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Using suspended nanoparticles in the base fluid is known as one of the most efficient ways for heat transfer augmentation and improving the thermal efficiency of various heat exchangers. Different types of nanofluids are available and used in different applications. The main purpose of this study is to investigate the effects of using hybrid nanofluid and number of plates on the performance of plate heat exchanger. In this study, TiO₂/water single nanofluid and TiO₂-Al₂O₃/water hybrid nanofluid with 1% particle weight ratio have been used to prepare hybrid nanofluid to use in plate type heat exchangers with three various number of plates including 8, 12 and 16.

The experiments have been conducted with the aim of examining the impact of plates number and used nanofluids on heat transfer enhancement. The performance tests have been done at 40°C, 45°C, 50°C and 55°C set outlet temperatures and in five various Reynolds numbers between 1,600 and 3,800. Also, numerical simulation has been applied to verify the heat and flow behavior inside the heat exchangers.

The results indicated that using both nanofluids raised the thermal performance of all tested exchangers which have a various number of plates. While the major outcomes of this study showed that TiO₂-Al₂O₃/water hybrid nanofluid has priority when compared to TiO₂/water single type nanofluid. Utilization of TiO₂-Al₂O₃/water nanofluid led to obtaining an average improvement of 7.5%, 9.6% and 12.3% in heat transfer of heat exchangers with 8, 12 and 16 plates, respectively.

In the present work, experimental and numerical analyzes have been conducted to investigate the influence of using TiO₂-Al₂O₃/water hybrid nanofluid in various plate heat exchangers. The attained findings showed successful utilization of TiO₂-Al₂O₃/water nanofluid. Based on the obtained results increasing the number of plates in the heat exchanger caused to obtain more increment by using both types of nanofluids.

Reviews a real‐life investigation carried out on copper coating of CO⊂2 welding wire. The major quality problems were non‐uniformity in coating thickness and other associated problems. The factors identified as responsible are speed of drawing the wire, acid, ferrous and copper sulphate. Describes three stages of the study. The results obtained revealed a number of interesting facts about the process. The extent to which the copper coating is influenced by these factors independently and interactively was vividly brought out by factorial experiments, fractional factorial experiments and standard orthogonal arrays. These results show various intricate dynamics of interest to a process controller.

Reports on a study carried out on an injection moulding process to produce agitators for washing machines, following complaints from customers. The study revealed that there was a lot of variation in the product dimensions produced. Attempts to demonstrate how robust design methodology is helpful in achieving variation reduction of product dimensions and achieving target values. Various analyses carried out using ANOVA and ANOM helped to understand the dynamics of the process. In the presence of multi‐responses and specially when the responses have conflicting behavior to each other, selection of optimum conditions for the process is explained in detail. Reports on the importance of confirmation experiments and the outcome of this detailed exercise.

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.

The aim of this paper is to circumvent the multi‐distribution effects and small sample constraints that may arise in unreplicated‐saturated fractional factorial designs during construction blueprint screening.

A simple additive ranking scheme is devised based on converting the responses of interest to rank variables regardless of the nature of each response and the optimization direction that may be issued for each of them. Collapsing all ranked responses to a single rank response, appropriately referred to as “Super‐Ranking”, allows simultaneous optimization for all factor settings considered.

The Super‐Rank response is treated by Wilcoxon's rank sum test or Mann‐Whitney's test, aiming to establish possible factor‐setting differences by exploring their statistical significance. An optimal value for each response is predicted.

It is stressed, by example, that the model may handle simultaneously any number of quality characteristics. A case study based on a real geotechnical engineering project is used to illustrate how this method may be applied for optimizing simultaneously three quality characteristics that belong to each of the three possible cases, i.e. “nominal‐is‐best”, “larger‐is‐better”, and “smaller‐is‐better” respectively. For this reason, a screening set of experiments is performed on a professional CAD/CAE software package making use of an L₈(2⁷) orthogonal array where all seven factor columns are saturated by group excavation controls.

The statistical nature of this method is discussed in comparison with results produced by the desirability method for the case of exhausted degrees of freedom for the error. The case study itself is a unique paradigm from the area of construction operations management.

The novel mechanical, chemical and thermodynamics characteristics of both single- and multi-wall carbon nanotubes (CNTs) make them a subject of much attention for the scientists and engineers from all domains. Fluid flows subject to CNTs are significant in biomedical engineering, energy storage systems, domestic and industrial cooling, automobile industries and solar energy collectors, etc. Keeping such effectiveness of CNTs in mind, this paper aims to examine peristaltic flow subject to CNTs in an asymmetric tapered channel. Both single and multiple walls CNTs are considered. The viscosity of nanomaterial depends on nanoparticles volume fraction and temperature. Total entropy rate through second law of thermodynamics is calculated. Heat source/sink and nonlinear heat flux are accounted.

The complicated flow expressions are simplified through lubrication approach. The velocity, temperature and entropy expressions are numerically solved by the built-in-shooting method.

The solutions for entropy generation, temperature and velocity are plotted, and the influences of pertinent variables are examined. The authors noticed that entropy generation is an increasing function of the Brinkman number.

The originality of this work is to communicate peristaltic CNTs-based nanomaterial peristaltic flow of viscous fluid in an asymmetric channel. No such consideration is yet published in the literature.

The purpose of this paper is to demonstrate application of the Taguchi method‐based conceptual signal‐to‐noise (S/N) approach and Pareto analysis of variance (ANOVA) to determine optimum level of three important factors related to mobile phone conversation during driving, namely time of drive (day or night), distance between cars, and mobile phone call duration that minimizes drivers' reaction time (RT) in braking response.

Three levels of each factor based on the experience, are considered in the present study. The design of experiment, in the form of an L₁₈ orthogonal array, as proposed by Genichi Taguchi is used to conduct 18 experiments. A total of 27 young male subjects participated in the experimental study. The experimental task involved pressing the brake pedal of the car by the driver upon seeing another car in front while the driver is talking on a mobile phone. Drivers' performance, measured in terms of RT in braking response, is recorded. Conceptual S/N ratio and Pareto ANOVA are employed to investigate the drivers' performance.

Results show that within the test range, drivers' performance, i.e. RT in braking response is optimum (minimum) when they performed the driving task at day time, distance between cars is long (20 meters), and mobile phone call duration is short (30 seconds). Mobile call duration is found to be the dominant parameter with a percentage contribution ratio of 41.882 percent towards the laid down objective followed by time of driving, 32.003 percent and distance between cars, 1.353 percent.

This paper provides valuable information to the drivers with respect to the use of optimum level of these factors in order to minimize their RT in braking response.

To the best of the authors' knowledge, no study has been conducted in the past to investigate the effect of these factors on the performance of drivers when they use mobile phones for conversation during driving. In addition, no attempt has yet been made to find the optimal level of these factors from drivers' performance in braking response view point. This paper is an original research work of authors and in the opinion it carries significantly important values as it provides new information to the persons who talk on mobile phones during driving.

The purpose of this paper is to propose a simple methodology in solving multi‐response optimisation problems by employing Taguchi methods and a non‐parametric statistical technique.

There is a continuous interest in developing effective and statistically sound multi‐response optimisation methods such that they will provide a firm framework in global product and process improvement. A non‐parametric approach is proposed for the first time in a five‐step methodology that exploits Taguchi's fractional factorial designs and the concept of signal‐to‐noise ratio in data consolidation. The distinct feature of this method is the transformation of each response variable to a single rank variable. The subsequent incorporation of the squared ranks for each of the investigated responses issues a single master‐rank response suitably referred to conveniently as a “Super Rank” (SR) response, thus collapsing all dependent product characteristic information into a single non‐dimensional variable. This SR variable is handled by standard non‐parametric methods such as Wilcoxon's two‐sample, rank sum test or Mann‐Whitney's test eliminating at the same time multi‐distribution effects and small‐sample complications expected for this type of experimentation.

The proposed methodology is tested on already published data pertaining a design problem in the electronic assembly technology field. The case study requires six‐factor simultaneous optimisation of three response variables. A second example is analyzed by the proposed method focusing on the optimisation of a submerged arc‐welding process problem due to a group of five factors. The Mann‐Whitney's test contrasts the effects of factor settings one‐to‐one on the SR response in order to assign statistical significance to the optimal factor settings.

The application of this methodology is tested at the same time in a real three‐response optimisation case study where each response belongs to different optimisation category.

The methodology outlined in this work eliminates the need for sophisticated multi‐response data handling. In addition, small‐sample considerations and multi‐distribution effects that may be inherent do not restrict the applicability of the method presented herein by this type of experimentation.

This investigation provides a new angle to the published methods of multi‐response optimisation by supporting Taguchi's design of experiments methods through a multi‐ranking scheme that leads to non‐parametric factor resolution.

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.

The aim of this paper is to examine product formulation screening at the industrial level in terms of multi‐trait improvement by considering several pertinent controlling factors.

The study adopts Taguchi's orthogonal arrays (OAs) for sufficient and economical sampling in a mixture problem. Robustness of testing data is instilled in this method by employing a two‐stage analysis where controlling components are investigated together while the slack variable is tested independently. Multi‐responses collapse to a single master response has been incurred according to the Super Ranking concept. Order statistics are employed to provide statistical significance. The slack variable influence is tested by regression and nonparametric correlation.

Synergy among Taguchi methodology, super ranking and nonparametric testing was seamless to offer practical resolution to product component activeness. The concurrent modulation of two key product traits due to five constituents in the industrial production of muffin‐cake is invoked. The slack variable, rich cream, is strongly active while the influence of added amount of water is barely evident.

The method presented is suitable only for situations where industrial mixtures are investigated. The case study demonstrates prediction capabilities up to quadratic effects for five nominated effects. However, the statistical processor selected here may be adapted to any number of factor settings dictated by the OA sampling plan.

By using a case study from food engineering, the industrial production of a muffin‐cake is examined focusing on a total of five controlling mixture components and two responses. This demonstration emphasizes the dramatic savings in time and effort that are gained by the proposed method due to reduction of experimental effort while gaining on analysis robustness.

This work interconnects Taguchi methodology with powerful nonparametric tests of Kruskal‐Wallis for the difficult problem of non‐linear analysis of mixtures for saturated, unreplicated fractional factorial designs in search of multi‐factor activeness in multi‐response cases employing simple and practical tools.