, and in part while visiting the Los Alamos National Laboratory. Research supported in part by NSF Award CCR-0208005 and NSF ITR Award CNS-0426683. Research of A. Srinivasan supported in part by NSF Award CCR-0208005, NSF ITR Award CNS-0426683, and NSF Award CNS-0626636.