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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.

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.

The advancement of additive manufacturing technologies has resulted in producing parts of high quality and reduced manufacturing time. This paper aims to achieve a simultaneous optimal solution for build time and surface roughness as the output data and also to find the best values for the input data consisting of build orientation, extrusion width, layer thickness, infill percentage and raster angle.

For this purpose, the effects of process parameters on the response variables were investigated by the design of experiments approach to develop empirical models using response surface methodology. The experimental parts of this research were conducted using an inexpensive and locally assembled fused filament fabrication (FFF) machine. A total of 50 runs for 4 different geometries, namely, cylinder, prism, 3DBenchy and twist gear vase, were performed using the rotatable central composite design, and each process parameters were investigated in two levels to develop empirical models. Also, a novel optimization method, namely, the posterior-based method, was accomplished to find the best values for the response variables.

The results demonstrated that not only the build orientation and layer thickness have notable effects on both response variables but also build time is dependent on extrusion width and infill percentage. Low infill percentage and high extrusion width resulted in increasing build time. By reducing layer thickness and infill percentage while increasing extrusion width, parts of high-quality surface finish and reduced built time were produced. Optimum process parameters were found to be of build direction of 0°, extrusion width of 0.61 mm, layer thickness of 0.22 mm, infill percentage of 20% and raster angle of 0°.

Through the developed empirical models and by minimizing build orientation and layer thickness, and also considerations for process parameters, parts of high-quality surface finish and reduced built time could be produced on FFF machines. To compensate for increased build time because of reduction in layer thickness, extrusion width and infill percentage must have their maximum and minimum value, respectively.

This paper aims to investigate the dimensional accuracy and surface roughness of printed masked stereolithography (m-SLA) parts. The fabricated specimens of photosensitive polymer resin have complex shapes and various features. The influence of four process parameters of m-SLA, including layer height, exposure time, light-off delay and print orientation, is studied on response characteristics.

The Box–Behnken design of response surface methodology is used to examine the effect of process parameters on the shrinkage of various geometrical dimensions like diameter, length, width, and height of different features in a complex shape. Additionally, a multi-response optimization has been carried out using the desirability function to minimize the surface roughness and printing time and maximize the dimensional accuracy.

The layer height and print orientation influence the surface roughness of parts. An increase in layer height results in increased surface roughness, and the orientation parallel to the z-axis of the machine gives the highest surface roughness. The dimensional accuracy of m-SLA parts is influenced by layer height, exposure time, and print orientation. Although not significant in dimensional accuracy and surface roughness, the light-off delay can affect printing time apart from other parameters like layer height and print orientation.

The effect of layer height and print orientation on dimensional accuracy, printing time, and surface roughness is investigated by researchers using simple shapes in other vat photopolymerization techniques. The present work is focused on studying the effect of these parameters and additional parameters like light-off delay in complicated geometrical parts in m-SLA.

In this article, a practical design methodology is proposed for discrete sizing optimization of high-rise concrete buildings with a focus on large-scale and real-life structures.

This framework relies on a computationally efficient approximation of the constraint and objective functions using a radial basis function model with a linear tail, also called the combined response surface methodology (RSM) in this article. Considering both the code-stipulated constraints and other construction requirements, three sub-optimization problems were constructed based on the relaxation model of the original problem, and then the structural weight could be automatically minimized under multiple constraints and loading scenarios. After modulization, the obtained results could meet the discretization requirements. By integrating the commercially available ETABS, a dedicated optimization software program with an independent interface was developed and details for practical software development were also presented in this paper.

The proposed framework was used to optimize different high-rise concrete buildings, and case studies showed that material usage could be saved by up to 12.8% compared to the conventional design, and the over-limit constraints could be adjusted, which proved the feasibility and effectiveness.

This methodology can therefore be applied by engineers to explore the optimal distribution of dimensions for high-rise buildings and to reduce material usage for a more sustainable design.

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.

Plasma arc cutting (PAC) is extensively applicable for cutting the materials in faster speed with better accuracy in different manufacturing industries. The cutting of sailhard steel plate plays a great challenge in plasma arc cutting process.

In this investigation, a special abrasion-resistant steel known as sailhard of 20 mm thickness plate has been cut by PAC machine. Cutting current, stand-off distance, cutting speed and gas pressure were selected as cutting parameters. The corresponding responses focused for this study are material removal rate, kerf and chamfer. L30 orthogonal array based on a central composite design (CCD) of response surface methodology (RSM) was used to design the run of the experiment. For predicting and modeling of optimal cutting conditions, a hybrid approach of desirability function-based response surface methodology (DRSM) was acquainted.

The result of this study determines that desirability index (DI) was affected significantly with the machining parameter as well as their interaction. A confirmation test was carried out to analyze the degree of effectiveness of DRSM technique.

In PAC, the selection of process parameters and effect of that parameter on the output responses is of greater value because of the selection of best cutting condition.

This paper aims to analyze the significance of machining parameters (workpiece’s rotational speed, magnet coil current and working gap) on final Ra (surface roughness) and material removal rate (MRR) of workpiece in reciprocating magnetorheological polishing (RMRP) process.

The research is planned to analyze, model and predict the optimum machining parameters to anticipate final Ra and MRR by applying response surface methodology (RSM) and multiresponse optimization (desirability function approach). The experiments have been planned by design of experiments (DOE). Analysis of variance (ANOVA) is applied to determine the significances of machining parameters on RMRP performance characteristics.

Response surface plots for final Ra and MRR by RSM show that machining parameters are significant for the responses. The optimum machining parameters obtained are optimized by desirability function approach (DFA), and the optimum parametric combination has been validated by confirmatory experiments. The experimental results of the final Ra and MRR are deviated by 5.12% and 2.31% from the response results under the same optimization conditions, respectively.

In this study, the RMRP responses (final Ra and MRR) are improved at predicted input machining parameters condition obtained by RSM and DFA approach. Furthermore, the research results provide a reference for experimental design and optimization of MRP process.

When building parts in a stereolithography apparatus (SLA), the user is faced with many decisions regarding how the part will be built. The quality of the build can be controlled by the user by changing one of several build style variables, including part orientation, cross sectional layer thickness, and laser hatch density. A user will probably have preferences for the part build (i.e. accuracy or speed), but may not understand how to vary the build style variables to produce the desired results. A method based on response surface methodology and multiobjective decision support is described in this paper for relating build goals to the build style variables to provide support for making build style decisions. The method is applied to the build style of a circuit breaker handle. Results indicate the method’s usefulness in supporting build style decisions. Expected behaviors of the goals with respect to the variables were confirmed and quantified. Additionally, response surface methodology was shown to be accurate and effective in modeling the relationships among variables and goals.

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.