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1 – 10 of over 92000Vinayambika S. Bhat, Thirunavukkarasu Indiran, Shanmuga Priya Selvanathan and Shreeranga Bhat
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
Research indicates that integral control parameters (Ki) affect outputs substantially more than proportional control parameters (Kp). 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.
Originality/value
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.
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Aitin Saadatmeli, Mohamad Bameni Moghadam, Asghar Seif and Alireza Faraz
The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an…
Abstract
Purpose
The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an economic–statistical design of the X̅ control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter.
Design/methodology/approach
The design of the modified X̅ chart is based on the two new concepts of adjusted average time to signal and average number of false alarms for X̅ control chart under Burr XII shock model with multiple assignable causes.
Findings
The cost model was examined through a numerical example, with the same cost and time parameters, so the optimal of design parameters were obtained under uniform and non-uniform sampling schemes. Furthermore, a sensitivity analysis was conducted in a way that the variability of loss cost and design parameters was evaluated supporting the changes of cost, time and Burr XII distribution parameters.
Research limitations/implications
The economic–statistical model scheme of X̅ chart was developed for the Burr XII distributed with multiple assignable causes. The correlated data are among the assumptions to be examined. Moreover, the optimal schemes for the economic-statistic chart can be expanded for correlated observation and continuous process.
Practical implications
The economic–statistical design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects; one of the proper alternatives may be the Burr XII distribution which is quite flexible. Yet, in Burr distribution context, only one assignable cause model was considered where more realistic approach may be to consider multiple assignable causes.
Originality/value
This study presents an advanced theoretical model for cost model that improved the shock model that presented in the literature. The study obviously indicates important evidence to justify the implementation of cost models in a real-life industry.
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Anirban Dutta and Biswapati Chatterjee
The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for…
Abstract
Purpose
The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.
Design/methodology/approach
Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.
Findings
It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.
Research limitations/implications
This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.
Practical implications
The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.
Originality/value
This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.
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Matloub Hussain, Paul R. Drake and Dong Myung Lee
The purpose of this paper is to quantify the effect of design parameters on the bullwhip effect and dynamic responses produced by a multi‐echelon supply chain with information…
Abstract
Purpose
The purpose of this paper is to quantify the effect of design parameters on the bullwhip effect and dynamic responses produced by a multi‐echelon supply chain with information sharing.
Design/methodology/approach
Taguchi design of experiments and system dynamics simulation are used to quantify the impact of a supply chain's design parameters, including degree of information sharing, on its dynamic performance, and the interactions that occur as the parameter values are varied.
Findings
Quantified relationships between supply chain design parameters and dynamic performance, including the bullwhip effect, are presented. Two parameters in particular, time to adjust inventory error and production lead time, are shown to have a particularly strong impact on the order variance compared to other parameters. However, there are several other significant findings.
Research limitations/implications
Batching and capacity constraints are common causes of the bullwhip effect, but they are not included here. Future work should quantify the impact of these.
Practical implications
This paper presents a systematic way for quantifying and understanding the impact of supply chain design parameters on the bullwhip effect and dynamic responses, and their interactions. The experimental results provide practical understanding for supply chain managers.
Originality/value
Previous studies have identified causes of the bullwhip effect but little attention has been given to quantifying their impact and interactions. This paper makes a contribution towards filling this gap, using system dynamics simulation and Taguchi design of experiments.
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Yannis Kallinderis, Xenakis Vouvakos and Pinelopi Menounou
The purpose of this paper is to simplify the preliminary design process as well as the initial evaluation of existing design parameters of civil jet aircraft; to include noise…
Abstract
Purpose
The purpose of this paper is to simplify the preliminary design process as well as the initial evaluation of existing design parameters of civil jet aircraft; to include noise level consideration right from the start of the design process; and to form a current database of civil jet aircraft design parameters.
Design/methodology/approach
Simple (linear) correlations are found between key design parameters.
Findings
Direct linear relationships are found between design parameters including noise levels. Simplified preliminary design process.
Originality/value
New correlations which simplify the current procedures for preliminary design. In addition, the noise is included right from the beginning of the design. Finally, a new database is formed with specially selected aircraft that is current and covers a wide spectrum of sizes.
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Jiju Antony, Daniel Perry, Chengbo Wang and Maneesh Kumar
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.
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
Although the optimal levels are determined from one large experiment, it was unable to determine the true optimal values of each design parameter.
Practical implications
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.
Originality/value
The paper provides an excellent resource for those people who are involved in the design optimisation of a new product.
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Pan Lee, Edwin H.W. Chan, Queena K. Qian and Patrick T.I. Lam
Design teams have difficulties in assessing building carbon emissions at an early stage, as most building energy simulation tools require a detailed input of building design for…
Abstract
Purpose
Design teams have difficulties in assessing building carbon emissions at an early stage, as most building energy simulation tools require a detailed input of building design for estimation. The purpose of this paper is to develop a user-friendly regression model to estimate carbon emissions of the preliminary design of office buildings in the subtropics by way of example. Five sets of building design parameters, including building configuration, building envelope, design space conditions, building system configuration and occupant behaviour, are considered in this study.
Design/methodology/approach
Both EnergyPlus and Monte Carlo simulation were used to predict carbon emissions for different combinations of the design parameters. A total of 100,000 simulations were conducted to ensure a full range of simulation results. Based on the simulation results, a regression model was developed to estimate carbon emissions of office buildings based on preliminary design information.
Findings
The results show that occupant density, annual mean occupancy rate, equipment load, lighting load and chiller coefficient of performance are the top five influential parameters affecting building carbon emissions under the subtropics. Besides, the design parameters of ten office buildings were input into this user-friendly regression model for validation. The results show that the ranking of its simulated carbon emissions for these ten buildings is consistent with the original carbon emissions ranking.
Practical implications
With the use of this developed regression model, design teams can not only have a simple and quick estimation of carbon emissions based on the building design information at the conceptual stage but also explore design options by understanding the level of reduction in carbon emissions if a certain building design parameter is changed. The study also provides recommendations on building design to reduce carbon emissions of office buildings.
Originality/value
Limited research has been conducted to date to investigate how the change of building design affects carbon emissions in the subtropics where four distinct seasons lead to significant variations of outdoor temperature and relative humidity. Previous research also did not emphasise on the impact of high-rise office building designs (e.g. small building footprint, high window-to-wall ratio) on carbon emissions. This paper adds value by identifying the influential parameters affecting carbon emissions for a high-rise office building design and allows a handy estimate of building carbon emissions under the subtropical conditions. The same approach may be used for other meteorological conditions.
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Burak Öztürk and Fehmi Erzincanli
This study aims to design a femoral component with minimum volume and maximum safety coefficient. Total knee prosthesis is a well-established therapy in arthroplasty applications…
Abstract
Purpose
This study aims to design a femoral component with minimum volume and maximum safety coefficient. Total knee prosthesis is a well-established therapy in arthroplasty applications. And in particular, with respect to damaged or weakened cartilage, new prostheses are being manufactured from bio-materials which are compatible with the human body to replace these damages. A new universal method (design method requiring optimum volume and safety [DMROVAS]) was propounded to find the optimum design parameters of tibial component.
Design/methodology/approach
The design montage was analyzed via the finite element method (FEM). To ensure the stability of the prosthesis, the maximum stress angle and magnitude of the force on the knee were taken into consideration. In the analysis process, results revealed two different maximum stress areas which were supported by case reports in the literature. Variations of maximum stress, safety factor and weight were revealed by FEM analysis, and ANOVA was used to determine the F force percentage for each of the design parameters.
Findings
Optimal design parameter levels were chosen for the individual’s minimum weight. Stress maps were constructed to optimize design choices that enabled further enhancement of the design models. The safety factor variation (SFV) of 5.73 was obtained for the volume of 39,219 mL for a region which had maximum stress. At the same time, for a maximum SFV and at the same time an average weight, values of 37,308 mL and 5.8 for volume and SFV were attained, respectively, using statistical methods.
Originality/value
This proposed optimal design development method is new and one that can be used for many biomechanical products and universal industrial designs.
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Sean M. Puckett and John M. Rose
Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size…
Abstract
Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).
The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.
Mohammad Hosein Nadreri, Mohamad Bameni Moghadam and Asghar Seif
The purpose of this paper is to develop an economic statistical design based on the concepts of adjusted average time to signal (AATS) and ANF for
Abstract
Purpose
The purpose of this paper is to develop an economic statistical design based on the concepts of adjusted average time to signal (AATS) and ANF for
Design/methodology/approach
The design used in this study is based on a multiple assignable causes cost model. The new proposed cost model is compared with the same cost and time parameters and optimal design parameters under uniform and non-uniform sampling schemes.
Findings
Numerical results indicate that the cost model with non-uniform sampling cost has a lower cost than that with uniform sampling. By using sensitivity analysis, the effect of changing fixed and variable parameters of time, cost and Weibull distribution parameters on the optimum values of design parameters and loss cost is examined and discussed.
Practical implications
This research adds to the body of knowledge relating to the quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where multiple assignable causes affect the production process.
Originality/value
The cost functions for uniform and non-uniform sampling schemes are presented based on multiple assignable causes with AATS and ANF concepts for the first time.
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