An alternative numerical approach for computing eddy currents: Case of the double-layered plate

A numerical approach is presented for cases where induced eddy current densities are to be computed in axisymmetric situations. Conductors are represented as stacks of inductively coupled, concentric, metal rings in which current is excited by an external coil. Matrix techniques are used to solve the resulting set of circuit equations. Sinusoidally induced currents in a sandwich plate consisting of two layers of different metals are discussed as an application. Comparison is made with a finite element calculation and with results obtained via an analytical approach. Nonsinusoidal excitation is also briefly discussed.     

Heuristic optimization system for the determination of index positions on CNC magazines with the consideration of cutting tool duplications

This paper presents a meta-heuristic optimization system developed for the determination of optimal index positions of cutting tools on the tool magazines (automatic tool changer (ATC) or turret magazine, etc.) of CNC (computerized numerical control) machine tools. The selection of index positions is performed using a simulated annealing (SA) algorithm that takes the following as the input: (1) a list of cutting tools assigned to certain machining operations; (2) the number of copies of each cutting tool available in the workshop; (3) total number of index positions on the tool magazines; (4) the indexing time of a tool magazine of a CNC machine tool specified by manufacturer. Then, the SA algorithm determines index locations of cutting tools on the tool magazines. Dereli et al. and Dereli and Filiz previously studied the present problem by using genetic algorithms (GAs). However, the duplication of cutting tools was not taken into account in their works, although it can reduce the total tool-indexing time and therefore improve the productivity considerably. Nevertheless, the consideration of the ‘tool duplications’ makes the problem much harder to model and to solve. In this paper, a novel solution representation-scheme based on the SA, which enables easy manipulation during feasible neighbourhood solution generation, is proposed for the determination of the index positions of cutting tools on the CNC magazines with the consideration of ‘tool duplications’. Example problems are solved to present the implementation and merits of the proposed optimisation system. It is shown that it is possible to allocate the cutting tools in an efficient manner on the CNC magazines with the developed system.     

Optimal cutting conditions in turning of particulate metal matrix composites based on experiment and a genetic search model

The results of an experimental study concerned with the evolution of cutting forces, tool wear and surface roughness, as functions of time when turning the particulate metal matrix composite A356/20/SiCp-T6 are presented. Inserts with polycrystalline diamond (PCD) were tested. Cutting forces were measured using a piezoelectric dynamometer. The wear type was identified and its evolution with cutting time was measured. To model the phenomena a hybrid technique based on an evolutionary search over the design space defined by the experimental results is considered. Optimal cutting conditions are searched using a genetic algorithm based on an elitist strategy. The chromosomes composed by random keys represent cutting conditions defined according to a temporal scale.     

Cutting path optimization in CNC cutting processes using a two-step genetic algorithm

This paper deals with the problem of generating 2D cutting paths for a stock plate nested with a set of regular and/or irregular parts. The objective of the problem is to minimize the total non-productive traveling distance of a cutter starting from a known depot, then cutting all the given parts, and returning back to the depot. A cutting path consists of the depot and piercing points, each of which is to be specified for cutting a part. The cutting path optimization problem is shown to be formulated as a generalized version of the standard traveling salesman problem. To solve the problem, a two-step genetic algorithm combining global search for piercing point optimization and local search for part sequencing is proposed. Traditional genetic operators developed for continuous optimization problems are modified to effectively deal with the continuous nature of piercing-point positions. A series of computational results are provided to illustrate the validity of the proposed algorithm.     

Production scheduling in a market-driven foundry: a mathematical programming approach versus a project scheduling metaheuristic algorithm

This paper describes a real problem in a market-driven medium sized foundry delivering a wide range of castings to different markets. The problem consists of finding an efficient production plan to schedule the different processes (moulding, furnacing, cutting, tooling, etc.) needed to the manufacture of the pieces. Different objectives and resources and technical constraints must be taken into account. To solve this problem we have first developed a more classical integer linear programming approach based on a rolling horizon strategy. The most innovative contribution of the paper is that it models the problem as a project scheduling problem. Based on this model we present a metaheuristic algorithm that adapts techniques from the area. Computational experiments comparing both approaches are provided on instances created by a generator simulating real instances.     

Hypersonic flight vehicle trajectory optimization using pattern search algorithm

In this work, trajectory optimization of an aerodynamically controlled hypersonic boost glide class of flight vehicle is presented. In order to meet the mission constraints such as controllability, skin temperature, and terminal conditions etc., the trajectory is optimized using a pattern search algorithm with the lift to drag (L/D) ratio as a control parameter. It is brought out that the approach offers a viable tool for optimizing trajectories for the considered class of vehicles. Further, the effects of the constraints on trajectory shape and performance are studied and the analysis is used to bring out an optimal vehicle configuration at the initial stage of the design process itself. The research also reveals that the pattern search algorithm offers superior performance in comparison with the genetic algorithm for this class of optimization problem.     

Multiobjective optimization by genetic algorithms: application to safety systems

When attempting to optimize the design of engineered systems, the analyst is frequently faced with the demand of achieving several targets (e.g. low costs, high revenues, high reliability, low accident risks), some of which may very well be in conflict. At the same time, several requirements (e.g. maximum allowable weight, volume etc.) should also be satisfied. This kind of problem is usually tackled by focusing the optimization on a single objective which may be a weighed combination of some of the targets of the design problem and imposing some constraints to satisfy the other targets and requirements. This approach, however, introduces a strong arbitrariness in the definition of the weights and constraints levels and a criticizable homogenization of physically different targets, usually all translated in monetary terms.The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. For an efficient search through the solution space we use a multiobjective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. Based on this information, the decision maker can select the best compromise among these objectives, without a priori introducing arbitrary weights.     

In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network

Prediction/detection of exit burrs is critical in manufacturing automation. In this research, an intelligent burr sensing/monitoring scheme is proposed. Acoustic emission (AE) was selected to detect burr formation during drilling. For effective extraction of information contained in the collected AE signals, wavelet transform (WT) was adopted and the selected features through WT were fed into a back-propagation artificial neural net (ANN) as input vectors. To validate the in-process AE monitoring system, both WT-based ANN and cutting condition-based ANN outputs (cutting speed, feed, drill diameter, etc.) were compared with experimental data. The results show that the proposed scheme is not only efficient with fewer inputs, but more reliable in predicting drilling burr types over cutting condition-based ANN.     

基于加速遗传算法的组合证券投资决策

应用加速遗传算法解决组合证券投资决策问题，可以克服传统遗传算法的缺点：对搜索空间（优化变量空间）的大小变化适应能力差，计算量大，易出现早熟收敛，控制参数的设置技术无明确准则指导等，与已有结果相比，对协方差矩阵无正定性要求，目标函数可以推广到规模庞大，提高预测精度等优点。     

Scatter search and star-paths: beyond the genetic metaphor

Scatter search and genetic algorithms have originated from somewhat different traditions and perspectives, yet exhibit features that are strongly complementary. Links between the approaches have increased in recent years as variants of genetic algorithms have been introduced that embody themes in closer harmony with those of scatter search. Some researchers are now beginning to take advantage of these connections by identifying additional ways to incorporate elements of scatter search into genetic algorithm approaches. There remain aspects of the scatter approach that have not been exploited in conjunction with genetic algorithms, yet that provide ways to achieve goals that are basic to the genetic algorithm design. Part of the gap in implementing hybrids of these procedures may derive from relying too literally on the genetic metaphor, which in its narrower interpretation does not readily accommodate the strategic elements underlying scatter search. The theme of this paper is to show there are benefits to be gained by going beyond a perspective constrained too tightly by the connotations of the term genetic. We show that the scatter search framework directly leads to processes for combining solutions that exhibit special properties for exploiting combinatorial optimization problems. In the setting of zero-one integer programming, we identify a mapping that gives new ways to create combined solutions, producing constructions calledstar-paths for exploring the zero-one solution space. Star-path trajectories have the special property of lying within regions assured to include optimal solutions. They also can be exploited in association with both cutting plane and extreme point solution approaches. These outcomes motivate a deeper look into current conceptions of appropriate ways to combine solutions, and disclose there are more powerful methods to derive information from these combinations than those traditionally applied.This research is supported in part by the Joint Air Force Office of Scientific Research and Office of Naval Research Contract No. F49620-90-C-0033 at the University of Colorado     

Limits on the performance of dispersive thin-film stacks

Dispersive thin-film stacks are interesting as compact, cost-effective devices for temporal dispersion compensation and wavelength multiplexing. Their performance depends on the total group delay or spatial shift that can be achieved. For general multilayer stacks, no analytic model exists relating the performance to the stack parameters such as the refractive indices and the number of layers. We develop an empirical model by designing and analyzing 623 thin-film stacks with constant dispersion. From this analysis we conclude that, for given stack parameters, the maximum constant dispersion value is inversely proportional to the wavelength range over which the dispersion is achieved. This is equivalent to saying that, for constant dispersion, there is a maximum possible spatial shift (or group delay) that can be achieved for a given material system and number of layers. This empirical model is useful to judge the feasibility of dispersive photonic nanostructures and photonic crystal superprism devices and serves as a first step in the search for an analytic performance model. We predict that an 8-channel wavelength multiplexer can be realized with a single 21-microm-thick SiO2-Ta2O5 thin-film stack.     

A note on “A simple approach to search for all d-MCs of a limited-flow network”

Many real-world systems are multi-state systems composed of multi-state components in which the reliability can be computed in terms of the lower bound points of level d, called d-Mincuts (d-MCs). Such systems (electric power, transportation, etc.) may be considered as network flows whose arcs have independent and discrete random capacities. In this comment, we investigate the algorithm proposed by Yeh [A simple approach to search for all d-MCs of a limited-flow network. Reliability Engineering and System Safety 2001;71(1):15–19]. We show that the algorithm misses some real d-MCs, and then pinpoint the algorithm's failure and suggest the correcting point.     

MOAPPS 1.0: Aggregate production planning using the multiple-objective tabu search

In recent years, there has been a trend in the research community to solve large-scale complex planning and design problems using the modern heuristics optimization techniques (i.e. tabu search, genetic algorithms, etc.). This is mainly due to unsuitability of the classical solution techniques in many circumstances. Depending upon the assumptions made and the modelling approach used, aggregate production planning (APP) problems can be quite complex and large scale. Therefore, there is a need to investigate the suitability of modern heuristics for their solution. In this paper, the multiple-objective APP problem is formulated as a pre-emptive goal-programming model and solved by a specially developed multiple-objective tabu search algorithm. The mathematical formulation is built upon Masud and Hwang's model (original model) due to its extensibility characteristics. The present model extents their model by including subcontracting and setup decisions. The multiple-objective tabu search algorithm is applied to both the original and extended model. Results obtained from the solution of the original model are then compared. It is observed that the multiple-objective tabu search algorithm can be used as an alternative solution mechanism for solving APP problems. During this study, an object-oriented program is also developed using C++. This software is named as MOAPPS 1.0 (Multiple Objective Aggregate Production Planning Software).     

Times and costs analysis for sheet-metal cutting processes in an integrated CAD/CAM system

The aim of the present article is to develop a detailed time and cost analysis for process planning sheet-metal operations. A special computer module for determining the times and costs in sheet-metal operations has been developed and integrated in a CAD/CAM system which incorporates modules for nesting, toolpath generation, post-processing, etc. The special module allows all the geometrical and technological data from other modules in the CAD/CAM system (e.g. cutting conditions, toolpath parameters, types of material, cutting gases, etc.) required for the cost and time analysis to be automatically included without the need to manually enter the data in the module. This program can also be used as a 'stand alone' module, it has been developed with a graphic programming language (AutoLISP). Case studies are presented to highlight the benefits of using the program during practice.     

Integrated optimisation on flow-shop production with cutting stock

This paper addresses the integrated scheduling optimisation on flow-shop production with one-dimensional cutting stock in make-to-order environments. The upstream cutting-stock process provides the items for the downstream production, while the flow-shop production can be executed only when the required items are released. The efficient schedules on cutting plan and the production sequence should be determined in a coordinated manner to improve the overall efficiency. This study aims to find an integrated schedule to minimise the makespan of the entire manufacturing process. We develop a hybrid algorithm by integrating a local search method and some efficient strategies under the nested partitions framework. Numerical experiments show that the proposed approach is capable of achieving high-quality solutions within a reasonable time.     

Personal-computer-based analyzer of impedance parameters

It is shown that personal-computer-based instruments are most suitable for analyzing the possibilities of monitoring and investigating an object represented by an alternating-current circuit. A simple converter is described for measuring impedance parameters (C, L, R. G. etc.) over a range of 11–12 orders of magnitude at frequencies from 10 to 10⁵ Hz with a relative error better than 0.2%, or even considerably less if the transducer is calibrated against an external standard. Translated from Izmeritel'naya Tekhnika, No. 6, pp. 56–60, June, 1996     

Passive Magnetic Shielding of Stacked Joint-free Superconducting Annular Disks Made of REBCO Tapes

Journal of Superconductivity and Novel Magnetism - The joint-free superconducting annular disks and their stacks, obtained by cutting REBCO (REBa2Cu3O7–δ, RE: rare earth) wide tapes, are...     

Pattern reduction in one-dimensional cutting stock problems

In industrial cutting operations the number of cutting patterns needed to satisfy a given set of orders may be crucial for the capacity load which can be achieved for the cutting equipment, since switching between different patterns often necessitates time-consuming set-ups. Therefore, in planning the cutting operations, one does not necessarily search for input-minimal cutting plans (i.e. cutting plans minimizing material costs) only, but also for plans with a small (or even minimal) number of cutting patterns. Such plans are usually generated in a two-step approach: in a first step, regardless of the number of patterns needed, an inputminimal cutting plan is generated while in a second step the number of patterns is reduced. In this paper a new method for the second step is presented, which can be considered as a generalization of other methods previously suggested for this purpose. The performance of the new method is evaluated on the basis of 1800 randomly generated problem instances. The solution quality of the new method turns out to be clearly superior to the existing methods, however, an increase in computing times occurs for some problem classes. In this case it is possible to speed up the proposed method considerably at the expense of a slight decrease in solution quality.     

A neuron adaptive detecting approach of harmonic current for APFand its realization of analog circuit

There have been a number of harmonic current detecting methods for the active power filter (APF), including the filtration approach by fixed frequency filters, the composition method of the imaginary and the real power based on the instantaneous reactive power theory, etc. In this paper, firstly, according to the adaptive noise canceling technology in signal processing, an adaptive detecting approach of harmonic current based on a neuron is presented. Then, on the basis of the configuration and learning algorithm for the developed system, the realization scheme of an analog circuit of the system is discussed. Thirdly, using PSPICE software, computer simulation studies of the circuit are performed. Finally, experimental studies are carried out. The performance and feasibility of the approach are tested and verified by the simulation and experimental results     

Optimization of multipass turning operations with genetic algorithms

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in multipass machining operations. The cutting process simultaneously considers multipass rough machining and finish machining. The optimum machining parameters are determined by minimizing the unit production cost subject to practical machining constraints. The cutting model formulated is a non-linear-constrained programming (NCP) problem with 20 machining parameter constraints. Experimental results show that the proposed genetic algorithm-based procedure for solving the NCP problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.