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This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of particle swarm optimization (PSO) with Derringer’s desirability.

The performance parameters such as brake thermal efficiency (BTHE), brake specific fuel consumption (BSFC), CO, HC, NO_x and smoke are considered as objectives with compression ratio, blend percentage and load as input factors. Optimization is carried out by using PSO coupled with the desirability approach.

From results, the optimum operating conditions are found to be at compression ratio of 18.5 per cent of fuel blend and 11 kg of load. At this input’s parameters of the engine, outputs performance parameters are found to be 34.84 per cent of BTHE, 0.29 kg/kWh of BSFC, 2.86 per cent of CO, 13 ppm of HC, 490 ppm of NO_x and 26.25 per cent of smoke.

The present study explores the abilities of both particle swarm algorithm and desirability approach when used together. The combined approach resulted in faster convergence and better prediction capability. The present approach predicted performance characteristics of the variable compression ratio engine with less than 10 per cent error.

The purpose of this article is to consider a corrective and preventive maintenance model with a view to both minimizing cost and maximizing system availability.

The proposed experimental multiobjective approach combines a simulation model and a statistical method to determine the best system parameters. The desirability function is used to convert a multiresponse problem into a maximization problem with a single aggregate measure. The model examined is based on a m identical machines system subject to unpredictable breakdown and repair, and the maintenance strategy used is based on the existing block‐replacement policy, which consists in replacing components upon failure or preventively, at scheduled intervals (T). Spare part inventory management is based on the (S, Q) model, whereby an order is placed when the replacement stock level drops below a given safety threshold level (S). At that time, a replacement part quantity (Q) is ordered, and is received after a stochastic lead time (τ).

The proposed model jointly minimizes the overall maintenance cost and maximizes system availability using a multiobjective optimization desirability function.

The multiobjective model can be used in a real manufacturing environment to help business decision makers determine the best compromise system parameters and adjust them to obtain desired response variables (overall production cost and system availability).

The proposed model allows the simultaneous optimization of two response variables, and determines the best system parameter compromise between the system cost minimization and the system availability maximization.

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.

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.

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.

The purpose of this study is (1) to confirm the relationship between the two dimensions of social desirability (pretending and denying), self-reported stress and health reports in police officers and (2) to assess whether dysfunctions in basal cortisol profiles are related to social desirability.

Social desirability is known to influence how individuals respond to sensitive topics, such as questions concerning health in the workplace, and has usually been defined according to two dimensions: pretending and denying. However, it is not known whether social desirability is only a bias in responding to health surveys or a more general attitude of denying problems and pretending to be stronger than one is in the everyday life. If the latter is true, social desirability may have important health implications, and underlying mechanisms must be described. In total, 77 police officers completed questionnaires measuring social desirability (denying and pretending), perceived stress as well as mental and somatic health symptoms. They were further instructed to collect saliva samples for cortisol concentrations assays.

These preliminary results showed that denying was negatively related to the report of stress and health symptoms. Furthermore, police officers higher in pretending showed a flatter diurnal cortisol slope.

The correlation between dysregulation of the hypothalmic-pituitary-adrenal (HPA) axis, as expressed by a flatter cortisol slope, and a higher score in the pretending subscale suggests that looking for social approval by inflating one's capacities is related to chronic work-related stress, making the individual more vulnerable to stress-related disease.

To study the potential health-relevant consequences and underlying mechanisms of social desirability bias related to police culture by including stress biomarkers.

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 study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO₂ emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.

Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO₂ emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.

The high R² values of 97.38% for CO₂ and 97.28% for service level, along with adjusted R² values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO₂ encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.

This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

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.