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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.

Intrusions exploit vulnerabilities and introduce external disturbances into information systems to compromise security attributes of information systems such as availability, integrity, and confidentiality. Intrusions into information systems cause faults of software and hardware components in information systems, which then lead to errors and failures of system performance. Intrusion tolerance requires information systems to function correctly in a timely manner even under impact of intrusions. In this paper, we discuss causes, chain effects and barriers of intrusions into information systems, and reveal roles that various information security techniques play in intrusion tolerance. We present two robust intrusion tolerance methods through fault masking: Taguchi’s robust method for system configuration and sharing of resources via an information infrastructure for redundancy.

Manufacturers in Europe, Japan, and the USA have widely employed the Taguchi methods of robust experimental design in optimizing product designs and manufacturing/assembly processes. However, these methods have made relatively little inroads into the service industries, for rather obscure reasons. Develops a robust experimental design to study the variabilities of a service process, namely, a customer complaint correction process, used by a small export company. The goal of the study is to reduce system response time to failures resulting from human or equipment error, equipment malfunction or damage, or unspecified abnormalities in the hardware or software modules of the system. Successfully identifies factors that affected the system response time in a statistically significant manner and yielded the optimum combination of factor levels that produce best results as measured in terms of system response time. Also demonstrates the usefulness and applicability of Taguchi methods in a service environment ‐ thus chipping away at the myth that Taguchi methods work only in a manufacturing environment.

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.

The aim of the paper is the fine‐tuning of proportional integral derivative (PID) controllers under model parameter uncertainties (noise).

The fine‐tuning of PID controllers achieved using the Taguchi method following the steps given: selection of the control factors of the PID with their levels; identification of the noise factors that cause undesirable variation on the quality characteristic of PID; design of the matrix experiment and definition of the data analysis procedure; analysis of the data; decision regarding optimum settings of the control parameters and predictions of the performance at optimum levels of control factors; calculation of the expected cost savings under optimum condition; and confirmation of experimental results.

An example of the proposed method is presented and demonstrates that given certain performance criteria, the Taguchi method can indeed provide sub‐optimal values for fine PID tuning in the presence of model parameter uncertainties (noise). The contribution of each factor to the variation of the mean and the variability of error is also calculated. The expected cost savings for PID under optimum condition are calculated. The confirmation experiments are conducted on a real PID controller.

As a further research it is proposed the contiguous fine‐tuning of PID controllers under a number of a variant controllable models (noise).

The enhancement of PID controllers by Taguchi method is proposed with the form of a hardware mechanism. This mechanism will be incorporated in the PID controller and automatically regulate the PID parameters reducing the noise influence.

Application of Taguchi method in the scientific field of automation control.

The purpose of this paper is to propose a methodology that may aid in assessing information technology (IT) quality characteristic optimisation through the use of simple and robust tools with minimal effort.

Non‐linear saturated fractional factorial designs proposed by Taguchi receive robust data processing by the efficient nonparametric test of Jonckheere and Terpstra.

The paper finds that e‐mail quality improvement is achieved by collecting data through an unreplicated‐saturated L₉(3⁴) design. Active influences are attributed to the e‐mail volume and the receiving hardware type.

The overall efficiency of the method is greatly enhanced due to incorporation of a nonparametric analysis tool that is known to perform effectively when data availability is minimal. The method does not succumb to normality and multi‐distributional effects which may easily handicap the decision‐making process when derived from other mainstream methods.

There are obvious professional and pedagogical aspects in this work aiming at IT quality practitioners offering facilitation towards implementing robust techniques while suppressing quality costs. It is noteworthy that nonparametric data processing improves on the ability to make predictions over Taguchi's regular Design of Experiments (DOE) formulation for small sampling conditions.

This method embraces designing efficiency by non‐linear orthogonal arrays with multi‐level order statistics providing the weaponry to deal with quality optimisation in complex environments such as those in the IT area. The value of this work may be appreciated best by quality managers and engineers engaged in routine quality improvement projects in the area of information systems which also augments the general database of quality‐related testing cases.

This study presents an effective means of applying neural networks to achieve robust design with dynamic characteristic considerations. Two neural networks are constructed to train the data set in the Taguchi’s orthogonal array (OA): one to search for the optimal condition, and the other to forecast the system’s response value. A measuring system employed in semiconductor manufacturing demonstrates the proposed approach’s effectiveness. According to those results, the proposed approach outperforms the conventional Taguchi method. By using the proposed approach, the adjustment factors are not a prerequisite for the dynamic characteristic problem. Moreover, the proposed approach enhances the generalization capability.

In this work, an efficient and easy statistical method to find an equivalent discrete distribution for a continuous random variable (r.v.) is proposed. The proposed method is illustrated by applying it to the treatment of the anthropometrical noise factors in the context of Robust Ergonomic Design (RED; Lanzotti 2006; Barone S. and Lanzotti A., 2007).

Taguchi's technique is best suited to optimize a single performance characteristic yielding an optimal setting of process parameters. A single setting of process parameters may be optimal for one quality characteristics but the same setting may yield detrimental results for other quality features. Thus the purpose of this paper is to describe simultaneous optimization of multi‐characteristics.

The multi‐machining characteristics have been optimized simultaneously using Taguchi's parameter design approach and the utility concept. The paper used a single performance index, utility value, as a combined response indicator of several responses.

A simplified model based on Taguchi's approach and utility concept is used to determine the optimal settings of the process parameters for a multi‐characteristic product. The model is used to predict optimal settings of turning process parameters to yield the optimum quality characteristics of En24 steel turned parts using TiC coated carbide inserts. The optimal values obtained using the multi‐characteristic optimization model have been validated by confirmation experiments. The model can be extended to any number of quality characteristics provided proper utility scales for the characteristics are available from the realistic data.

The proposed methodology can be applied to those industrial situations where a number of responses are to be optimized simultaneously.

The paper discusses a case study on En24 steel turned parts using titanium carbide coated tungsten carbide inserts. The material, En24 steel, has wide applications in aerospace, machine tools, automobiles, etc. The proposed algorithm is easy to apply.

Researchers heavily investigated the use of industrial robots to enhance the quality of spray-painted surfaces. Despite its advantages, automating process is not always economically feasible. The manual process, on the other hand, is cheaper, but its quality is prone to the mental and physical conditions of the worker making it difficult to operate consistently. This research proposes a mathematical cost model that integrates human factors in determining optimal process settings.

Taguchi's robust design is used to investigate the effect of fatigue, stability of worker's hand and speed on paint consumption, surface quality, and processing time. A crossed array experimental design is deployed. Regression analysis is then used to model response variables and formulate cost model, followed by a multi-response optimization.

Results reveal that noise factors have a significant influence on painting quality, time, and cost of the painted surface. As a result, a noise management strategy should be implemented to reduce their impact and obtain better quality and productivity results. The cost model can be used to determine optimal setting for different applications by product and by industry.

Hardly any research considered the influence of human factors. Most focused on robot trajectory and its effect on paint uniformity. In proposed research, both cost and quality are integrated into a single objective. Quality is measured in terms of uniformity, smoothness, and surface defects. The interaction between trajectory and flow rate is investigated here for the first time. A unique approach integrating quality management, statistical analysis, and optimization is used.