Operating characteristics of X-bar charts with asymmetric control limits

When the assumptions behind the Shewhart chart are not met, policies other than the traditional 3-sigma limits may enable speedier and more economical detection of process change. If a process has unequal probabilities of downward and upward shifts, downward shifts of differing magnitude from upward shifts, and/or standard deviations after downward shifts different from those after upward shifts, then increases in the in-control average run length and decreases in the out-of-control average run length are simultaneously achievable with control limits placed at unequal distances from the process mean. The magnitude of these improvements is investigated for several types of process. The results are then extended to median charts for processes with output that is not normally distributed.     

X-bar charts or pre-control: an economic evaluation using alternative cost models

The on-line monitoring techniques of pre-control and X-bar control charts are compared, using two cost models that differ in the treatment of process variation cost. The first cost model assumes that process variation results in increased costs only when out-of-tolerance parts are made, while the second cost model uses Taguchi's loss function to quantify the cost of process variation. An example shows that the least-cost alternative depends on the cost model used. The sensitivity of the two techniques to changes in a number of process parameters is analysed for the loss function-based model. The results indicate that the most important consideration in choosing the process monitoring procedure is the assumption regarding the cost of process variation.     

Economic design of X-bar control charts for use in monitoring continuous flow processes

This paper develops the economic design of an A'-bar chart for monitoring continuous flow processes such as those encountered in refining, chemical processing and mining. The economic model permits one to optimally select the subgroup size, the sampling interval, and the control limit width based upon an economic criterion. This economic model is then applied to a realistic set of costs from the chemical industry. A sensitivity analysis is performed to illustrate the effects of incorrectly estimating the cost components of the model.     

Design of exponential control charts using a sequential sampling scheme

Control charts for monitoring the time between events can be applied in various areas. In this study, we focus on the exponential control chart and consider the phase II problem (when process parameters are known) as well as the phase I problem (when process parameters are unknown). An exponential chart designed with the conventional approach has the disadvantage that the Average Run Length (ARL) value may increase when the process deviates from the nominal state. An ARL-unbiased design approach is therefore proposed for both phase II and phase I exponential charts. A sequential sampling scheme is adopted for the phase I exponential chart. The proposed ARL-unbiased design approach has several advantages over the conventional one, as it provides a self-starting feature and can significantly improve the ARL performance. Specific guidelines are suggested regarding the time to stop updating the estimates of parameters and control limits based on the actual false alarm rate. The phase I exponential chart can be calibrated to a constant in-control ARL value for each successive event accumulated to date. Simulated and real data examples are given to demonstrate the use and efficiency of the proposed design approach.     

Optimal shape control of functionally graded smart plates using genetic algorithms

This paper deals with optimal shape control of functionally graded smart plate containing patches of piezoelectric sensors and actuators. The genetic algorithm (GA) is designed to search for optimal actuator voltage and displacement control gains for the shape control of the functionally graded material (FGM) plates. The work extends the earlier finite element formulations of the two leading authors, so that it can be readily treated using genetic algorithms. Numerical results have been obtained to study the effect of the shape control of the FGM plates under a temperature gradient by optimising (i) the voltage distribution for the open loop control, and (ii) the displacement control gain values for the closed loop feedback control. The effect of the constituent volume fractions of zirconia, through varying the volume fraction exponent n, on the optimal voltages and gain values has also been examined.     

Design optimization of composites using genetic algorithms and failure mechanism based failure criterion

In this paper, minimum weight design of composite laminates is presented using the failure mechanism based (FMB), maximum stress and Tsai–Wu failure criteria. The objective is to demonstrate the effectiveness of the newly proposed FMB failure criterion (FMBFC) in composite design. The FMBFC considers different failure mechanisms such as fiber breaks, matrix cracks, fiber compressive failure, and matrix crushing which are relevant for different loading conditions. A genetic algorithm is used for the optimization study. The Tsai–Wu failure criterion over predicts the weight of the laminate by up to 86% in the third quadrant of the failure envelope compared to FMB and maximum stress failure criteria, when the laminate is subjected to compressive–compressive loading. It is found that the FMB and maximum stress failure criteria give comparable weight estimates. The FMBFC can be considered for use in the strength design of composite structures.     

Monitoring automatically controlled processes using statistical control charts

Statistical process control (SPC) and automatic process control (APC) are integrated under a hierarchical scheme. The integrated scheme is called statistical and automatic process control (SAPC). SAPC is based on the comparison of the outputs of the actual system and parallel systems that model the actual system under assumed deterministic disturbances. While APC provides a continuous controller intervention, SPC acts as a supervisory controller to detect process disturbances. A cross-correlation method is used to estimate the time when the disturbance starts to affect the system as well as the type of the disturbance. After the magnitude of the disturbance is determined, a counter action is applied on the system to minimize its effect.     

Facility layout optimization using simulation and genetic algorithms

Traditionally, the objective of a facility layout problem has been to minimize the material handling cost of the manufacturing system. While it is important to reduce the amount of material handling, the traditional methods do not address the actual time at which the material is transported. In today's short cycle time production environments, the timing of material movement may have a bigger impact on the productivity of the system than its cost. In this paper, a facility layout optimization technique is presented that takes into consideration the dynamic characteristics and operational constraints of the system as a whole, and is able to solve the facility layout design problem based on a system's performance measures, such as the cycle time and productivity. Each layout solution is presented in the form of a string that is suitable for analysis by a genetic algorithm technique. These solutions are then translated into simulation models by a specially designed automated simulation model generator. Genetic algorithms are used to optimize the layout for manufacturing effectiveness while simulation serves as a system performance evaluation tool. Combined with a statistical comparison technique to reduce the simulation burden, the test results demonstrate that the proposed approach overcomes the limitations of traditional layout optimization methods and is capable of finding optimal or near optimal solutions.     

Engineering design optimization using species-conserving genetic algorithms

The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will help the exploration for significant designs.     

Control point policy optimization using genetic algorithms

This paper describes the application of an integrated Genetic Algorithm (GA)/Discrete Event Simulation model for selecting optimum values for Critical Point Policy (CPP) hedging time and buffer size parameters. The CPP is shown to perform well, when compared with the Critical Ratio priority rule, in terms of improving service levels, particularly when subject to conditions where buffer sizes and Takt times are required to be small. The technique developed involves buffer sizes being chosen by a GA according to a constraint on the total storage space available within the system. A method is described for reducing the number of variables that the GA needs to deal with, hence, improving the efficiency of the GA optimization process. The development and application work reported also provides further understanding into how and when the CPP should be applied.     

Calculating petal tools by using genetic algorithms

To pass from a spherical surface to a conic one, it is possible to use a petal tool or a small solid tool that is placed at different time intervals at several radial zones of the glass. Genetic algorithms are applied to calculate the angular sizes of the incomplete annular tools that make up the petal tools. We also present the desired wear results carried out with the petal tool that was designed on the basis of the dwell times of complete annular tools. These dwell times are calculated by using base functions that are generated with annular tools and by applying the genetic algorithms.     

Design and implementation of CUSUM exceedance control charts for unknown location

Nonparametric control charts provide a robust alternative in practice when the form of the underlying distribution is unknown. Nonparametric CUSUM (NPCUSUM) charts blend the advantages of a CUSUM with that of a nonparametric chart in detecting small to moderate shifts. In this paper, we examine efficient design and implementation of Phase II NPCUSUM charts based on exceedance (EX) statistics, called the NPCUSUM-EX chart. We investigate the choice of the order statistic from the reference (Phase I) sample that defines the exceedance statistic. We see that choices other than the median, such as the 75th percentile, can yield improved performance of the chart in certain situations. Furthermore, observing certain shortcomings of the average run-length, we use the median run-length as the performance metric. The NPCUSUM-EX chart is compared with the NPCUSUM-Rank chart based on the popular Wilcoxon rank-sum statistic. We also study the choice of the reference value, k, of the CUSUM charts. An illustration with real data is provided.     

Synthesis of optical multilayer systems using genetic algorithms

Stochastic algorithms are a promising method for the synthesis of optical multilayer systems. Amethod based on the use of genetic algorithms is described and applied to the design of three very different optical filters. Solutions found by genetic algorithms are refined, and results are compared with those of previous publications.     

面向质量目标的Q控制图设计

针对大批量生产开始阶段的过程监控,提出了一种基于预定质量目标的Q控制图监控方法.其基本思路是利用面向质量目标的统计公差技术与Q控制图相结合应用,以实现大批量过程开始阶段均值和方差未知时面向质量目标的过程监控.基于质量目标建立统计公差(CP*,k*),并将该统计公差转化为基于给定置信概率的对CP和k的估计值的判定条件.通过案例分析,面向质量目标的Q控制图能够在过程保持受控状态的前提下以一定置信概率保证质量目标.     

Robot arc welding task sequencing using genetic algorithms

This paper addresses welding task sequencing for robot arc welding process planning. Although welding task sequencing is an essential step in welding process planning, it has been considered through empirical knowledge, rather than a systematic approach. Thus, an effective task sequencing method for robot arc welding is required. Welding operations can be classified by the number of weldlines and layers. Genetic algorithms are applied to tackle those welding task sequencing problems in productivity and welding quality aspects. A genetic algorithm for the Traveling Salesman Problem (TSP) is utilized to determine welding task sequencing for a multiweldline-singlepass problem. Further, welding task sequencing for multiweldline-multipass welding is investigated and appropriate genetic algorithms are introduced. A random key genetic algorithm is presented to solve multi-robot welding task sequencing: mutliweldline with multiple robots. Finally, the genetic algorithms are implemented for the welding task sequencing of three-dimensional weld plate assemblies. Various simulation tests for a welded structure are performed to find the combination of genetic algorithm parameters suitable to weld sequencing problems and to verify the quality of genetic algorithm solutions. Robot operations for weld sequences are simulated graphically using the robot simulation software IGRIP.     

On solving facility layout problems using genetic algorithms

Tam and Chan (1998) present a parallel genetic algorithm approach to solve the facility layout problem. They adopt a slicing tree representation of a floor layout. The coding scheme represents a layout as a string with three parts. This paper demonstrates the difficulties in applying classical crossover and mutation operators for solving facility layout problems. The paper modifies the representation of Tam and Chan and introduces a new preserving operation, referred to as transplanting , that manages to produce feasible offspring. It also studies the applicability of other genetic operations such as diagonal crossover and cloning in generating feasible offspring. The paper is written in a note format and the reader may refer to Tam and Chan for more details.     

Multi-objective optimization using genetic algorithms: A tutorial

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.     

Identification of partial blockages in pipelines using genetic algorithms

A methodology to identify the partial blockages in a simple pipeline using genetic algorithms for non-harmonic flows is presented in this paper. A sinusoidal flow generated by the periodic on-and-off operation of a valve at the outlet is investigated in the time domain and it is observed that pressure variation at the valve is influenced by the opening size of blockage and its location. In this technique, the unsteady (steady oscillatory) pressure time series at only one location is required to identify two blockages. In the proposed methodology, the solution of the governing hyperbolic PDEs of pipe flow is obtained using the method of characteristics. For any piping system similar to the hypothetical pipe system used in the simulations, generalized best amplitude and best frequency of the valve operation are determined, which give maximum deviation in pressure responses for a specific blockage at different locations for a given constant-head reservoir. The generalized best amplitude and best frequency of the valve operation are also obtained for two blockages. Accuracy of the proposed methodology in identifying blockages in a hypothetical simple pipe system with increased noise in the simulated measurements is studied. A non-dimensional variable is proposed to determine whether the proposed methodology is applicable to isolate partial blockages in a piping system. Finally, the proposed methodology is experimentally validated on a laboratory piping system for a single blockage and two blockages.     

Parameters identification of excitation system models using genetic algorithms

A methodology is presented to identify parameters of non-linear models of excitation systems (ESs). Based on the use of genetic algorithms (GAs), the proposed methodology carries out simultaneous parameter identification of linear and non-linear model components. The computational algorithm allows to adequately identify model parameters and it is not affected by the noise present in the measurements. The application of this methodology was developed to identify and validate ES models of different technologies that are used in stability studies through dynamic simulations. First, model parameters of DC1A and ST1A type ES were determined in a simulation environment. The performance of two identifiers based on a GA paradigm is analysed: GA with arithmetic and intermediate recombination operators (GA-BASE) and GA based on differential evolution (GA-DE) mutation. Then the GA-DE identifier is applied to estimate parameters of a static ES (EXE) model of a Brazilian hydro power plant utilising measurements corrupted by noise and registered during field tests. The results obtained are satisfactory and the responses of the identified models are close to real system measurements.     

Blast furnace charging optimization using multi-objective evolutionary and genetic algorithms

Charging programs giving rise to desired burden and gas distributions in the ironmaking blast furnace were detected through an evolutionary multi-objective optimization strategy. The Pareto optimality condition traditionally used in such studies was substituted by a recently developed k-optimality criterion that allowed for simultaneous optimization of a large number of objectives, leading to a significant improvement over the results of earlier studies. A large number of optimum charging strategies were identified through this procedure and thoroughly analyzed, in view of an efficient blast furnace operation.