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Magnetic tunnel junctions can serve as ultrasensitive low-frequency magnetic sensors, however, their low-frequency performance is limited by low-frequency noise, i.e., 1/f noise. In this paper, we investigate the 1/f noise in MgO magnetic tunnel junctions (MTJs) with a tunneling magnetoresistance (TMR) of 160%, and examine the influence of annealing and MTJ size. The results show that the annealing process can not only dramatically improve the TMR, but can also strongly decrease the MTJ noise. The effect is discussed in terms of the structure of MgO barriers and tunneling probabilities. Increasing the MTJ area to 6400 μm2 yields a voltage spectral density as low as 11 nV/Hz1/2 at 1000 Hz. The possible reasons for the area dependence are discussed. [ABSTRACT FROM AUTHOR]

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP; and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design. [ABSTRACT FROM AUTHOR]

Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be turned into a product. It is evident that the degree of complexity of the assembly problem will increase when the number of constraints is larger. Using Genetic Algorithms (GAs) to solve the assembly sequence features speed and flexibility can fit the requirements of various domains. In the case of larger constraint assembly problems, however, GAs will generate a large number of infeasible solutions in the evolution procedure, thus reducing the efficiency of the solution-searching process. Traditionally, using GAs is a random and blind-searching procedure in which it is not always the case that the offspring obtained through the evolutionary mechanism will meet the requirements of all limitations. In this study, therefore, Guided-GAs are proposed wherein the proper initial population and the alternation of crossover and mutation mechanisms are covered to overcome assembly planning problems that contain large constraints. The optimal assembly sequence is obtained through the combination of Guided-GAs and the Connector-based assembly planning context as previously suggested. Finally, practical examples are offered to illustrate the feasibility of Guided-GAs. It is found that Guided-GAs can effectively solve the assembly planning problem of larger constraints. [ABSTRACT FROM AUTHOR]

In an ever-changing era, fast responses in the research and development (R&D) of products should be made to satisfy the various needs of customers. The sequential consideration for assembly sequence planning and assembly line balancing can contribute to shrinking the gap between design and manufacturing. The so-called assembly sequence planning refers to a task in which planners arrange a specific assembly sequence according to the product design description as well as to their particular heuristics in assembling all the components of a product. Assembly line balancing is concerned with how to effectively arrange every assembly task so as to reach the highest efficiency and the shortest idling time in the total assembly line. In this study, connectors equipped with assembly engineering information (combination, direction, tool, and cycle time) serve as the basic units to replace the traditional method in which only parts are taken into consideration. Connectors can be defined as the basic units that offer limitations to components, and when these units are combined, they will perform the required functions. Under the constraints of connector-based precedence graph, similarity between connectors is used to arrange the assembly tasks. When the result meets the cycle-time requirements and the types of stations to which the connectors belong, the arrangement of stations based on the assembly sequential order is processed. Genetic algorithms are proposed to solve the arrangement of connectors and the selection of stations. Two examples illustrate how to apply the model in practical cases. [ABSTRACT FROM AUTHOR]

Assembly planning refers to the task where planners arrange a specific assembly sequence according to the product design description as well as to their particular heuristics in putting together all the components of a product. In assembly planning, one needs to take into consideration the relationships between components such as the geometric limitation factor before the precedence sequence is set up for assembly. This deliberation will contribute strongly to lower the production cost. Unlike traditional studies where the liaison graph goes with a genetic algorithm, an attempt is made to solve problems in assembly planning by using a genetic algorithm under the connector-based environment. Such a connector-based genetic algorithm takes a more realistic view. The key point in this approach to assembly planning is to combine the connector concept and characteristics of a genetic algorithm using object-oriented programming; thus, the programming language C++ is used to develop the mechanism of the algorithm. Finally, a stapler and a computer hard disk were used as practical examples to illustrate the possibility of such an idea. Consequently, in terms of assembly planning, it is feasible to create a genetic algorithm based on the connector's engineering features. [ABSTRACT FROM AUTHOR]