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There are process uncertainties and material property variations during laminated steel sheet forming, and those fluctuations may result in non-reliable forming quality issues such as fracture and delamination. Additionally, the optimization of sheet forming process is a typical multi-objective optimization problem. The target is to find a multi-objective design optimization and improve the process design reliability for laminated sheet metal forming. The paper aims to discuss these issues.

Desirability function approach is adopted to conduct deterministic multi-objective optimization, and response surface is used as meta-model. Reliability analysis is conducted to evaluate the robustness of the multi-objective design optimization. The proposed method is implemented in a step-bottom square cup drawing process. First, forming process parameters and three noise factors are assumed as probability variables to conduct reliability assessment of the laminated steel sheet forming process using Monte Carlo simulation. Next, only two forming process parameters, blank holding force and frictional coefficient, are considered as probability variables to investigate the influence of the forming parameter deviation on the variance of the response using the first-order second-moment method.

The results indicate that multi-objective design optimization using desirability function method has high efficiency, and an optimized robust design can be obtained after reliability assessment.

The proposed design procedure has potential as a simple and practical approach in the laminated steel sheet forming process.

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.

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.

The purpose of this paper is to present a new method to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V).

The desirability function analysis (DFA), fuzzy set theory with trapezoidal membership function and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method are used to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). In recent years, the utilization of titanium and its alloys, especially of Ti6Al4V materials, in many different engineering fields has undergone a tremendous increase. The ECM process has a potential in the machining of Ti6Al4V. The machining parameters such as electrolyte concentration, current, applied voltage and feed rate with multiple responses such as material removal rate (MRR) and surface roughness (SR) are considered. Experimental work is carried out on Ti6Al4V using second order central composite rotatable design. The two responses are converted into global knit quality index using DFA. Fuzzy set theory with trapezoidal membership function is used to convert all machining parameters and responses into fuzzy values. Then a TOPSIS approach which determines the optimal machining parameters in terms of higher closeness coefficient is proposed to optimize the machining parameters of ECM for titanium alloy. Finally, ANOVA is performed to investigate the significance of each machining parameter and to identify the most influencing factor which affects the process responses.

The optimal machining parameters for ECM process are determined using desirability function analysis, fuzzy set theory and TOPSIS.

A new method is proposed to optimize the electro chemical machining process parameters for titanium alloy.

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.

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 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.

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 discusses multi‐response robust parameter design problems based on response surface method. Most research effort on multi‐response parameter design problem focuses much on finding out optimal parameters based on certain criteria or objectives. Research shows that optimal solution in terms of some criteria may not be robust. To achieve robust solution we should consider how sensitive the solution is when the factors change around it. A comparative study of methods for multi‐response robust parameter design is conducted. Solution with consideration of robustness and optimality is proposed with applications of the example.

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.