Test method for enhanced mechanical shock fragility statistics accuracy

An optimum cushioning package, which is neither excessive nor inadequate, must be designed to ensure cushioning performance that maintains an acceptable failure rate during transportation while also minimizing packaging costs. For this purpose, statistics pertaining to transport hazards and product shock strength must be engaged. The proposed study presents a test method to enhance the statistical accuracy of mechanical shock fragility of products. Sample statistics are invariably unknown; hence, optimum test‐setting values cannot be determined at the beginning. The proposed test method has been devised for determining optimum test‐setting values of the (n + 1)th sample using statistics of n samples being tested. An improvement in the estimation accuracy of the variation coefficient for the critical‐velocity‐change test was confirmed via simulations performed using the proposed method. Optimization of the test‐setting value has also been experimentally confirmed. A comparison of histograms and statistics obtained using experimental results has demonstrated that the proposed method can better estimate distribution shapes compared with the simple method. An example of the application of experimental results to stress‐strength models has also been described. The observed result has a considerable influence on the design of cushioning packages, thereby demonstrating effectiveness of the proposed method.     

ASRSM: A Sequential Experimental Design for Response Surface Optimization

Most preset response surface methodology (RSM) designs offer ease of implementation and good performance over a wide range of process and design optimization applications. These designs often lack the ability to adapt the design on the basis of the characteristics of application and experimental space so as to reduce the number of experiments necessary. Hence, they are not cost‐effective for applications where the cost of experimentation is high or when the experimentation resources are limited. In this paper, we present an adaptive sequential response surface methodology (ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and high design optimization performance requirement. The proposed approach is a sequential adaptive experimentation approach that combines concepts from nonlinear optimization, design of experiments, and response surface optimization. The ASRSM uses the information gained from the previous experiments to design the subsequent experiment by simultaneously reducing the region of interest and identifying factor combinations for new experiments. Its major advantage is the experimentation efficiency such that for a given response target, it identifies the input factor combination (or containing region) in less number of experiments than the classical single‐shot RSM designs. Through extensive simulated experiments and real‐world case studies, we show that the proposed ASRSM method outperforms the popular central composite design method and compares favorably with optimal designs. Copyright © 2012 John Wiley & Sons, Ltd.     

Response surface optimization for a nonlinearly constrained irregular experimental design space

Finding optimum conditions for process factors in an engineering optimization problem with response surface functions requires structured data collection using experimental design. When the experimental design space is constrained owing to external factors, its design space may form an asymmetrical and irregular shape and thus standard experimental design methods become ineffective. Computer-generated optimal designs, such as D-optimal designs, provide alternatives. While several iterative exchange algorithms for D-optimal designs are available for a linearly constrained irregular design space, it has not been clearly understood how D-optimal design points need to be generated when the design space is nonlinearly constrained and how response surface models are optimized. This article proposes an algorithm for generating the D-optimal design points that satisfy both feasibility and optimality conditions by using piecewise linear functions on the design space. The D-optimality-based response surface design models are proposed and optimization procedures are then analysed.     

基于代理模型和改进遗传算法的注塑翘曲优化

提出一种最小化制品翘曲的注塑工艺参数优化集成方法.以空调柜机顶盖注塑制品开发为例,该方法使用Moldflow软件分析制品的翘曲变形,运用田口方法确定与制品翘曲量密切相关的工艺因素,然后采用响应曲面法(RSM)和改进的精英保留自适应遗传算法(EAGA)相结合的方法,建立主要影响工艺参数与制品翘曲量之间的关系模型,通过对模型寻优以实现对制品翘曲的优化.该方法的适用性在制品的实际生产中得到了验证.     

A comparison of designs for one‐step screening and response surface estimation

The sequential design approach to response surface exploration is often viewed as advantageous as it provides the opportunity to learn from each successive experiment with the ultimate goal of determining optimum operating conditions for the system or process under study. Recent literature has explored factor screening and response surface optimization using only one three‐level design to handle situations where conducting multiple experiments is prohibitive. The most straightforward and accessible analysis strategy for such designs is to first perform a main‐effects only analysis to screen important factors before projecting the design onto these factors to conduct response surface exploration. This article proposes the use of optimal designs with minimal aliasing (MA designs) and demonstrates that they are more effective at screening important factors than the existing designs recommended for single‐design response surface exploration. For comparison purposes, we construct 27‐run MA designs with up to 13 factors and demonstrate their utility using established design criterion and a simulation study. Copyright 2011 © John Wiley & Sons, Ltd.     

Robust parameter design using a supersaturated design for a response surface model

Recently, the application of response surface methodology (RSM) to robust parameter design has attracted a great deal of attention. In some cases, experiments are very expensive and may require a great deal of time to perform. Central composite designs (CCDs) and Box and Behnken designs (BBDs), which are commonly used for RSM, may lead to an unacceptably large number of experimental runs. In this paper, a supersaturated design for RSM is constructed and its application to robust parameter design is proposed. A response surface model is fitted using data from the designed experiment and a stepwise variable selection. An illustrative example is presented to show that the proposed method considerably reduces the number of experimental runs, as compared with CCDs and BBDs. Numerical experiments are also conducted in which type I and II error rates are evaluated. The results imply that the proposed method may be effective for finding the effects (i.e. main effects, two‐factor interactions, and pure quadratic effects) of active factors under the ‘effect sparsity’ assumption. Copyright © 2010 John Wiley & Sons, Ltd.     

A Hopfield Neural Network Approach to the Dual Response Problem

The application of neural networks to optimization problems has been an active research area since the early 1980s. Unconstrained optimization, constrained optimization and combinatorial optimization problems have been solved using neural networks. This study presents a new approach using Hopfield neural networks (HNNs) for solving the dual response system (DRS) problems. The major aim of the proposed method is to produce a string of solutions, rather than a ‘one‐shot’ optimum solution, to make the trade‐offs available between the mean and standard deviation responses. This gives more flexibility to the decision‐maker in exploring alternative solutions. The proposed method has been tested on two examples. The HNN results are very close to those obtained by using the NIMBUS (Nondifferentiable Interactive Multiobjective Bundle‐based Optimization System) algorithm. Choosing an appropriate solution method for a certain multi‐objective optimization problem is not easy, as has been made abundantly clear. Unlike the NIMBUS method, the HNN approach does not set any specific assumptions on the behaviour or the preference structure of the decision maker. As a result, the proposed method will still work and generate alternative solutions whether or not the decision maker has enough time and capabilities for co‐operation. Copyright © 2005 John Wiley & Sons, Ltd.     

ADAPTIVE RESPONSE SURFACE METHOD - A GLOBAL OPTIMIZATION SCHEME FOR APPROXIMATION-BASED DESIGN PROBLEMS

For design problems involving computation-intensive analysis or simulation processes, approximation models are usually introduced to reduce computation lime. Most approximation-based optimization methods make step-by-step improvements to the approximation model by adjusting the limits of the design variables. In this work, a new approximation-based optimization method for computation-intensive design problems - the adaptive response surface method(ARSM), is presented. The ARSM creates quadratic approximation models for the computation-intensive design objective function in a gradually reduced design space. The ARSM was designed to avoid being trapped by local optima and to identify the global design optimum with a modest number of objective function evaluations. Extensive tests on the ARSM as a global optimization scheme using benchmark problems, as well as an industrial design application of the method, are presented. Advantages and limitations of the approach are also discussed     

An Efficient Adaptive Sequential Methodology for Expensive Response Surface Optimization

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptive sequential methodology based on optimal design and experiments ranking for response surface optimization (O‐ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and requiring high design optimization performance. The proposed approach combines the concepts from optimal design of experiments, nonlinear optimization, and RSM. By using the information gained from the previous experiments, O‐ASRSM designs the subsequent experiment by simultaneously reducing the region of interest and by identifying factor combinations for new experiments. Given a given response target, O‐ASRSM identifies the input factor combination in less number of experiments than the classical single‐shot RSM designs. We conducted extensive simulated experiments involving quadratic and nonlinear response functions. The results show that the O‐ASRSM method outperforms the popular central composite design, the Box–Behnken design, and the optimal designs and is competitive with other sequential response surface methods in the literature. Furthermore, results indicate that O‐ASRSM's performance is robust with respect to the increasing number of factors. Copyright © 2012 John Wiley & Sons, Ltd.     

Low‐cost response surface methods from simulation optimization

We propose ‘low‐cost response surface methods’ (LCRSMs) that typically require half the experimental runs of standard response surface methods based on central composite and Box Behnken designs, but yield comparable or lower modeling errors under realistic assumptions. In addition, the LCRSMs have substantially lower modeling errors and greater expected savings compared with alternatives with comparable numbers of runs, including small composite designs and computer‐generated designs based on popular criteria such as D‐optimality. The LCRSM procedures appear to be the first experimental design methods derived as the solution to a simulation optimization problem. Together with modern computers, simulation optimization offers unprecedented opportunities for applying clear, realistic multicriterion objectives and assumptions to produce useful experimental design methods. We compare the proposed LCRSMs with alternatives based on six criteria. We conclude that the proposed methods offer attractive alternatives when the experimenter is considering dropping factors to use standard response surface methods or would like to perform relatively few runs and stop with a second‐order model. Copyright © 2002 John Wiley & Sons, Ltd.     

遗传算法中约束的凝聚选择和复合形处理方法

本文在实数编码的遗传算法中对约束条件提出一种凝聚选择和复合形法处理的方法。利用凝聚函数求出个体的约束违背值,在选择中不仅考虑适应度值而且考虑约束违背值,使有潜力的个体优先被选择。利用复合形法对违背约束的个体进行改进,从而改善整个种群的性能、增加可行个体数量,有利于最优解的搜索。算例表明本文方法是可行和有效的。     

Hybrid approximation algorithm with Kriging and quadratic polynomial‐based approach for approximate optimization

This paper describes a new hybrid algorithm that uses a Kriging and quadratic polynomial‐based approach for approximate optimization. The Kriging method is used for generating a global approximation model, and the polynomial‐based approximation method is used for generating a local approximation model. The Kriging system is only used to construct a polynomial‐based locally approximate model by estimating some function values and Hessian components of an estimated surface. The number of Kriging estimations can be reduced in comparison with direct Kriging‐based optimization, and a local optimum solution on an approximated surface can be clearly estimated without use of an optimization procedure based on a local appropriate quadratic polynomial model. Numerical examples of engineering optimization using the proposed method illustrate validity and effectiveness of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.     

Multi-objective optimization of a stirling-type pulse tube refrigerator

A genetic algorithm (GA) optimization method which is coupled to a one-dimensional finite volume method is proposed and implemented as a computer program for the modeling and optimization of a stirling-type pulse tube refrigerator (PTR). The multi-objective optimization procedure is applied to provide the optimization design parameters which are charge pressure, operating frequency, and temperature of after-cooler as well as swept volume of compressor. The procedure is selected to obtain the maximum coefficient of performance (COP) and the minimum cooling temperature (T_cold) as two objective functions. In order to validate the simulation code, the results were compared with the results of other models and experiments. The results showed a reasonably well agreement between simulation output and experimental data. The results of optimal designs are a set of multiple optimum solutions, called Pareto optimal solutions. Moreover, the closed form relations between two objectives are derived for Pareto optimal solutions of pulse tube refrigerator. Finally, a sensitivity analysis of the variation of each design parameter on both objective functions was carried out as well and the results are presented. As a result, the COP is more sensitive than T_cold in the optimum design points. The frequency of refrigerator is the most sensitive factor which affects the COP even with little changes.     

Response Surface Optimization in the Presence of Internal Noise With Application to Optimal Alignment of Carbon Nanotubes

Internal noise, which means fluctuation of input factors around their set values, is common in many experiments in the physical and engineering sciences. Existing methods for response surface optimization in the presence of internal noise typically adopt a two-step approach: (a) fitting a response model as a function of the set value and (b) using Monte Carlo methods to account for internal noise while optimizing the response. In this article, motivated by a problem in optimizing alignment of carbon nanotubes (CNT), we propose a Bayesian approach for response surface optimization in the presence of internal noise. A unit-free and interpretable measure to quantify the strength of internal noise is proposed. Suitable objective functions or performance measures consistent with the overall goal of optimizing the response function are identified, methods for estimating them from available experimental data are suggested, and simulations are conducted to compare them with respect to their ability to account for internal noise in the optimization problem. The loss accrued by ignoring the internal noise in the optimization problem is quantified and studied via simulation. The proposed method is demonstrated through its application in the CNT alignment problem.     

Design and performance analysis of code families for multi-dimensional optical CDMA

A code design algorithm for application in multi-dimensional optical code division multiple access (MD-OCDMA) for asynchronous optical fibre communication is proposed. Two-dimensional (2D) wavelength-time or space-time OCDMA and three-dimensional (3D) space-wavelength-time OCDMA are subsets of MD-OCDMA. Some applications and the performance analysis of the algorithm in 2D multipulse per row codes and 3D multipulse per plane codes are shown. In the applications discussed, this design ensures a maximum cross-correlation of '1' between any two codes. The performance metrics studied are the probability of error due to multiple-access interference for different numbers of active users and optimum temporal length for different values of cardinality. The performance analysis shows that the proposed 2D design offers very low probability of error due to multiple-access interference at lower cardinality when compared with other 2D designs using equivalent code dimension. A comparison of the proposed 3D design with an existing 3D design shows better performance at lower cardinality. The 3D designs show better performance when compared with the 2D designs.     

面内压缩超轻质点阵夹芯板的优化、 试验与仿真

为揭示点阵材料在航空航天工程中的应用潜力,对承受面内压缩载荷点阵夹芯板的力学行为进行了研究。基于夹芯板整体欧拉失稳、剪切失稳、格间局部失稳、跨格局部失稳和应力破坏多种理论失效模式,引入面板厚度、厚度方向的点阵层数、点阵杆件长度、截面尺寸、倾斜度、胞元长细比等优化变量,推导了点阵夹芯板的最小质量优化设计方法。同时利用激光选取熔融（SLM）增材制造工艺生产了点阵夹芯板试验件。随后,采用有限元方法对试验结果进行了仿真分析,两者误差在10%以内,证实了数值方法的准确性。最终对初始设计和优化设计方案进行了数值分析,发现优化方案在保持相同承载力的条件下,实现结构减重16.6%,验证了优化设计方法的有效性。同时,试验与仿真的一致性有力地证明了增材制造工艺在点阵夹芯结构制造方面的可行性。      

Optimization of seismic isolation systems via harmony search

In this article, the optimization of isolation system parameters via the harmony search (HS) optimization method is proposed for seismically isolated buildings subjected to both near-fault and far-fault earthquakes. To obtain optimum values of isolation system parameters, an optimization program was developed in Matlab/Simulink employing the HS algorithm. The objective was to obtain a set of isolation system parameters within a defined range that minimizes the acceleration response of a seismically isolated structure subjected to various earthquakes without exceeding a peak isolation system displacement limit. Several cases were investigated for different isolation system damping ratios and peak displacement limitations of seismic isolation devices. Time history analyses were repeated for the neighbouring parameters of optimum values and the results proved that the parameters determined via HS were true optima. The performance of the optimum isolation system was tested under a second set of earthquakes that was different from the first set used in the optimization process. The proposed optimization approach is applicable to linear isolation systems. Isolation systems composed of isolation elements that are inherently nonlinear are the subject of a future study. Investigation of the optimum isolation system parameters has been considered in parametric studies. However, obtaining the best performance of a seismic isolation system requires a true optimization by taking the possibility of both near-fault and far-fault earthquakes into account. HS optimization is proposed here as a viable solution to this problem.     

Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms

This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as the objective functions and are treated simultaneously for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are considered as design variables for the multi-objective optimization. An application example is presented to demonstrate the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation of the algorithm parameters on the convergence and fitness values of the objective functions are reported.     

Application of the nonlinear, double-dynamic Taguchi method to the precision positioning device using combined piezo-VCM actuator

In this research, the nonlinear, double-dynamic Taguchi method was used as design and analysis methods for a high-precision positioning device using the combined piezo-voice-coil motor (VCM) actuator. An experimental investigation into the effects of two input signals and three control factors were carried out to determine the optimum parametric configuration of the positioning device. The double-dynamic Taguchi method, which permits optimization of several control factors concurrently, is particularly suitable for optimizing the performance of a positioning device with multiple actuators. In this study, matrix experiments were conducted with L9(3(4)) orthogonal arrays (OAs). The two most critical processes for the optimization of positioning device are the identification of the nonlinear ideal function and the combination of the double-dynamic signal factors for the ideal function's response. The driving voltage of the VCM and the waveform amplitude of the PZT actuator are combined into a single quality characteristic to evaluate the positioning response. The application of the double-dynamic Taguchi method, with dynamic signal-to-noise ratio (SNR) and L9(3(4)) OAs, reduced the number of necessary experiments. The analysis of variance (ANOVA) was applied to set the optimum parameters based on the high-precision positioning process.     

HYBRID MULTIMODAL OPTIMIZATION WITH CLUSTERING GENETIC STRATEGIES

Abstract

Two-stage hybrid multimodal optimization approaches that combine cluster identification techniques in genetic algorithms with sharing and gradient-based local search methods are proposed. The multimodal optimization comprises the use of a sharing function implementation in genetic searches to pursue multiple local optima and subsequent executions of local searches to locate each local optimum when an extreme-containing region is identified. A new cluster identification technique is proposed for automatic and adaptive identification of the locations and sizes of design clusters in genetic algorithms with sharing. The first stage of the hybrid multimodal optimization is to use sharing-enhanced genetic algorithms for the identification of the near-optimum designs inside extreme-containing regions. The second stage simply involves consecutive employment of efficient gradient-based local searches by using the near-optimum designs as initial designs. Two strategies defining the coupling of the genetic search and local searches are proposed. The proposed hybrid optimization strategies are tested in a number of illustrative multimodal optimization problems.