Values Of Control Factors Research Articles

DOE optimization of a liquid cold plate used in a thermal management system for power converters in the railway sector through 3D CFD analysis

The thermal management system for power electronics is becoming a key factor in order to guarantee optimal working conditions, efficiency and safety of those components. In the field of liquid-cooled power electronics, the design of an optimal liquid cold plate (LCP) is fundamental to increase the heat transfer, minimizing the maximum temperature and the pressure drop. This work presents an optimization procedure of a finned extruded profile of an LCP for insulated gate bipolar transistor (IGBT) power module, for railway propulsion. The optimization has been performed through DOE, by considering different values of several control factors (shape of the fin, length of the fin, pitch between the channels created by the fins), in order to minimize the LCP maximum temperature and the pressure drop between inlet and outlet. The combination of different values of control factors results in 18 prototypes, which have been compared using three-dimensional CFD simulations, performed in Ansys Fluent environment. The influence of parameters variation on the objective functions (i.e. maximum temperature of the liquid cold plate and pressure drop) has been verified.

Harmonic analysis of a marine shaft-bearing suspension system and its semi-active control with PID strategy

A stable operating state of the marine shaft-bearing system is essential to minimize the vibration level and improve the transmission efficiency of the ship power system. A semi-active control with PID strategy is proposed through an adjustable damping magnetorheological shock absorber and the combination of the electromagnetic actuator based on the existing suspension system. The harmonic response of the system is calculated through transfer function with various suspension stiffness and damping. The controlled behaviors include suspension travel, soleplate deflection, sprung shaft and unsprung bearing, are obtained through Matlab/Simulink. The proposed controller is demonstrated to be applicable through robustness analysis by comparing with passive and LQR control based on the disturbance of pulse width modulation. The controlled behaviors with minor stiffness and significant damping are noticed to be more satisfied through the influence comparison of suspension parameters. The controlled performance includes reduction rate, overshoot rate and setting time with various values of control factors are discussed to prove the suitability. The semi-active control for the transverse vibration of the marine shaft-bearing system is thus realized based on the adjustment of control parameters.

Optimization of Process Parameters in Wire-EDM of AL6063-T6 Alloy Using Taguchi DOE

Abstract: Wire-EDM is a controlled machining process used to produce geometrically intricate shapes with great accuracy and good surface finish that are difficult to machine with the help of conventional machining processes. Work has been done to study parametric optimization in wire-EDM machining of AL6063-T6 alloy. The input process parameters such as pulse on time (Ton), pulse off time (Toff), wire tension (WT), wire feed (WF) and dielectric flushing pressure (WP) with the performance parameters as surface roughness (Ra) and material removal rate (MRR). L8 orthogonal array (OA) of matrix developed by Taguchi was used for design of experiment and data analysis by implementing S/N ratios, ANOVA, and response optimization to predict values of control factors at optimum conditions of Ra and MRR.

Performance prediction and optimization of a photovoltaic thermal system integrated with phase change material using response surface method

Abstract In this study, a predictive model is developed based on the response surface method (RSM), to show the relationship between the operating factors (independent parameters) and some important response factors (dependent parameters) of a photovoltaic thermal system integrated with phase change material (PVT/PCM). The considered phase change material is the Rubitherm (RT) series of organic phase change materials. Moreover, both single and multi-objective optimization are conducted using RSM for different optimization objectives to recommend the optimal values of control factors for different operating conditions. The selected operating factors include PCM layer thickness, solar radiation, melting temperature of PCM and ambient temperature. Five response factors, including electrical power, thermal power, electrical exergy, thermal exergy and entropy generation are considered. To evaluate the model statistical adequacy, diagnostic analysis is performed for each response to indicate that the predictive model has a reasonable accuracy. The analysis of variance (ANOVA) test and perturbation analysis are also applied to evaluate the suitability and statistical importance of the recommended model and they show that the sensitivity of solar radiation on energy and exergy outputs is higher than that of other operating factors. According to the results, to maximize the electrical power, thermal power, electrical exergy, thermal exergy, and to minimize the entropy generation, simultaneously, it is recommended to select the PCM layer thickness of 1.5 cm, solar radiation of 901 W/m2, melting temperature of 25 °C, and the ambient temperature of 40 °C, based on the proposed method in this study.

Effect of Semi-Solid Heat Treatment on the Microstructure and Dry Sliding Wear Behavior of Al–20Si Alloy at Optimized Parametric Conditions

Hypereutectic Al–20Si alloy needs optimization of different process parameters in order to obtain maximum wear resistance for tribological applications aerospace and automobile industries. The present study attempts to find the best possible combination of the process parameters for the semi-solid heat treatment of the alloy in Taguchi Method. The effects of three different control variables i.e. sliding velocity, sliding distance, and load on the wear characteristics were investigated. The optimal values of control factors were determined on the basis of ‘smaller-is-better’ approach from Taguchi optimization technique. The optimized conditions for wear tests were then applied to the isothermally heat-treated samples (10–30 min) at 600 °C. The quantitative analysis shows the significant change in the size, morphology, and distribution of primary Si and eutectic Si with heat treatment. Moreover, the worn surfaces were analyzed from the microstructures obtained in Field Emission Scanning Electron Microscopy (FESEM). The improved wear resistance observed in the case of 20 min of holding time can be attributed to the combined effect of refinement of primary Si, modification of eutectic Si and equiaxed α-Al during semi-solid heat treatment.

Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l 1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.

МОДЕЛЬ «СОЦИАЛЬНЫЙ ПОТЕНЦИАЛ ДЕТСТВА В РОССИЙСКИХ РЕГИОНАХ»: РЕЗУЛЬТАТЫ ЭКСПЕРИМЕНТОВ

The article presents the results of mathematical experiments with the system «Social potential of childhood in the Russian regions». In the structure of system divided into three subsystems – the «Reproduction of children in the region», «Children’s health» and «Education of children», for each defined its target factor (output parameter). The groups of infrastructure factors (education, health, culture and sport, transport), socio-economic, territorial-settlement, demographic and en-vironmental factors are designated as the factors that control the system (input parameters). The aim of the study is to build a model îf «Social potential of childhood in the Russian regions», as well as to conduct experiments to find the optimal ratio of the values of target and control factors. Three waves of experiments were conducted. The first wave is related to the analysis of the dynam-ics of indicators for 6 years. The second – with the selection of optimal values of control factors at fixed ideal values of target factors. The third wave allowed us to calculate the values of the target factors based on the selected optimal values of the control factors of the previous wave.

Robust Design of a Low-Cost Permanent Magnet Motor with Soft Magnetic Composite Cores Considering the Manufacturing Process and Tolerances

This paper uses the Taguchi method to optimize the manufacturing process and robust design of a low-cost permanent magnet motor with soft magnetic composite (SMC) cores. For the manufacturing process, SMC cores are produced by using the molding technology without any wire cutting costs. To maximize the relative permeability and minimize the core loss, the Taguchi method is employed to identify the best control factor values for the heat treatment of SMC cores based on a series of experimental results. Due to the manufacturing tolerances, there are significant uncertainties in the core densities and motor dimensions, which will result in big performance variations for the SMC motors in the batch production. To obtain a robust design less sensitive to these tolerances, the conventional Taguchi parameter design method and a sequential Taguchi optimization method are presented to maximize the average torque and minimize the core loss of a low-cost PM motor. Through comparison, it is found that the proposed optimization method is efficient. It can provide an optimal design with better motor performance and manufacturing quality. The proposed method will benefit the industrial production of cost-effective PM-SMC motors with robust and compact designs.

Optimization of control factors in tribological studies for as cast and 1Hr T6 treated Al25Mg2Si2Cu4Fe alloy using Taguchi methods

In this study, using the predictive model offered by Taguchi, optimization of wear parameters was carried out. Controlling factors such as sliding speed, load and T6 heat treatment (homogenizing hours) were considered. Pin on disc type friction monitor was used for determing the volumetric wear rate under constant speed for as cast and 1Hr T6 treated conditions. The L16 orthogonal array, signal to noise ratio (S/N) and regression results were used to arrive at optimal control factor values that affect the volumetric wear rate of the alloy. Combination of the dominating control factors has been predicted using Taguchi technique. Predicted combination of control factors were found to be close to the experimental observations i.e load was found to be a dominant factor for volumetric wear rate. Also 1Hr T6 treated alloy shows a higher volumetric wear resistance as compared to as cast alloy.

Optimization of controllable turning parameters for high speed dry machining of a super alloy

Turning operations were performed on Inconel 718, a Nickel based super alloy under dry cutting conditions using PVD coated carbide cutting tool. An optimized set of input parameters have be presented for obtaining the best combination of Tool wear, Temperature, surface roughness and Material removal Rate. The control factors that were investigated were Cutting speed, feed and Depth of cut. The chosen response factors are Flank Wear, Tool Tip Temperature, Surface roughness and Material removal rate. For each control factor 3 levels were selected, conducting 27 experiments in total according to full factorial design of experiments. Regression was carried out for the experimental results, where the mathematical model was found to be adequate enough. The mathematical models obtained for both the responses were used for formulating a multi objective optimization which was solved using Genetic Algorithm for optimized control factor values. The optimized solutions were confirmed with experimental results. In addition, tool life was also calculated for optimized solution. A user friendly application program was developed to suit the industry needs.

Using the «virtual engine» software in automatic control system of gas turbine engine

The paper presents the mathematical tools and virtual engine software used in digital automatic control systems of modern gas turbine engines (GTE). The software is based on a real-time fully variable thermogasdynamic engine model operating in onboard computers. The new approach makes it possible to control the engine by the parameters inaccessible for measurement due to the virtual engine software in the gas turbine engine control system. The engine fault tolerance improves. We synthesized the control loops of a bypass turbojet engine with a high bypass ratio according to critical parameters (engine thrust, gas temperature in the combustor, stall margin) determined by calculations. We propose a methodology of restoring information on inlet airflow data and values of control factors in case of failure of information channels. The quality of engine regulation improved considerably. The lifetime of the engine increased essentially in the context of varying its characteristics in operation. We developed a methodology of real-time identification of an onboard engine thermogasdynamic simulation model based on the reverse feedback approach.

Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach

Several techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash – Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods.

Experimental Investigation into the Freeze-Thaw Resistance of Concrete Using Recycled Concrete Aggregates and Admixtures

This work investigates the effects of recycled concrete aggregates on the freeze-thaw resistance of the concrete. Taguchi orthogonal optimization technique was used in this investigation in order to minimize the number of samples required for each experiment. Therefore, only a few combination values of control factors required were chosen. Orthogonal table with three levels and four factors was used to prepare the mixing proportions. The results were analysis to check interaction among the factors. The results showed that in saturated Recycled Aggregate Concrete (RAC), the recycled aggregate has negative effects on its durability performance, therefore their use in structures exposed to severe climate is not strongly recommended. However, with the use of mineral and chemical admixtures, particularly air-entraining admixtures and fly ash, the recycled aggregate concrete could exhibit some level of durability.

Applying neural network and scatter search to optimize parameter design with dynamic characteristics

Parameter design is critical to enhancing a system's robustness by identifying specific control factor set points (levels) that make the system least sensitive to noise. Engineers have conventionally applied Taguchi methods to optimize parameter design. However, Taguchi methods can only obtain the optimal solution among the specified control factor levels. They cannot identify the real optimum when the parameter values are continuous. This study proposes a hybrid procedure combining neural networks and scatter search to optimize the continuous parameter design problem. First, neural networks are used to simulate the relationship between the control factor values and corresponding responses. Second, scatter search is employed to obtain the optimal parameter settings. The desirability function is utilized to transform the multiple responses into a single response. A case with dynamic characteristics is carried out in blood glucose strip manufacturing in Taiwan to demonstrate the practicability of the proposed procedure.

An integrated approach to enhance the optical performance of couplers based on neural networks, desirability functions and tabu search

Resolving a multi-response parameter design problem using the traditional Taguchi method is difficult. This study presents an integrated optimization approach based on neural networks, exponential desirability functions and tabu search to optimize this complicated problem. The proposed approach identifies input control factor settings that maximize the overall minimal satisfaction with all of the responses. The optimal input control factor values are no longer restricted to solution points composed of discrete experimental levels. The proposed approach was implemented for a Taiwanese fiber-optic passive component manufacturer and illustrated through optimizing a fused biconic taper process, thus improving the optical performance of 5% (5/95) single-window broadband couplers. The confirmation results demonstrated the practicability and effectiveness of the proposed approach. This optimization approach was successfully implemented in 5% (5/95) single-window broadband coupler manufacturing and the average defect rate was reduced to about 1.0%, from the previous over 15%.

Optimal production control problem in stochastic multiple-product multiple-machine manufacturing systems

This paper deals with the issue of production control in a manufacturing system with multiple machines which are subject to breakdowns and repairs. The control variables considered are the production rates for different products on the machines. Our objective is to minimize the expected total discounted cost due to the finished good inventories and backlogs. Based on the structure of the hedging point policy, a parameterized near-optimal production policy for a multiple-product manufacturing system is proposed. The analytical formalism is combined with simulation-based statistical tools, such as experimental design and response surface methodology. The aim of such a combination is to provide an approximation of the optimal control policy. In the proposed approach, the parameterized near-optimal control policy is used as an input for the simulation model. For each entry consisting of a combination of parameters, the cost incurred is obtained. The significant effects of the control variables are determined by the experimental design. The relationship between the cost and these input factors is obtained through a response surface model. It is from the obtained relationship that the best values of control factors are determined. Extensive computational experience is reported for two-part-type and five-part-type production systems. Finally, simulation experiments on several examples are concentrated on the sensitivities of the control policy obtained.

THE MOST ROBUST CONTROL FACTOR VALUES FOR RECORDING WITH THERMAL INTERFERENCE UNIFORMING TECHNIQUE ON DIRECT OVERWRITE MAGNETO-OPTICAL DISK

We have attempted to obtain the parameters of the recording laser waveform by computer simulation. For the laser waveform, we have used seven kinds of control factors. For the fluctuation of the mark shapes, we have used three kinds of noise factors. To find the optimum control factor values, we have calculated various mark shapes using a computer simulation model which we have already provided for the DOW-MO disk. We have calculated the mark shapes of each combination of control factors under the nine combinations of noise factors. Judging from the controllability of the mark edges, we have obtained the best, that means 'the most robust', control factor values. The best control factor values are eventually the same as the standard values which we have already published in Reference [1].