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Diesel engine can produce power more efficiently with lower exhaust emissions when operated at optimum input parameter settings. To achieve this goal, the purpose of this paper is to optimize the input parameters of diesel engine which will lead to optimum performance and exhaust emissions.

To achieve the goal of improving diesel engine performance and exhaust emissions, four input parameters were considered in the study. Five different levels of each input parameter were taken. Four response variables under no load, half load and full load conditions were recorded. Experiments were performed in random manner according to selected Taguchi L25 orthogonal array. The data were analyzed using grey relational analysis coupled with principal component analysis. Analysis of S/N ratio was performed to obtain the optimum combination of input parameters. The grey relational grade at optimum setting of the input parameters was obtained by regression analysis.

Results of the current research work give the optimum input parameter settings for no load, half load and full load conditions of diesel engine. Engine produces power more efficiently with low exhaust emissions when operated at these optimum settings.

In view of the compliance to the stringent air pollution norms of the nations and fast depleting fossil fuels, it is of the utmost importance to design and operate the engine in the optimum range of its input parameters so that it produces more power with low exhaust emissions. This paper aims at optimizing input parameters of diesel engine to improve performance and exhaust emissions. Results of the study presented in this paper are significantly useful for diesel engine-related researchers and professionals.

From the literature review, it appears that only few researchers have conducted studies pertaining to the optimization of the input parameters of diesel engine to improve performance or exhaust emissions. Although few studies related to the optimization of compression ratio, fuel injection timing, fuel injection pressure and air pressure have been reported, no work related to optimization of temperature and pressure of turbocharged air has been reported. Therefore, the main focus of the current research work is on optimizing the charge air temperature and pressure with respect to performance and exhaust emissions.

The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

Scatter in the outcome of repeated experiments is unavoidable due to measurement errors in addition to the non-linear nature of the output responses with unknown influential input parameters. It is a standard practice to select an orthogonal array in the Taguchi approach for tracing optimum input parameters by conducting a few number of experiments and confirm them through additional experimentation (if necessary). The purpose of this paper is to present a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

The modified Taguchi approach is proposed to find the optimum process parameters and the expected range of the output response.

This paper presents a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

Adequacy of this methodology should be examined by considering the test data on different materials and structures.

The introduction of Chauvenet’s criterion and opposing the signal-to-noise ratio transformation on repeated experiments of each test run will provide fruitful results and less computation burden.

The weld joint mechanical properties of friction stir welding (FSW) are majorly reliant on different input parameters of the FSW machine. The study and optmization of these parameters is uttermost requirement and aim of this study to increase the suitability of FSW in different manufacturing industries. Hence, the input parameters are optimized through different soft computing methods to increase the considered objective in this study.

In this research, ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) of FSW prepared butt joints of AA6061 and AA5083 Aluminium alloys materials are investigated as per American Society for Testing and Materials (ASTM E8-M04) standard. The FSW joints were prepared by changing the three input process parameters. To develop experimental run order design matrix, rotatable central composite design strategy was used. Furthermore, genetic algorithm (GA) in combination (Hybrid) with response surface methodology (RSM), artificial neural network (ANN), i.e. RSM-GA, ANN-GA, is exercised to optimize the considered process parameters.

The maximum value of UTS, YS and EL of test specimens on universal testing machine was measured as 264 MPa, 204 MPa and 14.41%, respectively. The most optimized results (UTS = 269.544 MPa, YS = 211.121 MPa and EL = 17.127%) are obtained with ANN-GA for the considered objectives.

The optimization of input parameters to increase the output objective values using hybrid soft computing techniques is unique in this research paper. The outcomes of this study will help the FSW using manufacturing industries to choose the best optimized parameters set for FSW prepared butt joint with improved mechanical properties.

The purpose of this paper is to present a new method for determining the input parameters of the concrete damaged plasticity (CDP) model of ABAQUS standard software. The existing available methods in the literatures are case sensitive, i.e., they give different input parameters of CDP for a unique concrete class used in different finite element (FE) simulation of concrete structures. In this study, the authors attempt to introduce a new approach for the identification of the input parameters of the CDP model, which guarantees the uniqueness and precision of the model. In other words, by this method, the input parameters obtained for a specific concrete class with a unique characteristic strength can be used for FE simulation of the different concrete structures which were constructed by this concrete without the need to additional modifications raised from any new application.

For the input parameter identification of the CDP model, different standard tests of plain concrete are simulated by the ABAQUS standard software. These test simulations are performed for various set of input parameters. In the end, those set of input parameters which represents the best curve fitting with the experimental results is chosen as the optimum parameters.

By comparison of the FE simulation results obtained from the ABAQUS for two different concrete structures using the proposed input parameters for the CDP model with the experimental results, it was shown that the presented method for determining those parameters can guarantee the uniqueness and precision of the CDP model in simulation.

The method described for determining the input parameters of the CDP model of the ABAQUS standard software has not been previously presented.

Drilling of carbon fiber reinforced plastic (CFRP) composite plates with high surface quality are of great importance for assembly operations. The article aims to optimize the drill geometry and cutting parameters to improve the surface quality of CFRP composite material. In this study, CFRP plates were drilled with uncoated carbide drill bits with standard and step geometry. Thus, the effects of standard and step drill bits on surface quality have been examined comparatively. In addition, optimum output parameters were determined by Taguchi, ANOVA and multiple decision-making methods.

Drill bit point angles were selected as 90°, 110° and 130°. In cutting parameters, three different cutting speeds (25, 50 and 75 m/min) and three different feeds (0.1, 0.15 and 0.2 mm/rev) were determined. L18 orthogonal sequence was used with Taguchi experimental design. Three important output parameters affecting the surface quality are determined as thrust force, surface roughness and delamination factor. For each output parameter, the effects of drill geometry and cutting parameters were evaluated. Input parameters affecting output parameters were analyzed using the ANOVA method. Output parameters were estimated by creating regression equations. Weights were determined using the analytic hierarchy process (AHP) method, and multiple output parameters were optimized using technique for order preference by Similarity to An ideal solution (TOPSIS).

It has been determined from the experimental results that step drills generate smaller thrust forces than standard drills. However, it has been determined that it creates greater surface roughness and delamination factor. From the Taguchi analysis, the optimum input parameters for Fz step tool geometry, 90° point angle, 75 m/min cutting speed and 0.1 mm/rev feed. For Fd, are standard tool geometry, 90° point angle, 25 m/min cutting speed and 0.1 mm/rev feed and for Ra, are standard tool geometry, 130° point angle, 25 m/min cutting speed and 0.1 mm/rev feed. ANOVA analysis determined that the most important parameter on Fd is the tip angle, with 56.33%. The most important parameter on Ra and Fz was found to be 40.53% and 77.06% tool geometry, respectively. As a result of the optimization with multiple criteria decision-making methods, the test order that gave the best surface quality was found as 4–1-9–5-8–17-2–13-6–16-18–15-11–10-3–12-14. The results of the test number 4, which gives the best surface quality, namely, the thrust force is 91.86 N, the surface roughness is 0.75 µm and the delamination factor is 1.043. As a result of experiment number 14, which gave the worst surface quality, the thrust force was 149.88 N, the surface roughness was 3.03 µm and the delamination factor was 1.163.

Surface quality is an essential parameter in the drilling of CFRP plates. Cutting tool geometry comes first among the parameters affecting this. Therefore, different cutting tool geometries are preferred. A comparison of these cutting tools is discussed in detail. On the other hand, thrust force, delamination factor and surface roughness, which are the output parameters that determine the surface quality, have been optimized using the TOPSIS and AHP method. In this way, this situation, which seems complicated, is presented in a plain and understandable form.

In the experiments, cutting tools with different geometries are included. Comparatively, its effects on surface quality were examined. The hole damage mechanism affecting the surface quality is discussed in detail. The results were optimized by evaluating Taguchi, ANOVA, TOPSIS and AHP methods together.

The purpose of this paper is to use an ABC algorithm to improve the thrust–torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model.

The design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust–torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust–torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (ABC) algorithm was used. By using ABC algorithm, the maximum thrust–torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust–torque ratio was calculated by changing the input values used in the ±5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.

According to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.

It takes a long time to obtain the optimum thrust–torque ratio value through the ABC algorithm method.

Using ABC algorithm provides to improve the value of thrust–torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the ABC algorithm is more efficient.

The application of the ABC algorithm method can be used effectively to calculate the thrust–torque ratio in UAV.

Providing an original and penetrating a method that saves time and reduces the cost to improve the value of thrust–torque ratio of an unmanned helicopter.

The purpose of this paper is to investigate the influence of turning parameters such as cutting speed, feed rate and depth of cut on tool flank wear and machined surface quality of AISI 304 stainless steel during environment friendly turning under nanofluid minimum quantity lubrication (NMQL) conditions using PVD-coated carbide cutting inserts.

Turning experiments are conducted as per the central composite rotatable design under the response surface methodology. ANOVA and regression analysis are employed to examine significant cutting parameters and develop mathematical models for VB (tool flank wear) and R_a (surface roughness). Multi-response desirability optimization approach is used to investigate optimum turning parameters for simultaneously minimizing VB and R_a.

Optimal input turning parameters are observed as follows: cutting speed: 168.06 m/min., feed rate: 0.06 mm/rev. and depth of cut: 0.25 mm with predicted optimal output response factors: VB: 106.864 µm and R_a: 0.571 µm at the 0.753 desirability level. ANOVA test reveals depth of cut and cutting speed-feed rate interaction as statistically significant factors influencing tool flank wear, whereas cutting speed is a dominating factor affecting surface roughness. Confirmation tests show 5.70 and 3.71 percent error between predicted and experimental examined values of VB and R_a, respectively.

AISI 304 is a highly consumed grade of stainless steel in aerospace components, chemical equipment, nuclear industry, pressure vessels, food processing equipment, paper industry, etc. However, AISI 304 stainless steel is considered as a difficult-to-cut material because of its high strength, rapid work hardening and low heat conductivity. This leads to lesser tool life and poor surface finish. Consequently, the optimization of machining parameters is necessary to minimize tool wear and surface roughness. The results obtained in this research can be used as turning database for the above-mentioned industries for attaining a better machined surface quality and tool performance under environment friendly machining conditions.

Turning of AISI 304 stainless steel under NMQL conditions results in environment friendly machining process by maintaining a dry, healthy, clean and pollution free working area.

Machining of AISI 304 stainless steel under vegetable oil-based NMQL conditions has not been investigated previously.

The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness.

Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters.

Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results.

The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms.

Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process.

This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.