A robust approach to human-computer interface design using the Taguchi method

This paper presents a novel design scheme for traveling wave thermo-acoustic Stirling engines (TASEs) based on a target frequency using genetic algorithm (GA). First, the effects of engine design parameters on the system performance are studied via root locus method. Accordingly, it is found that the resonator length, the inertance diameter, and the hot gas temperature are the most effective parameters in the engine design procedure. Next, the relation between the closed-loop poles positions of the thermo-acoustic system and the effective design variables are described parametrically. Consequently, in order to estimate the optimum values of the effective design parameters as well as the unknown positions of the nondominant poles, an appropriate fitness function based on the desired engine frequency is proposed. Subsequently, a GA is used to achieve the optimal values of design parameters so as to minimize the considered cost function. Finally, the validity of the proposed design technique is verified by comparing the obtained results with the available data of an experimental case study.

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.

Applying stack simulation for branch target buffers

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

An optimal concurrent product design and service planning approach through simulation-based evaluation considering the whole product life-cycle span

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.

Modeling and thermo-economic optimization of heat recovery heat exchangers using a multimodal genetic algorithm

The method and results of 4E (energy, exergy, economic and environmental) analysis and multi-objective optimization for a gas turbine, heat recovery steam generator and multi-effect thermal vapour compression desalination unit are investigated in this article. To optimize the system and to determine the optimal values of design parameters, the genetic algorithm was used. The first objective function was considered as the sum of investment and operational costs as well as penalty for producing [Formula: see text] emissions. The second objective function was the cycle total amount of exergy destruction. The optimal values of design parameters such as drum pressure of heat recovery steam generator, pinch point temperature in heat recovery steam generator, top brine temperature, last stage temperature, minimum temperature difference of condenser and number of effects were then estimated. Also, the effects of gas turbine part load as well as ambient temperature and fuel cost changes on the optimal values of design parameters were analysed.

Experimental assessment and optimization of design parameters of thermo-hydraulic performance of heat exchanger tube using hollow elliptical swirl generators

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.

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.

This study focuses on the determination of the optimum values of the design parameters for heat transfer and pressure drop in a tube equipped with twisted tape using Taguchi method. Design parameters affecting the heat transfer rate and pressure loss were chosen as clearance ratio (c/D = 0.0178, 0.0357, 0.0535 and 0.0714), twist ratio (y/D = 2, 2.5, 3 and 3.5), and Reynolds number (Re = 5,200, 9,600, 14,000 and 18,400). Thus, Nusselt number in terms of heat transfer and pressure drop (ΔP) basis of flow friction were considered as performance parameters. L16(43), where 16, 4, 3 indicates the number of experiments, levels and parameters, orthogonal array was selected as experimental plan. Signal to noise ratios and contribution ratios of each parameter were calculated. The purpose of this study is to obtain the highest Nusselt number and the lowest pressure drop by reducing the number of experiments, and so to provide less experimental cost and time. Consequently, the heat transfer augments with the decrease of twist ratio, clearance ratio, but the increase of Reynolds number. In addition, pressure drop decreases with the increase of twist ratio, clearance ratio, and the decrease of Reynolds number. Optimum values were found as c/D = 0.0178, y/D = 2 and Re = 18,400 for Nusselt number and c/D = 0.0714, y/D = 3.5 and Re = 5200 for pressure drop.

This article is about a review of optimization of metal injection molding and microwave sintering process on tungsten cemented carbide produce by metal injection molding process. In this study, the process parameters for the metal injection molding were optimized using Taguchi method. Taguchi methods have been used widely in engineering analysis to optimize the performance characteristics through the setting of design parameters. Microwave sintering is a process generally being used in powder metallurgy over the conventional method. It has typical characteristics such as accelerated heating rate, shortened processing cycle, high energy efficiency, fine and homogeneous microstructure, and enhanced mechanical performance, which is beneficial to prepare nanostructured cemented carbides in metal injection molding. Besides that, with an advanced and promising technology, metal injection molding has proven that can produce cemented carbides. Cemented tungsten carbide hard metal has been used widely in various applications due to its desirable combination of mechanical, physical, and chemical properties. Moreover, areas of study include common defects in metal injection molding and application of microwave sintering itself has been discussed in this paper.

Ride comfort, safety, wear and vehicle speed are the most important factors in evaluating the efficiency of railway transportation. In order to decrease the track access charges it is often desirable to run the vehicle at maximum allowed speed, while keeping an admissible amount of wear in system. This usually deteriorates the ride comfort and safety level during the operation. Therefore, an optimization problem to find a tradeoff value for vehicle speed and design parameters is inevitable. Since, ride comfort, safety and wear values are sensitive to primary and secondary suspensions’ damping parameters it is desirable to find the optimum values of such design variables. In this regard, the multiobjective optimization of railway vehicle dampers is considered to increase the cost-efficiency of railway operation. One car vehicle model with 26 degrees of freedom (DOF) along with a set of initial states, design parameters and operational conditions is explored here. All bodies are assumed to be rigid. Vehicle carbody and bogie frames supposed to have the full set of DOF in space. While, only lateral and yaw motions are considered for each wheelset. It is also assumed that wheelset roll angle is a function of the lateral displacement. Primary and secondary suspensions compromised of parallel linear springs and dampers in longitudinal, vertical and lateral directions which connect wheelsets to bogie frames, and bogie frames to carbody, respectively. Lagrange formalism is employed to obtain the system’s equations of motion. The nonlinear heuristic theory is chosen to relate creepages and the corresponding creep contact forces. The dynamic response of the system is obtained for different operational scenarios including ideal and imperfect tangent and curved tracks. Series-based functions are chosen to approximate the harmonic lateral track irregularities. Accelerations at carbody level, shift forces and wear number are used to evaluate the ride comfort, safety and wear, respectively. MATLAB genetic algorithm optimization routine is applied to perform the optimization. The Pareto sets and Pareto fronts obtained from this study provide the vectors of optimal design parameters corresponding to maximum admissible vehicle speed and guarantee the best tradeoff values for safety and comfort with threshold on wear for each operational scenario. Analysis of the obtained results gives insight into multiobjective optimized dynamic response of a railway vehicle and useful hints for designing adaptive bogie systems with the possibility to switch between optimal damping parameters value and provide the best operational efficiency.

Drilling, a hole making process is especially important because it accounts for a large portion of overall machining operations. Amongst all machining operations, drilling using twist drill is most commonly applied method for generating holes for riveting and fastening of structural assemblies.This paper deals with multi performance characteristics in drilling of Aluminum 6061 alloy with water based Nano fluid (small amount of Al2O3 Nano particles in water) using Grey based – Taguchi method. Five parameters namely cutting speed, feed rate, point angle, clearance angle and variable volume fraction of Al2O3 Nanoparticles in water were identified and their ranges for the present investigation were determined from preliminary experiments. Thrust force and surface roughness are recorded using well calibrated equipments at 3 levels with L27 orthogonal array as per Taguchi experimental plan. Optimal combination of process parameters was obtained to minimize the recorded responses. In the case where experiments are ambiguous or when the experimental method cannot be carried out exactly, grey analysis helps to compensate for the shortcoming in statistical regression. Grey based – Taguchi method is an effective tool for analyzing the relationship between sequences with less data and can analyze many factors that can overcome the disadvantages of statistical method. Most of manufacturing industries perform a huge number of drilling operations in machine shops. The drilling technology has been studied to improve the cutting performance with optimizing the cutting parameters and the tool geometry. Some researchers have developed mathematical models of drilling to estimate thrust and torque. Williams [1] showed that during cutting there are three identifiable zones of interest at the drill point, the main cutting edges, the secondary cutting edges at the chisel edge and an indentation zone about the drill center. Zhang et al. model was based on mechanics of vibration and the continuous distribution of thrust and torque along the lip and the chisel edge of the twist drill [2]. The mean thrust and torque increased as feed increases under constant vibration parameters [3–4]. They concluded that vibration drilling is different from conventional drilling and it is a dynamic cutting process. Another model was presented for drilling processes by Yang et al. [5]. A statistical analysis of hole quality was performed by Furness et al. [12]. They found that feed and speed have a relatively small effect on the measured hole quality features. With the expectation of hole location error, the hole quality is not predictably or significantly affected by the cutting conditions. Although the authors did not expect these results, they have the important positive implication that production rates may be increased without sacrificing hole quality. Rincon and Ulsoy[15] showed that the changes in the relative motion of the drill do affect the variations of the forces. An increase in the ranges of drill motion results in an increase in the ranges of torque and thrust. The 6061 alloy of aluminum is primarily composed of magnesium and silicon. Some other elements of 6061 aluminum alloy include small amounts of iron, copper, manganese,