Optimal levels of process parameters for products with multiple characteristics

The purpose of off-line quality control is to design robust products using robust manufacturing processes before the actual manufacturing of the product. Most of the research work has focused on determining the optimal level settings of process parameters for products with a single quality characteristic. In this paper, we employ the loss function approach to determine the optimal level settings of the process parameters of the production processes for products with multiple characteristics.     

Joint optimization in process target and tolerance limit for L -type quality characteristics

Most models reported in the current process target literature assume a nominal-the-best type quality characteristic in determining the optimal process mean value. In real-world industrial settings, however, a number of quality characteristics of interest are of a larger-the-better type (L-type), and the problem of jointly determining the optimum process mean and tolerance limit for the L-type quality characteristic may be of significant practical importance, yet this has largely been overlooked in the literature. In this paper, cost-effective optimization models are developed, and the methods of finding optimum solutions are presented. Numerical examples and sensitivity analysis are given.     

Quality evaluation of internal cylindrical grinding process with multiple quality characteristics for gear products

Gears are among the most crucial components in the transmission systems of machine tools. Gear manufacturing includes a number of processing procedures. The grinding process is an important procedure involving high precision and fairly small grinding surfaces. For this reason, this study aimed at developing a quality assessment model for the internal cylindrical grinding process of gears. The Six Sigma quality indices (SSQIs) were used to directly assess the quality of the internal cylindrical grinding process due to their ability to directly reflect quality level and process yield. Since the process may include nominal-the-best (NTB), larger-the-better (LTB) and smaller-the-better (STB) quality characteristics, so we used the variable transformation method to normalise the specifications of each quality characteristic for the convenient and effective management and analysis of process performance for multiple quality characteristics. We then constructed a multi-characteristic process quality analysis chart (MPQAC) to simultaneously assess the quality levels of various quality characteristics. Furthermore, the MPQAC can provide references for process improvement. This ensures the quality of internal cylindrical grinding and enhances the quality of gear and machine tool products. Finally, a real-world application and numerical experiments demonstrate the effectiveness and practical applicability of the proposed method.     

Economic design of the specification region for multiple quality characteristics

Economic specification limits have typically been developed on the basis of a single quality characteristic. From the viewpoint of the customer, products are often evaluated based on multiple quality characteristics. The specification region for multiple quality characteristics must be determined on an economic basis where we minimize the total loss to both the producer and the customer and thus to the whole society. In this paper a multivariate normal distribution is considered for the quality characteristics. The specification region is given by truncating the multivariate normal distribution. We present the optimization model to develop the specification region for multiple quality characteristics based on the framework of multivariate quality loss function.     

Measuring production yield for processes with multiple quality characteristics

Process yield is an important criterion used in the manufacturing industry for measuring process performance. Methods for measuring yield for processes with single characteristic have been investigated extensively. However, methods for measuring yield for processes with multiple characteristics have been comparatively neglected. In this paper, we develop a generalized yield index, called TS _pk,PC , based on the index S_pk introduced by Boyles (Journal of Quality Technology, 23, 17–26, 1991 Boyles, RA. 1991. The Taguchi capability index. J. Qual. Technol., 23: 17–26. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) using the principal component analysis (PCA) technique. We obtained a lower confidence bound (LCB) for the true process yield. The proposed method can be used to determine whether a process meets the preset yield requirement, and make reliable decisions. Examples are provided to demonstrate the proposed methodology.     

Solving the optimal process target problem using response surface designs in heteroscedastic conditions

The contemporary industrial environment continues to rely on the identification of the optimal process target as a means to minimise the product defect rate and ultimately reduce manufacturing costs. Within the context of the optimal process target problem, this paper will offer three distinct contributions. First, a review of literature associated with the process target problem indicates that most research work assumes a known process distribution mean and variance prior to the identification of optimal settings. In contrast, this paper will incorporate the use of response surface designs into solving the process target problem, thus removing the need to make assumptions regarding the process parameters. Second, most research regarding the development of response surface designs either assumes that the same number of observations are made on a quality characteristic of interest, or model error always exhibits a uniform pattern of constant variance. This paper, however, will incorporate alternative modelling techniques to investigate instances when these assumptions are not present, thus broadening the scope of the process target problem. Finally, most research in this area focuses on the determination of the optimal process mean; in this paper, however, we propose a model for simultaneously determining the optimal process mean and variance.     

Designing an optimal water quality monitoring network for river systems using constrained discrete optimization via simulation

The problem of designing a water quality monitoring network for river systems is to find the optimal location of a finite number of monitoring devices that minimizes the expected detection time of a contaminant spill event while guaranteeing good detection reliability. When uncertainties in spill and rain events are considered, both the expected detection time and detection reliability need to be estimated by stochastic simulation. This problem is formulated as a stochastic discrete optimization via simulation (OvS) problem on the expected detection time with a stochastic constraint on detection reliability; and it is solved with an OvS algorithm combined with a recently proposed method called penalty function with memory (PFM). The performance of the algorithm is tested on the Altamaha River and compared with that of a genetic algorithm due to Telci, Nam, Guan and Aral (2009) Telci, I. T., K. Nam, J. Guan, and M.M. Aral, 2009. “Optimal Water Quality Monitoring Network Design for River Systems.” Journal of Environmental Management, 90 (3–4): 2987–2998. doi: 10.1016/j.jenvman.2009.04.011[Crossref], [PubMed], [Web of Science ®] , [Google Scholar].     

On waveform design for optimal target discrimination

Probability theory is applied to the problem of radar target discrimination. The theory is used to construct an excitation waveform (an enhanced discrimination pulse) which maximizes the posterior odds in favor of one target over the other. It is shown that the enhanced discrimination pulse has its spectrum concentrated near the frequency where the difference in the impulse spectrum of the two targets reaches a maximum. Enhanced discrimination pulses are calculated using analytical models of thin cylindrical targets and experimental data collected from simplified aircraft models.©1993 John Wiley & Sons Inc     

Robust design for multiple dynamic quality characteristics using data envelopment analysis

The Taguchi method is extensively adopted in various industries to continuously improve product design in response to customer requirements. The dynamic system of the Taguchi method is frequently implemented to design products with flexible applications. However, Taguchi's dynamic system can be employed only for individual quality characteristic, and the relationship between the quality characteristic and the signal factor is assumed to be linear. Because of these restrictions, Taguchi's dynamic system is ineffective for multiple quality characteristics or when the quality characteristic has a nonlinear relationship with the signal factor. This study describes a novel procedure for optimizing a dynamic system based on data envelopment analysis. The proposed procedure overcomes the limitations of Taguchi's dynamic system. Two cases are analyzed to demonstrate the effectiveness of the proposed procedure. The results show that the proposed procedure can enhance multiple dynamic quality characteristics. Copyright © 2008 John Wiley & Sons, Ltd.     

Sequential optimization of parameter and tolerance design for multiple dynamic quality characteristics

System design, parameter design and tolerance design are the three stages of design process as presented by G. Taguchi. Systems design identifies the basic elements of the design to provide new or improved products to customers. Parameter design determines the optimal parameter settings, which will minimize variation from the target performance of the product. Tolerance design finally identifies the components of the design, which are sensitive in terms of affecting the quality of the product, and establishes tolerance limits that will give the required level of variation in the design. Most studies have focused primarily on optimizing the parameter design or tolerance design for multiple static quality characteristics. In this paper, a mathematical formula corresponding to the model is derived from Taguchi's quadratic quality loss function to minimize the expected total cost for the parameter design of multiple dynamic quality characteristics. When the optimal parameter design is not sufficient to reduce the output variation, the first-order Taylor series expansion is then used to analyse the variations of noise factors for optimizing the tolerance design. It concludes with an example demonstrating this approach.     

An economic model for determining the optimal quality and process control policy in a queue-like production system

This paper interrelates and synthesizes the economic design of quality and process control policy and the management of a production system described in terms of an M/G/X queue. A mathematical model is developed to determine the optimal quality and process control policy when both the quality and quantity issues in the production system are considered. The optimal control policy is defined as the one that maximizes the expected profit per unit time over an infinite horizon. Hypothetical data is used to illustrate the impact of on-line control policies on the operating characteristics of the production process.

For a given control policy, it is shown that the expected profit per unit time of the production system can be increased by either improving the reliability of the production system, increasing the arrival rate of incoming jobs, increasing the system's processing rate, or shortening the system's shutdown time. The results also indicate that the larger the performance difference between the in-control and the out-of-control states, the greater the amount of effort should be involved in quality and process control. Finally, it is important for the capacity design of a production system to take into account the requirrments for both quality control and process adjustment.     

考虑风振响应特性的多目标等效静力风荷载实用分析方法

针对大跨屋盖结构风效应分析理论框架中不同环节相应分析方法众多且未能较好协调统一的问题,以最后环节的等效静力风荷载求解为目标,提出一种考虑风振响应特性的多目标等效静力风荷载实用分析方法.基于脉动风振背景响应、共振响应及其耦合项组合结果,利用LRC法基本原理推导构造多目标等效方程的基本向量,实现与脉动风振响应完全协调对应,...     

离轴溅射法中最佳几何参量的预测

为提高磁控溅射薄膜的厚度均匀性,采用理论计算的方法分析了离轴溅射薄膜的厚度分布,结果表明最佳偏心距及对应的有效薄膜尺寸都与靶基距呈线性增大的关系。溅射原子的角分布、溅射环的宽度以及膜厚均匀性要求都会影响该线性关系。针对以上变化因子,本文归纳出了普适的公式,可方便地对实际工作进行指导。     

Robust product development for multiple quality characteristics using computer experiments and an optimization technique

Conventional parameter or tolerance designs focus on developing exact methods to minimize quality loss or manufacturing cost. The inherent assumption is that the response functions which represent the link between controllable variables and response values of quality characteristics are known before a design is developed. Moreover, parameter and tolerance values are assumed to be independent controllable variables in previous works; namely, they are determined separately in design activities. Currently, advanced computer software, such as computer-aided engineering, can help engineers to handle design problems with unknown response functions, at the stage of product design and process planning. Therefore, in this study, the software ANSYS was employed to obtain simulation data which represent the response values of quality characteristics. These response values will be used to fit a set of response functions for later analysis. However, previous works in computer simulation for design and planning usually lack consideration of the noise impact from an external design system. To approximate a realistic design environment, various levels of controllable variables, in conjunction with artificial noises created from uncontrollable variables, are used to generate simulated data for statistical analysis via Response Surface Methodology (RSM). Then, an optimization technique, such as mathematical programming, is adopted to integrate these response functions into one formulation so that optimal parameter and tolerance values are concurrently determined, with multiple quality characteristics taken into consideration. A bike-frame design was used to demonstrate the presented approach, followed by multiple quality characteristics of interest: material cost, bike-frame weight, structure reliability, and rigidity dependability. The goal is to minimize material cost and bike frame weight and to maximize structure reliability and rigidity dependability. This approach is useful for solving any complex design problems in the early stages, while providing enhanced functionality, quality, economic benefits, and a shorter design cycle.     

Selection of optimal process analyzers for plant-wide monitoring

In this paper, the effect of process analyzer selection and positioning on plant-wide process monitoring is investigated. A fundamental problem in process analytical chemistry is the incomparability of different instrument characteristics. A fast but imprecise instrument is incomparable to a slow but precise instrument. Theory is developed to overcome this problem by using an abstract definition of a process analyzer. This definition allows us to put all instrument characteristics for a particular monitoring task on an equal footing. This results in a measurability factor M that expresses monitoring performance of any process measurement by combining instrument characteristics such as precision, sampling rate, grab size, response correlation, and delay time. Both the choice of location and the performance characteristics of different process analyzers can be evaluated using the measurability factor. The unifying nature of the measurability factor allows for a rational decision between completely different process analyzers and locations (Smilde et al., in this issue). The theory is illustrated and validated with an experiment. A tubular reactor for free-radical bulk polymerization of styrene is monitored by in-line short-wave near-infrared spectroscopy at different positions. Alternatively, product samples are collected for at-line near-infrared analysis. Both analyzers measure styrene monomer concentration. The analysis results are used to predict conversion as well as number and weight average molecular mass of the polystyrene reactor product. The theoretical measurability factors for this case study correspond well with the experimental findings.     

Design of optimal process parameters for non-isothermal forging

This paper presents a state space model and an optimal design scheme for non-isothermal metal forming processes. By selecting nodal velocity and temperature as the state variables, a non-isothermal state equation with coupled deformation and thermal terms is established. Based on this state space model, a control design scheme is developed to obtain the optimal die velocity and initial die temperature which will ensure that the effective strain-rate and temperature satisfy the design requirements. A titanium alloy engine disk forging is used to demonstrate two design examples. The results show that the proposed model and design scheme behave well for different design requirements.     

Designing optimal controllers for doubly fed induction generators using a genetic algorithm

This work presents a design procedure based on evolutionary computation, more specifically on a genetic algorithm combined with the formal pole placement project, to obtain optimal controllers to the rotor-side converter of doubly fed induction generators (DFIGs), in variable-speed wind generation systems connected to the electrical grid. With this procedure it is intended to improve the global system dynamic behaviour during and after the fault period, also increasing the transient stability margin of the power system and the fault ride-through capability. The control action of the DFIG converters is accomplished by proportional and integral controllers, whose gainspsila adjustment is not a trivial task, because of the high complexity of the system. The results obtained confirm the efficiency of the proposed control design procedure.