Optimum Values Of Design Parameters Research Articles

Control Charts for Exponentially Distributed Characteristics: SD, ED, ESD with Taguchi’s Loss Function

ABSTRACT This paper addresses the challenge of quality characteristics that follow an exponential distribution, which can significantly impact decision-making in various fields. Existing approaches rely on approximations to convert exponential distributions to normal distributions, upon which control charts are constructed. However, such conversions introduce errors that can lead to incorrect outcomes, particularly for highly sensitive characteristics. To address this limitation, we propose the development of control charts specifically designed for exponential characteristics, without relying on approximations. Our objective is to introduce four different schemes for constructing these control charts: a statistical scheme, an economic scheme, an economic-statistical scheme combined with Taguchi’s loss function, and an economic-statistical scheme without the application of a loss function. To determine optimal design parameter values for each scheme, we employ the artificial bee colony algorithm. Additionally, we conduct a sensitivity analysis to investigate the impact of design parameters on each proposed design. To illustrate the practical implementation of these control charts, we provide a numerical example that demonstrates their effectiveness. By addressing the limitations of existing approaches and offering novel control chart designs, this paper contributes to enhancing decision-making accuracy and reliability in scenarios involving exponentially distributed quality characteristics.

Parametric Optimization and Efficiency Assessment of Horizontal Earth Water Pipe Heat Exchanger (EWPHE) in the Context of Bangladesh

This study aimed to evaluate the efficiency of the Earth Water Pipe Heat Exchanger (EWPHE) system in Bangladesh through simulation and experimental work carried out at Joypurhat in Bangladesh during the coldest period of the year, January. Additionally, the study sought to establish the method for selecting optimal installation parameters of EWPHE to increase performance and efficiency. A simulation model was made in the TRNSYS (v16.0) platform to determine the optimum values of design parameters. The simulation was run by altering its operating parameters, such as the rate of water flow, length of the pipe, built materials of the pipe, and the diameter of the underground pipeline. According to the findings, pipe burial can be done to a depth of 3.5 meters to achieve optimal output. The results of comparing four distinct materials, namely GI, Steel, PVC, and HDPE, show that these materials' characteristics do not significantly affect how well the systems perform. Additionally, as the water flow rate is increased, it is shown that the EWPHE performance is declining. According to the simulation results, it is concluded that a pipe with a length of 50 meters would be sufficient in the proposed EWPHE system to reach an average temperature decrease from 35 °C to 27 °C. Subsequently, in the field experiment, the EWPHE system had an average efficacy of 55% throughout January, which is quite promising. Journal of Engineering Science 14(2), 2023, 79-88

Optimization of design parameters of the vibration protection system of a motor grader seat with quasi-zero stiffness

BACKGROUND: Vibrations occurring during operation of land transport-technological machines deteriorate the quality of control, reduce the productivity of the work performed, adversely affect the health of machine operators. In order to reduce the impact of vibrations on the operators, vibration protection systems of the cab and seat are used.
 AIMS: For practical application of the developed design of the passive vibration protection system of the motor grader operators seat on the basis of a parallelogram mechanism, tensile spring, cable and rollers, it is necessary to develop an algorithm for optimizing the values of design parameters.
 METHODS: The mean square value of seat acceleration in a stationary reference frame, determined as a result of simulation of the motor grader motion over a set of stochastic microprofiles of the support surface with various characteristics and velocities, was used as an optimization criterion. Comprehensive simulation mathematical model of a motor grader with cabin vibration protection supports and vibration protection system of a seat was used. All parameters of the optimization algorithm and model were divided into fixed, random and varying ones. The latter include a number of dimensions of the parallelogram mechanism of the seat vibration protection system, the coefficient of viscous friction of the mechanisms shock absorber, the height of the quasi-zero stiffness zone. The dependence of the criterion on the design parameters of the seat vibration protection mechanism, which has an implicit pattern and is determined by means of a simulation mathematical model, served as the target function. Boundary conditions were imposed on a number of parameters to ensure the operability of the mechanism, manufacturability of its parts as well as ergonomic considerations. Optimization of the values of the varying parameters was performed with the simplex method. At the same time, a part of the parameters unambiguously influencing the value of the target function was preliminarily maximized or minimized within the boundary ranges.
 RESULTS: An algorithm for assignment and optimization of design parameters of the vibration protection system of a motor grader seat with quasi-zero stiffness is developed, one of the key features of which is the possibility to adjust the vibration protection system of the seat to the weight of the current operator. Examples of algorithm application are given.
 CONCLUSIONS: The algorithm makes it possible to determine the optimal values of design parameters of the seat vibration protection system based on a parallelogram mechanism such as: the dimensions of the parallelogram mechanism, including the attachment points of rollers and cable, the coefficient of tensile spring stiffness, the coefficient of viscous friction of a shock absorber, the parameter of adjusting the mechanism to the weight of the current operator. The key feature of the developed vibration protection system and the optimization algorithm of its design parameters is the possibility of adjustment to the weight of the current operator.

Optimal Design of the Monolithic Flexure Mount for Optical Mirror Using Response Surface Method

An optimal design of a simple beam-shaped flexure hinge mount supporting an optical mirror is presented. An optical mirror assembly is an opto-mechanically coupled system as the optical and mechanical behaviors interact. This side-supporting mount is flexible in the radial direction and rigid for the remaining degrees of freedom to support the mirror without transferring thermal load. Through thermo-elastic, optical and eigenvalue analysis, opto-mechanical performance was predicted to establish the objective functions for optimization. The key design parameters for this flexure are the thickness and length. To find the optimal values of design parameters, response surface analysis was performed using the design of experiment based on nested FCD. Optimal design candidates were derived from the response surface analysis, and the optimal design shape was confirmed through Opto-mechanical performance validation analysis.

Design of dual-band Wilkinson power divider based on novel stubs using PSO algorithm

AbstractIn this paper, a new dual-band Wilkinson power divider (WPD) is designed and fabricated using novel low and high impedance stubs instead of quarter-wavelength transmission lines. The proposed circuit was analyzed using odd and even mode analysis, and the optimal values of design parameters were obtained using the particle swarm optimization algorithm. The designed power divider has input reflection coefficients (S11) of −22.1 and −17 dB at the first operating frequency of 2.2 GHz and the second operating frequency of 14.2 GHz, respectively. It also improves stop-band and fractional bandwidth (FBW) while maintaining a simple topology. The proposed WPD suppresses undesired harmonics from the second to the fifth with an attenuation level of less than −20 dB in the first band and generates a broad stop-band (4.4–11.5 GHz). In the first band, the FBW is 54.5%, and in the second band, it is 20.1%.

Economic–statistical design of Bayesian control chart based on the predictive distribution

PurposeThe purpose of this paper is to present an economic–statistical design (ESD) for the Bayesian control chart based on predictive distribution with two types of informative and noninformative prior distributions.Design/methodology/approachThe design used in this study is based on determining the control chart of the predictive distribution and then its ESD. The new proposed cost model is presented by considering the conjugate and Jeffrey's prior distribution in calculating the expected total cycle time and expected cost per cycle, and finally, the optimal design parameters and related costs are compared with the fixed ratio sampling (FRS) mode.FindingsNumerical results show decreases in costs in this Bayesian approach with both Jeffrey's and conjugate prior distribution compared to the FRS mode. This result shows that the Bayesian approach which is based on predictive density works better than the classical approach. Also, for the Bayesian approach, however, there is no significant difference between the results of using Jeffrey's and conjugate prior distributions. Using sensitivity analysis, the effect of cost parameters and shock model parameters and deviation from the mean on the optimal values of design parameters and related costs have been investigated and discussed.Practical implicationsThis research adds to the body of knowledge related to quality control of process monitoring systems. This paper may be of particular interest to quality system practitioners for whom the effect of the prior distribution of parameters on the quality characteristic distribution is important.Originality/valueeconomic statistical design (ESD) of Bayesian control charts based on predictive distribution is presented for the first time.

Design Optimization of Angular Contact Ball Bearing Using Genetic Algorithm and Grid Search Method

Abstract The present work proposes an approach to design and optimize the internal geometry of angular contact ball bearings (ACBBs) with the help of the genetic algorithm (GA) and the grid search method (GSM). Rolling bearings have very complex internal geometry and have varied types. In literature among other types of bearings, very few authors attempted optimization of ACBBs, and subjected to very few constraints. Additionally, none of them considered the free contact angle as a design parameter, which is very important. So, the main motivation of the present work is to see the effect of inclusion of free contact angle in the optimum design of ACBBs. To optimize the geometry of ACBBs, in present study, we have taken a set of 11 design variables out of which 5 are geometric design variables and the rest are constraint parameters. The solution space has been bounded using a set of 20 realistic constraints. This way performing the same analysis with more realistic constraint model of the bearing, increases the feasibility and reliability of the optimized solutions. The results obtained from the genetic algorithm and the grid search method for the dynamic capacity of the bearing were compared to make a evaluate performance of two approaches. The simulation is performed on a series of selected ACBBs. Using the GA, for 7006 ACBB 83% improvement in its dynamic capacity over its rated values has been achieved, while using the GSM improvement is 43%. However, time taken by the GA over the GSM is one-sixth. A sensitivity analysis is done to check the most affecting parameters for the selected objective function. The present methodology helps the designers to reduce the design time, and select the optimum values of design parameters for manufacturing the custom bearing by prioritizing a specific objective.

Optimisation of design parameters in solar air heater channel with right-angle triangular inserts using the Taguchi approach

ABSTRACT The Taguchi approach is used to uncover the optimal values of design parameters in solar air heater (SAH) channels with right-angle triangular shape inserts. The outcome of the design parameters, such as Reynolds number, relative roughness height and relative roughness pitch on thermo-hydraulic performance of roughened SAH channel, is investigated. Taguchi orthogonal array test arrangement (L18) is selected to optimise the design parameters. The performance characteristics evaluated by the Taguchi method are Nusselt number (Nu), friction factor (f) and thermohydraulic performance parameter (η). The statistical analysis of variance (ANOVA) was executed using the Minitab software to determine the contribution of each effective parameter for attaining the process outcome. For the Nusselt number and friction factor, according to the Taguchi analysis, the Reynolds number emerges as the most effective parameter while relative roughness height is the most effective parameter for thermo-hydraulic performance. Experimental confirmation tests are conducted to validate the suitability of the Taguchi approach. It is observed that the predictions and results noted from experiments show a close match for all the performance characteristics.

Influence of Geometric Parameters for a 100 kW Inward Flow Radial Supercritical CO2 Turbine

Abstract Highly compact and efficient design makes inward flow radial (IFR) turbine a preferred choice for kilowatt scale supercritical CO2 (sCO2) power blocks. The influence of geometric design parameters on sCO2 turbine performance differs from gas turbines because of their small size, high rotational speeds, and lower viscous losses. The paper presents a computational fluid dynamics (CFD) study for a 100 kW IFR turbine to arrive at optimal geometric design parameters—axial length, outlet-to-inlet radius ratio, number of rotor blades, and velocity ratio, and understand their influence on the turbine's performance. The results are compared with well-established gas turbine correlations in the specific speed range of 0.2 to 0.8 to understand the implications on sCO2 IFR turbines. The analysis shows significant variations in the optimal values of design parameters when compared with gas turbines. It is found that sCO2 turbines require fewer blades and higher velocity ratios for optimal performance. The maximum turbine efficiency (∼82%) is achieved at a lower specific speed of ∼0.4 compared to a gas turbine with specific speed varying between 0.55 and 0.65. Additionally, higher negative incidence angles in the range of −50 deg to −55 deg are required at high specific speeds to counter the Coriolis effect in the rotor passage. The paper presents the variation of stator, rotor, and exit kinetic energy losses with specific speeds. The cumulative losses are found to be minimum at the specific speed of ∼0.4.

Thermal optimization of secondary cooling systems in the continuous steel casting process

A new process design methodology for the cooling system in the continuous steel casting process is introduced. It is based on a semi-empirical approach for choosing the primary cooling zone design parameters and using an optimization-based method to decide on the design parameters corresponding to the secondary cooling zone. The heat flux through the secondary cooling zone is the design parameter in the latter case. Unlike former studies that considered a misfit temperature function as the objective function, it is defined here as the square of the temperature field penalized with the Niyama’s criterion infeasibility to ensure the production of microporosity-free strands. This way, no information about the target temperature field is required in advance. The corresponding heat transfer problem is coupled to CALPHAD to consider commercial steel grades straightforwardly. An efficient gradient-based optimization algorithm is employed to find the optimal values of design parameters. Then, this algorithm is employed in an actual process design problem corresponding to the continuous slab casting of a commercial steel grade. A post-processing approach is suggested to convert the results of numerical optimization to industrially relevant machine parameters, and then they are exploited in practice in the foundry plant. While the optimization problem is only constrained to avoid solidification-induced defects, it produces sound and crack-free strands. Moreover, comparing numerical results to actual surface temperature measurements reveals more petite than a six percent discrepancy between the simulation and experiment. These observations illustrate the feasibility of the presented design methodology as the initial step in deciding on the cooling system parameters in this process.

Optimal thermal design of anisotropic plates with arbitrary cutouts using genetic algorithm

Anisotropic plates in different applications may have geometric defects such as openings and cracks. The presence of the opening disturbs the heat flow, which creates significant thermal stress around the opening. When the heat flux is high enough, these extreme stresses can lead to structural failure. This article aims to obtain the optimal parameters for achieving the minimum value of the normalized stress near the cutout's boundary in perforated anisotropic plates utilizing the genetic algorithm. Optimization parameters include the curvature of opening's corners, orientation angle of opening, fibers angle, heat flux angle, and opening's elongation. The plate is under heat flux, and the opening's border is thermally insulated. The stress distribution around the opening is calculated using Lekhnitskii's complex variable method and complex potential functions. The genetic algorithm is then implemented to find the optimal values for design parameters. The results show that by selecting the optimal parameters related to the anisotropic material and the opening's geometry, the stress intensity factor of the perforated anisotropic plates is remarkably reduced. Furthermore, this optimization algorithm can be extended to find the optimized parameters and achieve the optimal designs in anisotropic and isotropic perforated plates under thermal loadings.

Deep Neural Network-Based Surrogate Model for Optimal Component Sizing of Power Converters Using Deep Reinforcement Learning

The optimal design of power converters often requires a huge number of simulations and numeric analyses to determine the optimal parameters. This process is time-consuming and results in a high computational cost. Therefore, this paper proposes a deep reinforcement learning (DRL)-based optimization algorithm to optimize the design parameters for power converters using a deep neural network (DNN)-based surrogate model. The surrogate model of power converters can quickly estimate the power efficiency from input parameters without requiring any simulation. The proposed optimization model includes two major steps. In the first step, the surrogate model is trained offline using a large dataset. In the second step, a soft actor-critic-based optimization model interacts with the surrogate model from step 1 to determine the optimal values of design parameters in power converters. Unlike deep Q learning-based methods, the proposed method is able to handle large state and action spaces. In addition, using entropy-regularized reinforcement learning, our proposed method can accelerate and stabilize the learning process and also prevent trapping in local optima. Finally, to show the effectiveness of the proposed method, the performance of different optimization algorithms is compared, considering over ten power converter topologies.

Integrated Taguchi method‐assisted polynomial Metamodelling & Genetic Algorithm based optimisation of a surface inset permanent synchronous motor for performance improvement

In this study, an Integrated Taguchi method-assisted polynomial Metamodelling & Genetic Algorithm (ITM&GA)-based optimisation technique is implemented for design optimisation of a surface inset permanent magnet synchronous motor (SIPMSM). The motor geometry is analysed by implementing the finite element method for application of the motor in electric compressors of the cooling system of an electric vehicle (EV). The polynomial surrogate model is computed with the help of Taguchi experiments to eliminate the redesigning process of models to reach the optimum values of design parameters and reduce the ambiguity to select the best optimum solution in Traditional Taguchi Method. The root-mean-square error test is performed to validate the accuracy of metamodels. The optimum solutions are then converged using the GA technique. The optimum results are compared and presented. Using the ITM&GA technique, the reduction in unwanted ripples in torque and cogging torque along with the improved torque performance of the motor is achieved successfully. The proposed mechanism is effective in obtaining quick and accurate solutions for preliminary designs of the SIPMSM for the electric compressor application in EVs.

Performance investigation and parametric optimization of an eco‐friendly sustainable design solar air heater

The impacting air jet type collector equipped with fins underneath the absorber surface has been investigated experimentally. Real outdoor conditions were preferred for a specific range of operational parameters (mentioned in Table 1). The experimental investigation covers the analysis of temperature rise parameter (TRP) and instantaneous collector thermal efficiency. The thermal enhancement and other performance characteristics as a result of combining both (impacting air jet mechanism and fins) methods have been studied in detail. It has been observed that TRP decreases and collector efficiency increases with an increase in flow rate. The influence of variable duct aspect ratio has been examined as a function of relevant geometrical parameters of the duct. In the time span of 12.00 hrs–01.00 pm maximum rise in air temperature has been reported at different fin parameters. In conjunction with jet and fin parameters, a parametric optimization model is discussed which yields the optimum values of design parameters to attain maximum efficiency. The experimental and analytical results are validated with similar works. The comparative analysis reports a mean deviation of ±12.7% in TRP and ±7.8% in collector efficiency at w/Dh = 0.23, Dj/Dh = 0.076. The optimum values will be of great help for the researchers and designers exploring this area.

Balancing of planar six-bar mechanism with genetic algorithm

In the present work, the optimal balancing of the planar six-bar mechanism is investigated to minimize the fluctuations of shaking force and shaking moment. An optimization problem is formulated for balancing the planar six-bar mechanism by developing an objective function. The genetic algorithm and MINITAB software were used to solve the optimization problem. The selection of weighting factors has a crucial role to obtain the optimum values of design parameters. Two sets of weighting factors were considered as per the contribution of X and Y components of the shaking force and shaking moments. Shaking force and shaking moments were minimized drastically and were compared with the original values.

Robust design approach to the minimization of functional performance variations of products and systems

Functional performance variations of products and systems are often used to measure the qualities of products and systems considering the changes in the design parameter values caused by uncertainties. A robust design approach has been developed in this research to minimize the functional performance variations considering the design parameter uncertainties by identifying the boundaries of the functional performance variations through optimization. In this work, a mathematical model is developed to describe the relationships among functional performance, design configurations and parameters, and design parameter uncertainties. A multi-level optimization model is established to identify: (1) The optimal design configuration, (2) the optimal values of design parameters, and (3) the boundaries of functional performance variations. Sensitivity analysis considering the impact of parameter uncertainties on functional performance variation boundaries has also been conducted. A case study on the design of a truss system has been conducted. Case study results show that the sensitivities of functional performance variation boundaries to the design parameter uncertainties can be reduced significantly using the new robust design approach.

Design and optimized analysis of high speed precise machine tool spindles

Upcoming machine tools need to be extremely efficient systems to maintain the needed intellectual performance and stability. The spindle tool system is necessary to optimize which enhances the rigidity of the spindle and in turn produces the cutting stability with higher productivity. Prediction of the dynamic behavior at spindle tool tip is therefore an important criterion for assessing the machining stability of a machine tool at design stage. In this work, a realistic dynamic high-speed spindle /milling tool holder/ tool system model is elaborated on the basis of rotor dynamics predictions. The integrated spindle tool system in analyzed with the Timoshenko beam theory by including the effects of shear and rotary deformation effects. Using the frequency response at the tool tip the corresponding stability lobe diagrams are plotted for the vertical end mill system. Furthermore an optimization study is carried out at design stage for the bearing system and the rotor positions for maximizing the chatter vibration free cutting operation at the desired depth of cuts with precise rotational speeds.It is markedly found that the first mode of vibration had a large impact on the dynamic stability of the system. The parametric studies are conducted such as tool overhang and bearing span and the influence of these on the system dynamics are identified and the corresponding stability lobe diagrams are plotted. It is evidently found that the second mode of the frequency response has critically affected the bearing span and competing lobes are identified. These results are assisted to design a spindle bearing system at the desired machining conditions. A neural network based observer is designed based on the simulation resultsto predict optimum design parameter values.

Development of ANN model for discharge prediction and optimal design of sharp-crested triangular plan form weir for maximum discharge using linked ANN–optimization model

Abstract Triangular plan form weirs are advantageous over a normal weir in two ways. Within the limited channel width, use of such a weir increases the crest length and hence for a given head, increases the discharge and for a given discharge, reduces the head in comparison with a normal weir. In a previous study, researchers proposed an empirical equation to compute the discharge coefficient of a triangular plan form weir. The prediction error on the discharge coefficient was ±7% from the line of agreement. In the present study, an ANN model has been utilized to train randomly selected 70% data, with 15% tested and validation made for the remaining 15% data. The model predicts the discharge coefficient with a prediction error in the range of ±2.5% from the line of agreement, thereby decreasing the prediction error in Cd by 64%. Also, the sensitivity analysis of the developed ANN model has been performed for all the parameters (weir height, skew weir length and flow depth) involved in the study and the weir height was found to be the most sensitive parameter. Furthermore, the linked ANN–optimization model has been developed to find the optimal values of design parameters of a triangular plan form weir for maximum discharge.

Variation-Aware SRAM Cell Optimization Using Deep Neural Network-Based Sensitivity Analysis

Under process, voltage, and temperature variations, SRAM cell stability largely fluctuates from the nominal value. In the design step, SRAM cell optimization while ignoring the fluctuation induces the yield loss for the stability. Variation-aware optimization of an SRAM cell can prevent the yield loss problem by considering the mean and variance of SRAM cell stability when finding optimal design parameters. This paper proposes a novel SRAM optimization method that uses a deep neural network (DNN). Multiple DNNs from ensemble techniques represent the mean and variance of SRAM cell stability for the nominal design parameters. Subsequent sensitivity analysis of DNN extracts the ${K}$ design parameters that have the most dominant effects on the mean and variance of SRAM cell stability. Then multidimensional optimization is used to find the optimal values of these ${K}$ parameters to maximize the mean stability while minimizing its variance. The proposed method achieved an average of 2% error compared to MC simulation. The proposed optimization method takes only 561 s to provide the most optimal design parameter values of an SRAM cell.

Multi-objective optimization of mixed convection air cooling in an inclined channel with discrete heat sources

In this paper, a multi-objective optimization of mixed convection air-cooling of two identical discrete heat sources mounted in an inclined channel is conducted to obtain the optimal design parameters, using Response Surface Methodology (RSM) and desirability function approach. Reynolds number (Re), inclination of the channel (r) and the width of the discrete heat source (b) are selected as the input parameters. The computational simulations are done for different Reynolds numbers (25-Re-250), inclination angles of the channel (0o-r-360o) and the width of the heat source (5-b-10 mm). A central composite design (CCD), which comes under the RSM approach, with design parameters was used for analysis of variance (ANOVA). A second-order regression models were developed to correlate the design parameters (Re, r and b) with mean Nusselt number (Nu) and maximum temperature (TMax). The optimum values of design parameters produce the lowest value of maximum temperature (TMax) with a higher mean Nusselt number (Nu) are Reynolds number (Re)=209.94, inclination angle of the channel (r)=330o and heat source width (b)=5.13 mm.