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Scatter in the outcome of repeated experiments is unavoidable due to measurement errors in addition to the non-linear nature of the output responses with unknown influential input parameters. It is a standard practice to select an orthogonal array in the Taguchi approach for tracing optimum input parameters by conducting a few number of experiments and confirm them through additional experimentation (if necessary). The purpose of this paper is to present a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

The modified Taguchi approach is proposed to find the optimum process parameters and the expected range of the output response.

This paper presents a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

Adequacy of this methodology should be examined by considering the test data on different materials and structures.

The introduction of Chauvenet’s criterion and opposing the signal-to-noise ratio transformation on repeated experiments of each test run will provide fruitful results and less computation burden.

The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness.

Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters.

Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results.

The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms.

Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process.

This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

Investors are called to be good stewards/trustees of their investments, often on behalf of third parties. In light of this fiduciary responsibility, and the conundrum of public criticism potentially impacting on share price, this paper aims to use the basis of the UK governance code to explore what important dialogue investors really have with their holdings to support good governance.

Semi-structured telephone interviews with eight institutional investors explore governance issues and investor company dialogue, giving insights into the aspects of the importance of their part in the UK corporate governance code.

Rather than being sleeping lions, investors positively engage with companies, with regular communication being high on their agenda and not always via the annual general meeting. There is a preference to engage directly with the company rather than in public view or via share dumptin. Thus, we often do not see their actions around their fiduciary duties as often they avoid public criticism or any visibility that could do reputational harm and decrease company value.

This dialogue was just before the point of the exposure of the financial crisis; however, it shows the importance that investors give to taking their responsibilities seriously. Importantly, it provides a springboard for further debate following the financial crises and the updates of the financial environment.

Even though policy seeks engagement, the nuances of the investor dialogue are under explored compared to visible quantitative metrics. This dialogue assures that investors are active, even if their engagement is not public and can be deemed as hidden.

Complementing quantitative studies, this paper explores a qualitative approach, uniquely sharing insights into a hidden and little explored world of fiduciary dialogue.

The purpose of this paper is to explore the relationship between product innovativeness and the process of technology switching. This issue is important for two reasons: (1) in mature markets, the only way to increase market share is through consumers' switching from competitor firms and (2) it is essential to determine whether the product innovation strategy can meet users' needs in high-technology markets.

Research centers (university hospitals) specializing in magnetic resonance imaging (MRI) are the lead users of the MRI market. In this market, the technology switching process was tracked using an annual conference database. Interviews with industry experts and lead users were conducted in order to determine the relationship between product innovativeness and technology switching.

The findings reveal that in the lead users' segment, technology switching is occurring at a significant level. The interviews emphasized the influence of product innovativeness on technology switching in the global MRI market, as well as the importance of adopting an open innovation process as a strategy to enhance product innovativeness.

The results can be generalized to industries with similar characteristics, such as high rates of technological change and technology heterogeneity. In high-technology markets, managers should monitor switching behavior. They should also study the influence of product innovativeness on such behavior in order to determine the correct product innovation strategy and meet users' preferences effectively.

The literature reports considerable research that investigates switching behavior, but most publications use data from a short time period and cover a limited geographical region. This is the first study that uses data to track the switching behavior of high-technology products on a global scale over a 22-year period.

In any machining process, the surface profile of the workpiece is continuously changing with respect to time and input parameters. In a conventional machining process, input parameters are feed and depth of cut whilst other parameters are considered to be constant throughout the process.

The direct and indirect participation of this instantaneous curvature can be used to optimize the strategy of cutting operation in terms of different parameters like heat generation-induced stresses, etc. The concepts of the metric tensor and Riemannian curvature tensor are made use in this study as a representation of curvature itself. The objective of this study is to create a mathematical methodology that can be implemented on a highly flexible machining process to find an optimum cutting strategy for a particular output parameter.

The study also includes different case studies for the validation of this newly introduced mathematical methodology.

The study will also find its position in other mechanical processes like forging and casting where instantaneous curvature affects various mechanical properties.

In the current exploration, the machining of a Nimonic 90 superalloy material was carried out in a die-sinking electric discharge machine. Experimentation was performed to investigate the impact of three input machining factors – current (I), pulse on time (T_on) and pulse off time (T_off) – on various response characteristics such as material removal rate (MRR), surface roughness (R_a) and electrode wear rate (EWR).

A Taguchi L9 design and ANOVA were used to assess machine response characteristics. The study also involved a grey relational analysis (GRA) multi-objective technique of optimization.

For single-objective performance, the most appropriate machining factors for achieving the best performance were attained as: MRR (I = 20 A, T_on = 200 µs and T_off = 45 µs), R_a (I = 14 A, T_on = 100 µs and T_off = 25 µs) and EWR (I = 17 A, T_on = 150 µs and T_off = 45 µs). The proposed grey relational approach provided the optimal settings (i.e. 14 A I, 100 µs T_on and 25 µs T_off) for the variables used to calculate the predicted and experimental results. Also, a confirmation test indicated that the final experimental grey relational grade value was enhanced when the experimentation was performed at optimal setting.

To the best of the authors’ knowledge, the present work is the first to examine the proposed machining variables (i.e. current, pulse on time and pulse off time) in relation to the optimization technique of GRA for a Nimonic 90 alloy using a die-sinking electric discharge machining method.

The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate them at their optimal parametric combinations. There are several mathematical tools and techniques that have been effectively deployed for identifying the optimal parametric mixes for the NTM processes. Amongst them, grey relational analysis (GRA) has become quite popular due to its sound mathematical basis, ease to implement and apprehensiveness for multi-objective optimization of NTM processes.

In this paper, GRA is integrated with fuzzy logic to present an efficient technique for multi-objective optimization of three NTM processes (i.e. abrasive water-jet machining, electrochemical machining and ultrasonic machining) while identifying their best parametric settings for enhanced machining performance.

The derived results are validated with respect to technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance is also performed so as to identify the most significant control parameters in the considered NTM processes.

This grey-fuzzy logic approach provides better parametric combinations for all the three NTM processes with respect to the predicted grey-fuzzy relational grades (GFRG). The developed surface plots help the process engineers to investigate the effects of various NTM process parameters on the predicted GFRG values.

The adopted approach can be applied to various machining (both conventional and non-conventional) processes for their parametric optimization for achieving better response values.

The purpose of this study was to composite maize, a cereal grain with low protein and lysine content, with groundnut a rich source of protein, to improve the nutritional quality of maize–groundnut composite flours and their resultant porridges.

Defatted and full fat groundnut flours were used to prepare maize–groundnut composite flours and porridges at the ratio of 100:0, 55:45, 70:30 and 85:15, respectively. They were analyzed for proximate composition, energy value, amino acid and fatty acid profiles.

Compositing maize with groundnut significantly (p ≤ 0.05) increased proteins from 10 per cent in maize flour to up to 21 per cent in composite porridge (denoted DFC1). The energy values for composite porridges were 434 Kcal/100 g when compared with 398 Kcal/100 g established for maize porridge alone. Lysine content was three times higher in composite flours than for maize flour, while for composite porridges, lysine was four times higher than in maize porridge. There was an increase of 35 per cent in oleic acid content when maize flour was composited with groundnut flour.

Further research is required to evaluate the properties of full fat and defatted maize–groundnut porridges and their effects on consumer acceptability.

It can be concluded that compositing maize with full fat and defatted groundnut has the capability of improving the nutritional quality of cereal-based diets consequently contributing to a significant increase in nutritional security of African populations and those of other developing countries of the world.

The purpose of this paper is to investigate prediction and optimization of multiple performance characteristics in the wire electrical discharge machining (wire-EDM) process of SKD 61 (AISI H13) tool steel.

The experimental studies were conducted under varying wire-EDM process parameters, which were arc on time, on time, open voltage, off time and servo voltage. The optimized responses were recast layer thickness (RLT), surface roughness (SR) and surface crack density (SCD). Arc on time was set at two different levels, whereas the other four parameters were set at three different levels. Based on Taguchi method, an L18 mixed-orthogonal array was selected for the experiments. Further, three methods, namely grey relational analysis (GRA), backpropagation neural network (BPNN) and genetic algorithm (GA), were applied separately. GRA was performed to obtain a rough estimation of optimum drilling parameters. The influences of drilling parameters on multiple performance characteristics were determined by using percentage contributions. BPNN architecture was determined to predict the multiple performance characteristics. GA method was then applied to determine the optimum wire-EDM parameters.

The minimum RLT, SR and SCD could be obtained by setting arc on time, on time, open voltage, off time and servo voltage at 2 ms, 3 ms, 90 volt, 10 ms and 38 volt, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the responses.

There were no publications regarding multi-response optimization using a combination of GRA and BPNN-based GA methods during wire-EDM process available.