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The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

The purpose of this paper is to present computer‐generated combined arrays as efficient alternatives to Taguchi's crossed arrays to solve robust parameter problems.

The alternative combined array designs were developed for the cases including six to twelve variables where CMR designs are not smaller than Taguchi's designs. The efficiency to estimate the effects of interest was calculated and compared to the efficiency of the corresponding CMR designs.

For all the cases investigated at least one computer generated combined array design was found with the same size as the CMR design and with higher efficiency.

Robust parameter design identifies appropriate levels of controllable variables in a process for the manufacturing of a product. The designed experiments involve the controllable variables along with the uncontrollable or noise variables to design a product or process that will be robust to changes in these noise variables. Response surface methodology estimates the actual relationship between the response and the input variables with an empirical model based on the designed experiment. Once the empirical model is fitted, the surface described by the model can be used to describe the behavior of the response over the experimental region. The higher efficiency of the computer generated combined array designs proposed in this research produces lower variances for the parameter estimates and lower variance of prediction for the model. As a result, the response will be described in a more realistic form.

The paper shows that using a computer‐generated design to solve a robust parameter problem will result in a better approximation to the true response of the process. Consequently, optimizing the fitted model will produce settings for the parameters closer to the real optimal settings.

To establish a statistical formulation of robust design optimization and to develop a fast optimization algorithm for the solution of the statistical design problem.

Existing formulations and methods for statistical robust design are reviewed and compared. A consistent problem formulation in terms of statistical parameters of the involved variables is introduced. A novel algorithm for statistical optimization is developed. It is based on the unscented transformation, a fast method for the propagation of random variables through nonlinear functions. The prediction performance of the unscented transformation is demonstrated and compared with other methods by means of an analytical test function. The validity of the proposed approach is shown through the design of the superconducting magnetic energy storage device of the TEAM workshop problem 22.

Provides a consistent formulation of statistical robust design optimization and an efficient and accurate method for the solution of practical problems.

The proposed approach can be applied to all kinds of design problems and allows to account for the inevitable effects of tolerances and parameter variations occuring in practical realizations of designed devices.

Robust parameter design is conventionally analyzed by means of statistical techniques. However, the statistical‐based approach is inefficient when optimizing a dynamic system in regards to quantitative control factors and missing observations. The aim of this paper is to propose an alternative approach based on data mining tools to model and optimize dynamic robust design with missing data.

A three‐phase approach based on data mining techniques is proposed. First, a back‐propagation network is trained to construct the response model of a dynamic system. Second, three formulas of performance measures are developed to evaluate the predicted responses of the response model. Finally, a genetic algorithm is then performed to obtain the optimal parameter combination via the response model.

The proposed approach is capable of dealing with both qualitative and quantitative control factors for dynamic systems as well as static systems. In addition, the proposed approach can efficiently treat parameter experiments with missing data. The proposed approach is demonstrated with a numerical example. Results show that this three‐phase data mining approach outperforms the conventional statistic‐based approaches.

This work provides a relatively effective approach to optimize the three types of dynamic robust parameter design. Performing the approach, practitioners do not require much background in statistics but instead rely on their knowledge of engineering.

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.

The purpose of this paper is to present a new method to obtain robust solutions to electromagnetic optimization problems, solved with evolutional algorithms, which are insensitive to changes in design parameters such as spatial size, positioning and material constant.

Adjoint variable method is employed to evaluate the sensitivity of individuals in evolutional processes.

It is shown in the numerical examples, where the present method is applied to optimization of a superconducting energy storage system and C‐shape magnet, that robust solutions are actually obtained which are insensitive to deviations in spatial sizes.

Unlike usual optimization methods, the present method takes into account deviation in the design parameters due to production errors and long‐term changes. Moreover, the present method is limited to about twice the computational cost of non‐robust optimization methods.

The recent expansion in tertiary education in India, an increased student enrollment as well as global competition have created a challenge for the existence of the institutes. It has been realized that a quality of service is associated with customer satisfaction and it is one of the key points for survival for any organization as it minimizes the various risks associated with an organization. The purpose of this paper is to present the results of an empirical study conducted to obtain the impact of various academic systems on student's satisfaction across the institution. Second, it focusses on minimizing various risks by providing an optimum combination of parameters of different academic activities.

This empirical research investigates customer satisfaction on support services of academic process and focus on minimizing various risks by finding an optimum combination of parameters of academic activities.

It identifies the levels of sensitivity of the various factors affecting the academic process of technical education that might influence the management to design the technical curricula to increase student's satisfaction.

The study demonstrates the impact of statistical process control (SPC) and Taguchi parameter design to monitor the academic process of the institution and finding an optimum condition of the various parameters involved with the process, which would maximize customer satisfaction across the institution. The result suggests that this approach may add more value to both academics and practitioners.

It is an original contribution to integrate SPC and Taguchi robust parameter design in assessing customers’ satisfaction in Indian scenario.

Over the past few years, mathematical and computational models of organizations have attracted a great deal of interest in various fields of scientific research (see Lin & Carley, 1993 for review). The mathematical models have focused on the problem of quantifying the structural (mis)match between organizations and their tasks. The notion of structural congruence has been generalized from the problem of optimizing distributed decision-making in structured decision networks (Pete, Pattipati, Levchuk, & Kleinman, 1998) to the multi-objective optimization problem of designing optimal organizational structures to complete a mission, while minimizing a set of criteria (Levchuk, Pattipati, Curry, & Shakeri, 1996, 1997, 1998). As computational models of decision-making in organizations began to emerge (see Carley & Svoboda, 1996; Carley, 1998; Vincke, 1992), the study of social networks (SSN) continued to focus on examining a network structure and its impact on individual, group, and organizational behavior (Wellman & Berkowitz, 1988). Most models, developed under the SSN, combined formal and informal structures when representing organizations as architectures (e.g., see Levitt et al., 1994; Carley & Svoboda, 1996). In addition, a large number of measures of structure and of the individual positions within the structure have been developed (Roberts, 1979; Scott, 1981; Wasserman & Faust, 1994; Wellman, 1991).

The purpose of this paper is to obtain a better understanding on robust performance of a hardening and tempering process producing component worm shaft used in the steam power plant. This research is capable to explaining the variation of process capability in terms of robustness.

This paper proposed a methodology (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant and process capability acts as a surrogate measure of robustness. In each experimental run, the values of responses and the corresponding multivariate process capability indices across the outer array are determined. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart).

The results provide useful information in term of insensitiveness of the process against the noise (raw material and process noise) variation where the process capability acts as a surrogate measure of process robustness and explains the variation of process capability in term of robustness.

This paper adds to the body of knowledge on robustness of a manufacturing process. This paper may be of particular interest to practicing engineers as it suggests what factors should be more emphasis to achieve robust (consistent) performance from the process.

The originality of this paper lies within the context in which this study is to address key relationships between process robustness and process capability in a manufacturing industry.

This paper aims to illustrate an application of Taguchi method of experimental design (TMED) for the development of a new ignition coil for an automotive vehicle.

The application of TMED for optimisation of manufacturing processes has been widely published in the existing literature. However, the applications of TMED in the design and development of new products are not yet widely reported. This case study presents the results of a designed experiment which utilises a 16‐trial experiment to study 14 design parameters and one interaction. The case study strictly follows a systematic and disciplined methodology outlined in the paper.

The optimal settings of the critical design parameters are determined. The optimal settings have resulted in increased customer satisfaction, improved market share and low defect rate in the hands of customers.

Although the optimal levels are determined from one large experiment, it was unable to determine the true optimal values of each design parameter.

Manufacturers may use TMED to optimise processes (either design or manufacturing) without expensive and time‐consuming experimentation. This case study demonstrates the true power of a well planned and designed experiment over the traditional varying one‐factor‐at‐a‐time approach to experimentation which is rather unreliable, not cost‐effective and may lead to false optimal conditions.

The paper provides an excellent resource for those people who are involved in the design optimisation of a new product.