Parameter Design Problem Research Articles

Erratum to: Application of a linearly decreasing weight particle swarm to optimize the process conditions of al matrix nanocomposites

In this paper, SiC nanoparticles were added into the commercial casting Al–Si aluminum alloy to fabricate metal matrix nanocomposites (MMNCs) with uniform reinforcement distribution. Experimental results revealed that the presence of nano-SiC reinforcement led to significant improvement in hardness and UTS while the ductility of the aluminum matrix is retained. An integrated optimization approach using an artificial neural network and a modified particle swarm is proposed to solve a process parameter design problem in casting. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the particle swarm is used to perform the optimization with multiple objectives. The results showed that the particle swarm is an effective method for solving multi-objective optimization problems, and that an integrated approach using an artificial neural network and a modified particle swarm can be used to solve complex process parameter design problems.

다특성 파라미터설계 방법의 비교 연구

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

Concurrent fitness evaluations in searching for the optimal process conditions of Al matrix nanocomposites by linearly decreasing weight

In this article, the effect of nanoceramic reinforcements on the mechanical properties of aluminum matrix composites was critically reviewed. Reinforcement of an Al alloy with nanoceramic particulates is expected to improve its tensile strength. This article also reports the role of an integrated optimization approach using an artificial neural network and a modified particle swarm to solve a process parameter design problem in casting of this class of metal matrix nanocomposites. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the particle swarm is used to perform the optimization with multiple objectives.

Improving the lighting performance of a 3535 packaged hi-power LED using genetic programming, quality loss functions and particle swarm optimization

The lighting performance of a 3535 packaged hi-power LED (light-emitting diode) is mainly influenced by its geometric design and the refractive properties of its materials. In the past, engineers often determined the settings of the geometric parameters and selected the refractive properties of the materials through a trial-and-error process based on the principles of optics and their own experience. This procedure was costly and time-consuming, and its use did not ensure that the settings of the design parameters were optimal. Therefore, this study proposed a hybrid approach based on genetic programming (GP), Taguchi quality loss functions, and particle swarm optimization (PSO) to solve the multi-response parameter design problems. The feasibility and effectiveness of the proposed approach was demonstrated by a case study on improving the lighting performance of an LED. The confirmation results showed that all of the key quality characteristics of an LED fulfill the required specifications, and the comparison found that the proposed hybrid approach outperforms the traditional Taguchi method in solving this multi-response parameter design problem. The proposed hybrid approach can be extended to solve parameter design problems with multiple responses in various application fields.

Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector

The lighting performance of an LED (light-emitting diode) flash is significantly influenced by the geometric form of a reflector. Previously, design engineers have usually determined the geometric design of a reflector according to the principles of optics and their own experience. Some real reflectors have then been created to verify the feasibility and performance of a certain geometric design. This, however, is a costly and time-consuming procedure. Furthermore, the geometric design of a reflector cannot be proven to be actually optimal. This study proposes a systematic approach based on genetic programming (GP) and ant colony optimisation (ACO), called the GP–ACO procedure, to improve the geometric design of a reflector. A case study is used to demonstrate the feasibility and effectiveness of the proposed optimisation procedure. The results show that all the crucial quality characteristics of an LED flash fulfil the required specifications; thus, the optimal geometric parameter settings of the reflector obtained can be directly applied to mass production. Consequently, the proposed GP–ACO procedure can be considered an effective method for resolving general multi-response parameter design problems.

Calculation and Analysis of Drive Torque of Axial Magnetic Drive Mechanism

The magnetic circuit parameter design problem was proposed aiming at axial magnetic drive mechanism driving torque. Analyzed axial magnetic drive mechanism driving principle .According to the mathematics model of the magnetic coupling torque that was established based on the equivalent magnetic charge theory, analyzed magnetic circuit design, included material selection, magnetic circuit type, the working air gap of magnetic circuit, magnet and the thickness of the magnets and resistive magnets. The basis was provided for the rational design of high-performance axial magnetic drive mechanism.

Prediction variance performance of combined array designs

Many experiments involve variables that can be easily controlled and variables that are difficult to control (noise). In robust design one goal is to determine the settings of the controllable factors that optimise the response while simultaneously minimise the variability transmitted to the response from the noise variables. This simultaneous optimisation can be addressed using a model for the mean response and a model for the transmitted variability to fit both the control and noise variables. Combined array designs are widely used in these robust parameter design problems. We extend previous work in this area for a larger number of factors. Specifically, we investigate the prediction variance performance of a variety of combined array designs for five to 20 variables and various combinations of control and noise variables. The design region for the control variables is spherical. Prediction variance results for a number of designs and some recommendations for their use are provided.

Robust Design of Horizontal Axis Wind Turbines Using Taguchi Method

The robust design of horizontal axis wind turbines, including both parameter design and tolerance design, is presented. A simple way of designing robust horizontal axis wind turbine systems under realistic conditions is outlined with multiple design parameters (variables), multiple objectives, and multiple constraints simultaneously by using the traditional Taguchi method and its extensions. The performance of the turbines is predicted using the axial momentum theory and the blade element momentum theory. In the parameter design stage, the energy output of the turbine is maximized using the Taguchi method and an extended penalty-based Taguchi method is proposed to solve constrained parameter design problems. The results of the unconstrained and constrained parameter design problems, in terms of the objective function and constraints are compared. Using an appropriate set of tolerance settings of the parameters, the tolerance design problem is formulated so as to yield an economical design, while ensuring a minimal variability in the performance of the wind turbine. The resulting multi-objective tolerance design problem is solved using the traditional Taguchi method. The present work provides a simple and economical approach for the robust optimal design of horizontal axis wind turbines.

Optimization of tile manufacturing process using particle swarm optimization

In this paper, an integrated optimization approach using an artificial neural network and a bidirectional particle swarm is proposed. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the bidirectional particle swarm is used to perform the optimization with multiple objectives. Finally, the proposed approach is used to solve a process parameter design problem in cement roof-tile manufacturing. The results showed that the bidirectional particle swarm is an effective method for solving multi-objective optimization problems, and that an integrated approach using an artificial neural network and a bidirectional particle swarm can be used to solve complex process parameter design problems.

Robust parameter design optimization of simulation experiments using stochastic perturbation methods

Stochastic perturbation methods can be applied to problems for which either the objective function is represented analytically, or the objective function is the result of a simulation experiment. The Simultaneous Perturbation Stochastic Approximation (SPSA) method has the advantage over similar methods of requiring only two measurements at each iteration of the search. This feature makes SPSA attractive for robust parameter design (RPD) problems where some factors affect the variance of the response(s) of interest. In this paper, the feasibility of SPSA as a RPD optimizer is presented, first when the objective function is known, and then when the objective function is estimated by means of a discrete-event simulation.

Creating Polytope Representations of Design Spaces for Visual Exploration Using Consistency Techniques

Understanding the limits of a design is an important aspect of the design process. When mathematical models are constructed to describe a design concept, the limits are typically expressed as constraints involving the variables of that concept. The set of values for the design variables that do not violate constraints constitute the design space of that concept. In this work, we transform a parametric design problem into a geometry problem thereby enabling computational geometry algorithms to support design exploration. A polytope-based representation is presented to geometrically approximate the design space. The design space is represented as a finite set of (at most) three-dimensional (possibly nonconvex) polytopes, i.e., points, intervals, polygons, and polyhedra. The algorithm for constructing the design space is developed by interpreting constraint-consistency algorithms as computational-geometric operations and consequently extending (3,2)-consistency algorithm for polytope representations. A simple example of a fingernail clipper design is used to illustrate the approach.

Artificial neural network based approach for dynamic parameter design

The Taguchi parameter design method has been recognized as an important tool for improving the quality of a product or a process. However, the statistical methods and optimization procedures proposed by Taguchi have much room for improvement. For instance, the two-step procedure proposed by Taguchi may fail to identify an optimum design condition if an adjustment parameter does not exist, the optimal setting of a design parameter is determined only among the levels included in the parameter design experiment, and, for the dynamic parameter design, the signal parameter is assumed to follow a uniform rather than a general distribution. This paper develops an artificial neural network based dynamic parameter design approach to overcome the shortcomings of the Taguchi and existing alternative approaches. First, an artificial neural network is trained to map the relationship between the characteristic, design, noise and signal parameters. Second, Latin hypercube samples of the signal and noise parameters are obtained and used to estimate the slope between the signal parameter and characteristic as well as the variance of the characteristic at each set of design parameter settings. Then, the dynamic parameter design problem is formulated as a nonlinear optimization problem and solved to find the optimal settings of the design parameters using sequential quadratic programming. The effectiveness of the proposed approach is illustrated with an example.

Modeling a dynamic design system using the Mahalanobis Taguchi system — two steps optimal based neural network

This work presents a novel algorithm, the MTS-TSO based Neural Network (NN) algorithm, which combines the Mahalanobis Taguchi System (MTS) with the Two-Step Optimal (TSO) method for parameter selections which are adjusted under a dynamic environment for product parameter design. The utility of the algorithm is assessed in two dimensions: the MTS shows how individual product parameter dimensions are selected; and, the TSO-NN links parameters selection decisions across two different times and it can be used to focus on dynamic system design (DSD) and to identify product architecture dimensions that are critical for a dynamic design system strategy. The MTS which can easily solve product parameter design problems and shows it’s computationally efficient in the previous works. Additionally, the TSO algorithm is a simple and efficient means of constructing a dynamic design system, which is verified by the neural network algorithm from this work, and the neural network algorithm from this work, and the neural network is already successfully applied in dynamic system of the past studies. Based on the main aims and verifies of this work, we conclude that the MTS-TSO based neural network algorithm can be applied successfully to dynamic environments for solving product design problems.

Robust Multi‐response Parameter Design based on RSM

This paper discusses multi‐response robust parameter design problems based on response surface method. Most research effort on multi‐response parameter design problem focuses much on finding out optimal parameters based on certain criteria or objectives. Research shows that optimal solution in terms of some criteria may not be robust. To achieve robust solution we should consider how sensitive the solution is when the factors change around it. A comparative study of methods for multi‐response robust parameter design is conducted. Solution with consideration of robustness and optimality is proposed with applications of the example.

PCB design improvement in the circuit between the north bridge and SDRAM through an integrated procedure

Printed circuit boards (PCBs) are the primary base of electronic components, and they exist in almost all electronic devices. The PCB stackup design, physical rule settings, and electrical rule settings are the three main factors influencing the signal quality in a PCB. An improper PCB stackup design and physical and electrical rule settings will cause a high-speed signal out of the defined specification, and ultimately contribute to an unreliable device. Experienced engineers usually refer to the PCB design guides and chipset specifications recommended by the integrated circuit (IC) supplier, and determine the optimal PCB design parameter settings through trial and error. However, this approach cannot guarantee that parameter settings are truly optimal. To solve PCB design problems, this study proposes an integrated procedure based on a neural network, desirability functions, and genetic algorithms. A case involving PCB design improvements in the circuit from the north bridge (NB) to SDRAM in a personal computer (PC) is used to demonstrate the feasibility and effectiveness of the proposed optimization procedure. Results show that each quality characteristic very nearly approaches its perfect state. Furthermore, all of the crucial quality characteristics fulfill the required specifications and achieve a 100% yield from the six sigma quality viewpoint. Consequently, the proposed procedure can be considered an effective method of resolving multi-response parameter design problems.

Learning Design Rules With Explicit Termination Conditions to Enable Efficient Automated Design

We present a two-step technique for learning reusable design procedures from observations of a designer in action. This technique is intended for the domain of parametric design problems in which the designer iteratively adjusts the parameters of a design so as to satisfy the design requirements. In the first step of the two-step learning process, decision tree learning is used to infer rules that predict which design parameter the designer is likely to change for any particular state of an evolving design. In the second step, decision tree learning is again used, but this time to learn explicit termination conditions for the rules learned in the first step. The termination conditions are used to predict how large of a parameter change should be made when a rule is applied. The learned rules and termination conditions can be used to automatically solve new design problems with a minimum of human intervention. Experiments with this technique suggest that it can reproduce the decision making process observed from the designer, and it is considerably more efficient than the previous technique, which was incapable of learning explicit rule termination conditions. In particular, the rule termination conditions allow the new program to automatically solve design problems with far fewer iterations than previously required.

Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem

This paper addresses a multi-stage job-shop parallel-machine-scheduling problem with an ant colony optimization system developed. The problem is practically important and yet more complex, especially when customer order splitting in multiple lots for the reduction of operation times in each workstation is allowed. It also includes the decisions of the numbers of parallel machines in workstations dynamically scheduled. In addition, this paper also addresses the multiple-objectives scheduling. For the practical concern, in addition to the production (or quantitative) objectives, the marketing (strategic or qualitative) criteria are also considered. A soft constraint thus may be realized from a thus-called qualitatively evaluated order sequence. The soft constraint with the ant colony optimization solution constructs a penalty function for the multiple qualitative objectives and the results of scheduling obtained by ant colony optimization. For this problem, the ant colony optimization components (including the network representation, tabu lists, transition probabilities, and pheromone trail updating) are also developed and adapted for the multiple objectives. The experiment results of parameter design and different problem sizes are provided. The results of a genetic algorithm also developed for the present problem under the developed system concept are also provided, since in the literature the genetic algorithm has also not been explored for the present problem with multiple objectives and order splitting. The results of both solution techniques show the potential usefulness of the system and are comparable, but the ant colony optimization provides a more computationally efficient better result.

Performance improvement of software-based system using an integrated approach – a case study

The modern automation system consists of software and hardware components to achieve the high quality products and processes. In such type of software-based systems, optimal design is more important to improve the system performance. The perfect parameter design problems are complex because of non-linear relationships and interactions may occur among parameters. So, a proper approach is needed for a parameter optimal design. An integrated approach of neural network with genetic algorithms is proposed to address the optimal design of software-based automation system. This article outlines neural network methodology to predict the response of the software-based automation system for various process parameters values. Then, the genetic algorithm is used to predict the quantitative value of process parameter to improve the performance of the system. In this work, a cascading electro-pneumatic kit is taken as case analysis to analyse the performance of software-based system.

Optimal Designs for Mixture-Process Experiments with Control and Noise Variables

Mixture experiments that involve process variables are widely encountered in industry. When some of the process variables are noise variables, interest focuses on finding levels for the mixture components and the controllable process variables that result in a product that has a desirable mean response and that has small variability in the response transmitted from the noise variables. This is a variation of the robust parameter design problem. Choosing an appropriate experimental design for this type of problem is addressed in the paper. We show how designs that have small prediction variance for the mean and the slope variance can be obtained. We also show how designs that are robust to the level of interaction between control and noise variables can be constructed.

DYNAMIC PARAMETER DESIGN BY ANT COLONY OPTIMIZATION AND NEURAL NETWORKS

Parameter design is the most important phase in the development of new products and processes, especially in regards to dynamic systems. Statistics-based approaches are usually employed to address dynamic parameter design problems; however, these approaches have some limitations when applied to dynamic systems with continuous control factors. This study proposes a novel three-phase approach for resolving the dynamic parameter design problems as well as the static characteristic problems, which combines continuous ant colony optimisation (CACO) with neural networks. The proposed approach trains a neural network model to construct the relationship function among response, inputs and parameters of a dynamic system, which is then used to predict the responses of the system. Three performance functions are developed to evaluate the fitness of the predicted responses. The best parameter settings can be obtained by performing a CACO algorithm according to the fitness value. The best parameter settings that are obtained are no longer restricted to the values of control factor levels. The proposed approach is demonstrated with two illustrative examples. Results show that the proposed approach outperforms the Taguchi method.