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The purpose of this paper is to originate a statistical model for delamination factor, surface roughness, machining force and also to determine and compare the effects of machining parameters (spindle speed, fiber orientation angle, helix angle and feed rate) on the output responses during end-milling of glass fiber reinforced polymers (GFRP) by using desirability functional analysis (DFA) and grey relational analysis (GRA).

Based on Taguchi’s L27 orthogonal array, milling experiments were carried on GFRP composite plates employing solid carbide end mills with different helix angles. The machining parameters were optimized by an approach based on DFA and GRA, which were useful tools for optimizing multi-response considerations, namely, machining force, surface roughness and delamination factor. A composite desirability index was obtained for multi-responses using individual desirability values from DFA. Based on this index and grey relational grade the optimum levels of parameters were identified and significant contribution of parameters was ascertained by analysis of variance.

Fiber orientation angle (66.75 percent) was the significant parameter preceded by feed rate (15.05 percent), helix angle (7.76 percent) and spindle speed (0.30 percent) for GFRP composite plates.

Multi-objective optimization in end-milling of GFRP composites using DFA and GRA has not been performed yet.

This paper aims to examine an investigation of high-pressure coolant (HPC) drilling process with regard to experimental models of output parameters, effect of input parameters on output parameters and simultaneous optimization of the output parameters.

Experimental plan was designed using response surface method and experiments were conducted on HPC drilling set up. Measurements for output parameters were carried out and mathematical models were obtained. Multi response optimization using a composite desirability function approach was used to obtain optimum values of input parameters for simultaneous optimization of output parameters.

Optimal value of input parameters for optimization of HPC drilling process were obtained as; coolant pressure: 21 bar, spindle speed: 3,970 rpm, feed rate: 0.084 mm/rev and peck depth: 5.50 mm. The composite desirability obtained is 0.9412, which indicates that the performance of HPC drilling process was significantly optimized. Developed mathematical models of the output parameters accurately represent the entire design space under investigation.

This is the first study that involves variation of higher coolant pressure and investigation of HPC drilling process using response surface methodology and multi response optimization technique with desirability function.

The purpose of this paper is to optimise the process parameters, namely, fibre orientation angle, helix angle, spindle speed, and feed rate in milling of glass fibre-reinforced plastic (GFRP) composites using grey relational analysis (GRA) and desirability function analysis (DFA).

In this work, experiments were carried out as per the Taguchi experimental design and an L27 orthogonal array was used to study the influence of various combinations of process parameters on surface roughness and delamination factor. As a dynamic approach, the multiple response optimisation was carried out using GRA and DFA for simultaneous evaluation. These two methods are best suited for multiple criteria evaluation and are also not much complicated.

The process parameters were found optimum at a fibre orientation angle of 15°, helix angle of 25°, spindle speed of 6,000 rpm, and a feed rate of 0.04 mm/rev. Analysis of variance was employed to classify the significant parameters affecting the responses. The results indicate that the fibre orientation angle is the most significant parameter preceded by helix angle, feed rate, and spindle speed for GFRP composites.

An attempt to optimise surface roughness and delamination factor together by combined approach of GRA and DFA has not been previously done.

This paper aims to investigate the effects of build parameters and strain rate on the mechanical properties of three-dimensional (3D) printed polylactic acid (PLA) by integrating digital image correlation and desirability function analysis. The build parameters included in this paper are the infill density, build orientation and layer height. These findings provide a framework for systematic mechanical characterization of 3D-printed PLA and potential ways of choosing process parameters to maximize performance for a given design.

The Taguchi method was used to shortlist a set of 18 different combinations of build parameters and testing conditions. Accordingly, 18 specimens were 3D printed using those combinations and put through a series of uniaxial tensions tests with digital image correlation. The mechanical properties deduced for all 18 tests were then used in a desirability function analysis where the mechanical properties were optimized to determine the ideal combination of build parameters and strain rate loading conditions.

By comparing the tensile mechanical experimental properties results between Taguchi's recommended parameters and the optimal parameter found from the response table of means, the composite desirability had increased by 2.08%. The tensile mechanical properties of the PLA specimens gradually decrease with an increase in the layer height, while they increase with increasing the infill densities. On the other hand, the mechanical properties have been affected by the build orientation and the strain rate in similar increasing/decreasing trends. Additionally, the obtained optimized results suggest that changing the infill density has a notable impact on the overall result, with a contribution of 48.61%. DIC patterns on the upright samples revealed bimodal strain patterns rendering them more susceptible to failures because of printing imperfections.

These findings provide a framework for systematic mechanical characterization of 3D-printed PLA and potential ways of choosing process parameters to maximize performance for a given design.

The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey relational analysis and desirability function approach (DFA).

The welded sheets were fabricated as per Taguchi orthogonal array design. The effects of tool rotational speed, transverse speed and tool tilt angle process parameters on ultimate tensile strength and hardness were analyzed using grey relational analysis, and DFA and optimum parameters combination was determined.

The tensile strength and hardness values were evaluated from the welded joints. The optimum values of process parameters were estimated through grey relational analysis and DFA methods. Similar kind of optimum levels of process parameters were obtained through two optimization approaches as tool rotational speed of 1150 rpm, transverse speed of 24 mm/min and tool tilt angle of 2° are the best process parameters combination for maximizing both the tensile strength and hardness. Through these studies, it was confirmed that grey relational analysis and DFA methods can be used to find the multi response optimum values of FSW process parameters.

In the present study, the FSW is performed with L9 orthogonal array design with three process parameters such as tool rotational speed, transverse speed and tilt angle and three levels.

Aluminium alloys are widely using in automotive and aerospace industries due to holding a high strength to weight property.

Very limited work had been carried out on multi objective optimization techniques such as grey relational analysis and DFA on friction stir welded joints made with dissimilar aluminium alloys sheets.

Selective inhibition of sintering (SIS) is a new rapid prototyping method that builds parts in a layer‐by‐layer fabrication basis. SIS works by joining powder particles through sintering in the part's body, and by sintering inhibition of some selected powder areas. In this research, statistical tools were applied to improve some important properties of the parts fabricated by the SIS process. An investigation of surface quality and dimensional accuracy was conducted using response surface methodology and through analysis of the experimental results, the impact of the factors on them was modeled. After developing a desirability function model, process operating conditions for maximum desirability are identified. Finally, the desirability model is validated.

This study aims to investigate the effects of process input parameters (welding current, welding time, electrode pressure and holding time) on the output responses (nugget diameter, peak load and indentation) that control the mechanical properties and quality of the joints in dissimilar resistance spot welding (RSW) for the third generation of advanced high-strength steel (AHSS) quenching and partitioning (Q&P980) and (SPFC780Y) high-strength steel spot welds.

Design of experiment approach with two level factors and center points was adopted. Destructive peel and shear tensile strengths were used to measure the responses. The significant factors were determined using analysis of variance implemented by Minitab 18 software. Finally, multiresponse optimization was carried out using the desirability function analysis method.

Holding time was the most significant factor influencing nugget diameter, whereas welding current had the greatest impact on peak load and indentation. Multiresponse optimization revealed that the optimal settings were a welding current of 12.5 KA, welding time of 18 cycles, electrode pressure of 420 Kgf and holding time of 10 cycles. These settings produced a nugget diameter of 8.0 mm, a peak load of 35.15 KN and an indentation of 22.5%, with a composite desirability function of 0.764.

This study provides an effective approach for multiple response optimization to the mechanical behavior of RSW joints, even though there have been few studies on the third generation of AHSS joints and none on the dissimilar joints of the materials used in this study.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

The application of synthetic dyestuffs in the dyeing and printing industries has been criticized because of the introduction of contaminants into the environment. With time, the increasing international awareness of environment and ecology preservation has led to the industry’s attention towards natural dyes and their efficient usage compared to synthetic counterparts. Because the need for “Green” goods and services are rising public awareness, this paper aims to use a banana bio-resource waste to dye cotton fabric.

Factorial design with three variables, including parts of a banana plant, combination of alkalis and application temperature at three different levels, was studied to identify a significant correlation between the effect of these variables on the colour strength and fastnesses of the dyed cotton fabrics.

Dyeing samples achieved with various parts of banana are found to offer significant colour strength and a good wash and rub fastness. Experimental design analysis helped to formulate a standard workable dyeing recipe with the minimum use of resources exhibiting reasonably good wash and rub fastness.

This dyeing technique is novel and can be found useful for partially replacing synthetic dyes with natural colourants possessing good washing and rubbing fastness.

This paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO₂ hybrid composite fabricated by friction stir processing.

An empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.

The relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO₂) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO₂ resulted in a decrease in ductility.

AZ91D/AgNPs/TiO₂ hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.

In terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.