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This study aims to model the important flow response quantities over a shrinking wedge with the help of response surface methodology (RSM) and an artificial neural network (ANN). An ANN simulation for optimal thermal transport of incompressible viscous fluid under the impact of the magnetic effect (MHD) over a shrinking wedge with sensitivity analysis and optimization with RSM has yet not been investigated. This effort is devoted to filling the gap in existing literature.

A statistical experimental design is a setup with RSM using a central composite design (CCD). This setup involves the combination of values of input parameters such as porosity, shrinking and magnetic effect. The responses of skin friction coefficient and Nusselt number are required against each parameter combination of the experimental design, which is computed by solving the simplified form of the governing equations using bvp4c (a built-in technique in MATLAB). An empirical model for Cf_x and Nu_x using RSM and ANN adopting the Levenberg–Marquardt algorithm based on trained neural networks (LMA-TNN) is attained. The empirical model for skin friction coefficient and Nusselt number using RSM has 99.96% and 99.99% coefficients of determination, respectively.

The values of these matrices show the goodness of fit for these quantities. The authors compared the results obtained from bvp4c, RSM and ANN and found them all to be in good agreement. A sensitivity analysis is performed, which shows that Cf_x as well as Nu_x are most affected by porosity. However, they are least affected by magnetic parameters.

This study aims to simulate ANN and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge.

The purpose of this paper is to find the optimal values of process parameters in injection molding when both warpage and shrinkage are minimized.

In finding the optimal values, advantages of finite element software, Moldflow, and dual response surface method (dual RSM) combined with the nonlinear programming technique by Lingo are exploited. Considering the nine process parameters, injection time, injection pressure, packing pressure, packing time, cooling time, coolant temperature, mold‐open time, melting temperature and mold surface temperature, a series of mold analyses are performed to exploit the warpage and shrinkage data. In the analyses, warpage is considered the primary response, whereas shrinkage is the secondary response.

The results indicate that dual RSM combined with the nonlinear programming technique can outperform the Taguchi's optimization method. The optimal process values are also confirmed by re‐running experiments on Moldflow. Additionally, an auxiliary dual RSM model is proposed to search for a better result based on the given findings by dual RSM at the cost of running extra experiments. Based on dual RSM, a multiple objective optimization for the whole plastic product is finally suggested to integrate the dual RSM models that are developed for the individual nodes or edges.

This paper proposes a new method to find the optimal process for plastic injection molding.

The purpose of this paper is to develop a mathematical model for surface roughness and delamination through response surface methodology (RSM) and analyse the influences of the entire individual input machining parameters (cutting speed, fibre orientation angle, depth of cut and feed rate) on the responses in milling of glass fibre reinforced plastics (GFRP) composites with solid carbide end mill cutter coated with PCD.

Four factors, five level central composites and a rotatable design matrix in response surface methodology were employed to carry out the experimental investigation. “Design Expert 8.0” software was used for regression and graphical analysis of the data were collected. The optimum values of the selected variables were obtained by solving the regression equation and by analyzing the response surface contour plots. Analysis of variance (ANOVA) was applied to check the validity of the model and for finding the significant parameters.

The developed second order response surface model was used to calculate the surface roughness and delamination of the machined surfaces at different cutting conditions with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained.

The effect of fibre orientation during milling of GFRP laminates using RSM has not been previously attempted for analysis.

This paper aims to study the comparison between a response surface methodology (RSM) and artificial neural network (ANN) in the modelling and prediction of surface roughness during endmilling of glass-fibre-reinforced polymer composites.

Aiming to achieve this goal, several milling experiments were performed with polycrystalline diamond inserts at different machining parameters, namely, feed rate, cutting speed, depth of cut and fibre orientation angle. Mathematical model is created using central composite face-centred second-order in RSM and the adequacy of the model was verified using analysis of variance. ANN model is created using the back propagation algorithm.

With regard to the machining test, it was observed that feed rate is the dominant parameter that affects the surface roughness, followed by the fibre orientation. The comparison results show that models provide accurate prediction of surface roughness in which ANN performs better than RSM.

The data predicted from ANN are very nearer to experimental results compared to RSM; therefore, this ANN model can be used to determine the surface roughness for various fibre-reinforced polymer composites and also for various machining parameters.

The purpose of this paper is to present a model to predict the micro-hardness of friction stir processed (FSPed) AZ31B magnesium alloy using response surface methodology (RSM). Another objective is to identify process parameters and through-thickness position which will give higher micro-hardness values. Moreover, the study aims at defining the factor that exhibits the most effect on the micro-hardness. Friction stir processing (FSP) machine can then be fed with the optimized parameters to achieve desirable properties.

An experimental setup was designed to conduct FSP. Several AZ31B magnesium samples were FSPed at different combinations of rotational and translational speeds. The micro-hardness of all the combinations of process parameters was measured at different through-thickness positions. This was followed by an investigation of the three factors on the resulting micro-hardness. RSM was then used to develop a model with three factors and three levels to predict the micro-hardness of FSPed AZ31 magnesium alloy within the covered range. The analyses of variance in addition to experimental verification were both used to validate the model. This was followed by an optimization of the response.

The model showed excellent capability of predicting the micro-hardness values as well as the optimum values of the three factors that would result in better micro-hardness. The model was able to capture the effects of rotational speed, translational speed, and through-thickness position. Results suggest that micro-hardness values were mostly sensitive to changes in tool rotational speed.

FSP is considered to be one of the advanced microstructural modification techniques which is capable of enhancing the mechanical properties of light-weight alloys. However, the lack of accurate models which are capable of predicting the resulted properties from process parameters hinders the widespread utilization of this technique. At the same time, RSM is considered as a vital branch of experimental design due to its ability to develop new processes and optimize their performance. Hence, the developed model is very beneficial and is meant to save time and experimental effort toward effective use of FSP to get the desired/optimum micro-hardness distribution.

Congruence serves as a key framework in many leader–follower dyad theories. This paper aims to introduce polynomial regression analysis with response surface methodology (PRA with RSM) as a statistical technique for investigating research questions concerning leader–follower dyadic relationships in the hospitality context.

First, this paper illustrates the necessity of applying PRA with RSM to more effectively address the research issues related to leader–follower dyadic relationships. Next, this paper presents an overview and the key concepts of PRA with RSM. Critical issues that need to be noted and two recent hospitality leadership studies that have used PRA with RSM are discussed. Third, an empirical example in the hotel context is provided to illustrate the application of PRA with RSM.

By applying this methodology to the study of hospitality leader–follower dyadic relationships, researchers will be able to address a range of topics related to dyadic theory, such as leader–member exchange and value congruence.

PRA with RSM reveals that congruence effects vary within leader–follower dyads. Industry professionals can promote a better leader–follower fit by incorporating dyadic surveys to understand mutual agreement and perceptions regarding same-workplace phenomena.

The paper addresses the misalignment between leader–follower dyadic theory and the methodology used in hospitality leadership studies.

The paper aims to develop a mathematical model for delamination and surface roughness during end milling by using response surface methodology (RSM) and to determine how the input parameters (cutting speed, depth of cut, helix angle and feed rate) influence the output response (delamination and surface roughness) in machining of hybrid glass fibre reinforced plastic (GFRP; Abaca and Glass) composite using solid carbide end mill cutter.

Four-factor, three-level Taguchi orthogonal array design in RSM is used to carry out the experimental investigation. The “Design Expert 8.0” is used to analyse the data collected graphically. Analysis of variance is carried out to validate the model and for determining the most significant parameter.

The feed rate is the cutting parameter which has greater influence on delamination (88.39 per cent), and cutting speed is the cutting parameter which has greater influence on surface roughness (53.42 per cent) for hybrid GFRP composite materials. Both surface roughness and delamination increase as feed rate increases, which means that the composite damage is larger for higher feed rates.

Effect of milling of hybrid GFRP composite on delamination and surface roughness with various helix angles of solid carbide end mill has not been analysed yet using RSM.

There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.

Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.

The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.

This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.

This paper aims to analyze deeply the effect of surface roughness conditions of the common interface of the two-layered riveted cantilever beams on their frictional damping during free lateral vibration at first mode. Here, the product, (µ × α), and damping ratio, ξ, are the parameters whose variations are analyzed in this investigation. For this, the influencing parameters considered are the natural frequency of vibration, f; the amplitude of initial excitation, y; and surface roughness value, Ra.

For experimentally evaluating logarithmic damping decrement, d, the frequency response function analyzer for the case of free lateral vibrations was used. Later, for evaluating the product, µ × α (where µ is the kinematic coefficient of friction and α is the dynamic slip ratio), and then, the damping ratio, ξ, the empirical relation suggested for logarithmic damping decrement, d, of riveted cantilever beams was used. After this, the full and reduced quadratic models of the product, µ × α, ξ, response surface methodology (RSM) with the help of Design Expert 11 software was used. Corresponding main effects plots, surface plots and prediction comparison plots were obtained to observe the variations of the product, µ × α, ξ for the variations of influencing parameters: f, y and Ra. Finally, a machine learning technique such as artificial neural networks (ANNs) using “nntool” present in MATLAB R13a software was used to predict the ξ for the different combinations of f, y and Ra.

The full and reduced quadratic regression models for the product, (µ × α) and the damping ratio, ξ of riveted cantilever beams for free lateral vibrations of the first mode in terms of the parameters: f, y and Ra were obtained. In addition, the main effects plots, surface plots and prediction comparison plots for the product, µ × α, ξ, with the corresponding experimental values of the product, µ × α, ξ, were obtained. Also, the execution of ANNs using MATLAB R13a software is proved to be the more accurate tool for the prediction of damping ratios in comparison to quadratic regression equations obtained from Design Expert 11 software. In the end, the assumption that the effect of surface roughness value on the product, (µ × α), and the damping ratio, ξ, is negligible is proved to be true using the main effects plots for the product, (µ × α) and ξ obtained from the Design Expert 11 software.

Obtaining the full and reduced quadratic regression equations for the product, (µ × α), and ξ of the two-layered riveted cantilever beams in terms of parameters: f, y and Ra was done. In addition, the conditions for the corresponding minimum and maximum values of the product, (µ × α), and ξ were obtained. Later, the main effects plots, surface plots and comparison plots of the predicted product, (µ × α), and ξ versus experimental product, (µ × α), and ξ were also obtained. Finally, the predicted values of the product, (µ × α), and ξ using the ANNs tool are observed to be the more accurate values in comparison to that obtained from RSM using the Design Expert 11 software.

This paper aims to develop a mathematical model for analysing surface roughness during end milling by using response surface methodology (RSM) and to determine how the input parameters (cutting speed, depth of cut and feed rate) influence the output parameter (surface roughness) in the machining of hybrid glass fibre reinforced plastic (GFRP; Abaca and Glass) composite by using solid carbide end mill cutter.

Three factors and a three-level Box–Behnken design in RSM were used to carry out the experimental investigation. Handysurf E-35A was used to measure the surface roughness of the machined hybrid GFRP composites. The “Design Expert 8.0” was used to analyse the data collected graphically. Analysis of variance was carried out to validate the model and determine the most significant parameter.

The response surface model was used to predict the input factors influencing the surface roughness of the machined surfaces of hybrid GFRP composite at different cutting conditions with a chosen range of 95 per cent confidence intervals. Analysis of the influences of the entire individual input machining parameters on the surface roughness carried out using RSM.

The effect of the milling of hybrid GFRP composite on the surface roughness with solid carbide end mill by using RSM has not been analysed yet.