Optimization of chemistry and process parameters for control of intermetallic formation in Mg sludges

Intermetallic formation in sludge during magnesium(Mg)melting,holding and high pressure die casting practices is a very important issue.But,very often it is overlooked by academia,original equipment manufacturers(OEM),metal ingot producers and even die casters.The aim of this study was to minimize the intermetallic formation in Mg sludge via the optimization of the chemistry and process parameters.The Al8Mn5 intermetallic particles were identified by the microstructure analysis based on the Al and Mn ratio.The design of experiment(DOE)technique,Taguchi method,was employed to minimize the intermetallic formation in the sludge of Mg alloys with various chemical compositions of Al,Mn,Fe,and different process parameters,holding temperature and holding time.The sludge yield(SY)and intermetallic size(IS)was selected as two responses.The optimum combination of the levels in terms of minimizing the intermetallic formation were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,690℃ for the holding temperature and holding at 30 mins for the holding time,respectively.The best combination for smallest intermetallic size were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,630℃ for the holding temperature and holding at 60 mins for the holding time,respectively.Three groups of sludge factors,Chemical Sludge(CSF),Physical Sludge(PSF)and Comprehensive Sludge Factors(and CPSF)were established for prediction of sludge yields and intermetallic sizes in Al-containing Mg alloys.The CPSF with five independent variables including both chemical elements and process parameters gave high accuracy in prediction,as the prediction of the PSF with only the two processing parameters of the melt holding temperature and time showed a relatively large deviation from the experimental data.The Chemical Sludge Factor was primarily designed for small ingot producers and die casters with a limited melting and holding capacity,of which process parameters could be fixed easily.The Physical Sludge Factor could be used for mass production with a single type of Mg alloy,in which the chemistry fluctuation might be negligible.In large Mg casting suppliers with multiple melting and holding furnaces and a number of Mg alloys in production,the Comprehensive Sludge Factor should be implemented to diminish the sludge formation.

Process Parameters Optimization of Laser Cladding for HT200 with 316L Coating Based on Response Surface Method

In order to improve the sealing surface performance of gray cast iron gas gate valves and achieve precise molding control of the cladding layer,as well as to reveal the influence of laser cladding process parameters on the morphology and structure of the cladding layer,we prepared the 316L coating on HT 200 by using Design-Expert software central composite design(CCD)based on response surface analysis.We built a regression prediction model and analyzed the ANOVA with the inspection results.With a target cladding layer width of 3.5 mm and height of 1.3 mm,the process parameters were optimized to obtain the best combination of process parameters.The microstructure,phases,and hardness variations of the cladding layer from experiments with optimal parameters were analyzed by the metallographic microscope,confocal microscope,and microhardness instrument.The experimental results indicate that laser power has a significant impact on the cladding layer width,followed by powder feed rate;scan speed has a significant impact on the cladding layer height,followed by powder feed rate.The HT200 substrate and 316L can metallurgically bond well,and the cladding layer structure consists of dendritic crystals,columnar crystals,and equiaxed crystals in sequence.The optimal process parameter combination satisfying the morphology requirements is laser power(A)of 1993 W,scan speed(B)of 8.949 mm/s,powder feed rate(C)of 1.408 r/min,with a maximum hardness of 1564.3 HV0.5,significantly higher than the hardness of the HT200 substrate.

Optimization Study of Active-Passive Heating System Parameters in Village Houses in the Southern Xinjiang Province

Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang,a combined active-passive heating system was proposed,and the simulation software was used to optimize the parameters of the system,according to the parameters obtained from the optimization,a test platform was built and winter heating test was carried out.The simulation results showed that the thickness of the air layer of 75 mm,the total area of the vent holes of 0.24 m^(2),and the thickness of the insulation layer of 120 mm were the optimal construction for the passive part;solar collector area of 28 m^(2),hot water storage tank volume of 1.4 m^(3),mass flow rate of 800 kg/h on the collector side,mass flow rate of 400 kg/h on the heat exchanger side,and output power of auxiliary heat source of 5∼9 kWwere the optimal constructions for active heating system.Test results showed that during the heating period,the system could provide sufficient heat to the room under different heating modes,and the indoor temperature reached over 18°C,which met the heating demand.The economic and environmental benefits of the system were analyzed,and the economic benefits of the systemwere better than coal-fired heating,and the CO_(2) emissionswere reduced by 3,292.25 kg compared with coalfiredheating.The results of the study showed that the combinedactive-passiveheating systemcouldeffectively solve the heating problems existing in rural buildings in Southern Xinjiang,and it also laid the theoretical foundation for the popularization of the combined heating systems.

Optimization of AZ80 magnesium alloy squeeze cast process parameters using morphological matrix

The squeeze cast process parameters of AZ80 magnesium alloy were optimized by morphological matrix. Experiments were conducted by varying squeeze pressure, die pre-heat temperature and pressure duration using L9（33） orthogonal array of Taguchi method. In Taguchi method, a 3-level orthogonal array was used to determine the signal/noise ratio. Analysis of variance was used to determine the most significant process parameters affecting the mechanical properties. Mechanical properties such as ultimate tensile strength, elongation and hardness of the components were ascertained using multi variable linear regression analysis. Optimal squeeze cast process parameters were obtained.

High temperature deformation behavior and optimization of hot compression process parameters in TC11 titanium alloy with coarse lamellar original microstructure

The high temperature deformation behaviors of α＋β type titanium alloy TC11 （Ti-6.5Al-3.5Mo-1.5Zr-0.3Si） with coarse lamellar starting microstructure were investigated based on the hot compression tests in the temperature range of 950-1100 ℃ and the strain rate range of 0.001-10 s-1. The processing maps at different strains were then constructed based on the dynamic materials model, and the hot compression process parameters and deformation mechanism were optimized and analyzed, respectively. The results show that the processing maps exhibit two domains with a high efficiency of power dissipation and a flow instability domain with a less efficiency of power dissipation. The types of domains were characterized by convergence and divergence of the efficiency of power dissipation, respectively. The convergent domain in a＋fl phase field is at the temperature of 950-990 ℃ and the strain rate of 0.001-0.01 s^-1, which correspond to a better hot compression process window of α＋β phase field. The peak of efficiency of power dissipation in α＋β phase field is at 950 ℃ and 0.001 s 1, which correspond to the best hot compression process parameters of α＋β phase field. The convergent domain in β phase field is at the temperature of 1020-1080 ℃ and the strain rate of 0.001-0.1 s^-l, which correspond to a better hot compression process window of β phase field. The peak of efficiency of power dissipation in ℃ phase field occurs at 1050 ℃ over the strain rates from 0.001 s^-1 to 0.01 s^-1, which correspond to the best hot compression process parameters of ,8 phase field. The divergence domain occurs at the strain rates above 0.5 s^-1 and in all the tested temperature range, which correspond to flow instability that is manifested as flow localization and indicated by the flow softening phenomenon in stress-- strain curves. The deformation mechanisms of the optimized hot compression process windows in a＋β and β phase fields are identified to be spheroidizing and dynamic recrystallizing controlled by self-diffusion mechanism, respectively. The microstructure observation of the deformed specimens in different domains matches very well with the optimized results.

Application of EBSD technique to ultrafine grained and nanostructured materials processed by severe plastic deformation:Sample preparation, parameters optimization and analysis

With the help of FESEM, high resolution electron backscatter diffraction can investigate the grains/subgrains as small as a few tens of nanometers with a good angular resolution （~0.5°）. Fast development of EBSD speed （up to 1100 patterns per second） contributes that the number of published articles related to EBSD has been increasing sharply year by year. This paper reviews the sample preparation, parameters optimization and analysis of EBSD technique, emphasizing on the investigation of ultrafine grained and nanostructured materials processed by severe plastic deformation （SPD）. Detailed and practical parameters of the electropolishing, silica polishing and ion milling have been summarized. It is shown that ion milling is a real universal and promising polishing method for EBSD preparation of almost all materials. There exists a maximum value of indexed points as a function of step size. The optimum step size depends on the magnification and the board resolution/electronic step size. Grains/subgrains and texture, and grain boundary structure are readily obtained by EBSD. Strain and stored energy may be analyzed by EBSD.

Machine tool selection based on fuzzy evaluation and optimization of cutting parameters

The paper analyzes the factors influencing machine tool selection. By using fuzzy mathematics theory, we establish a theorietical model for optimal machine tool selection considering geometric features, clamping size, machining range, machining precision and surface roughness. By means of fuzzy comprehensive evaluation method, the membership degree of machine tool selection and the largest comprehensive evaluation index are determined. Then the reasonably automatic selection of machine tool is realized in the generative computer aided process planning （CAPP） system. Finally, the finite element model based on ABAQUS is established and the cutting process of machine tool is simulated. According to the theoretical and empirical cutting parameters and the curve of surface residual stress, the optimal cutting parameters can be determined.

Parameters optimization of electroless deposition of Cu on Cr-coated diamond

Electroless copper plating on diamond particles precoated with 1%Cr was carried out to evaluate the effects of various experimental parameters on coating quality and deposition rate to obtain the optimized reaction parameters. The formulated samples under optimized parameters were characterized by X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, X-ray photoelectron spectra and optical microscopy. The best parameters, where uniform and maximum coating thickness was achieved, are etching with 20%NaOH for 30 min, sensitization and activation with SnCl2 and PdCl2 for 5 and 20 min, respectively. The composition of the copper solution bath was 16 g/L CuSO4＆#183;5H2O, 35 mL/L formaldehyde （HCHO）, 23 g/L KNaC4H4O6 at 60 ℃, pH=13 and stirring at （350±15） r/min under ultrasonication.

Parametric Optimization of Wheel Spoke Structure for Drag Reduction of an Ahmed Body

The wheels have a considerable influence on the aerodynamic properties and can contribute up to 25%of the total drag on modern vehicles.In this study,the effect of the wheel spoke structure on the aerodynamic performance of the isolated wheel is investigated.Subsequently,the 35°Ahmed body with an optimized spoke structure is used to analyze the flow behavior and the mechanism of drag reduction.The Fluent software is employed for this investigation,with an inlet velocity of 40 m/s.The accuracy of the numerical study is validated by comparing it with experimental results obtained from the classical Ahmed model.To gain a clearer understanding of the effects of the wheel spoke parameters on the aerodynamics of both the wheel and Ahmedmodel,and five design variables are proposed:the fillet angleα,the inside arc radius R1,the outside radius R2,and the same length of the chord L1 and L2.These variables characterize the wheel spoke structure.The Optimal Latin Hypercube designmethod is utilized to conduct the experimental design.Based on the simulation results of various wheel spoke designs,the Kriging model and the adaptive simulated annealing algorithm is selected to optimize the design parameters.The objective is to achieve the best combination for maximum drag reduction.It is indicated that the optimized spoke structure resulted in amaximum drag reduction of 5.7%and 4.7%for the drag coefficient of the isolated wheel and Ahmed body,respectively.The drag reduction is primarily attributed to changes in the flow state around the wheel,which suppressed separation bubbles.Additionally,it influenced the boundary layer thickness around the car body and reduced the turbulent kinetic energy in the wake flow.These effects collectively contributed to the observed drag reduction.

Parameter Optimization of Tuned Mass Damper Inerter via Adaptive Harmony Search

Dynamic impacts such as wind and earthquakes cause loss of life and economic damage.To ensure safety against these effects,various measures have been taken from past to present and solutions have been developed using different technologies.Tall buildings are more susceptible to vibrations such as wind and earthquakes.Therefore,vibration control has become an important issue in civil engineering.This study optimizes tuned mass damper inerter(TMDI)using far-fault ground motion records.This study derives the optimum parameters of TMDI using the Adaptive Harmony Search algorithm.Structure displacement and total acceleration against earthquake load are analyzed to assess the performance of the TMDI system.The effect of the inerter when connected to different floors is observed,and the results are compared to the conventional tuned mass damper(TMD).It is indicated that the case of connecting the inerter force to the 5th floor gives better results.As a result,TMD and TMDI systems reduce the displacement by 21.87%and 25.45%,respectively,and the total acceleration by 25.45%and 19.59%,respectively.These percentage reductions indicated that the structure resilience against dynamic loads can be increased using control systems.

Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.

Reliability-based Robust Optimization Design of Automobile Components with Non-normal Distribution Parameters

In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (】0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (】0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.

Multi-objective optimization of process parametersduring low-pressure die casting of AZ91Dmagnesium alloy wheel castings

Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.

Parameters Optimization of a Novel 5-DOF Gasbag Polishing Machine Tool

The research on the parameters optimization for gasbag polishing machine tools, mainly aims at a better kinematics performance and a design scheme. Serial structural arm is mostly employed in gasbag polishing machine tools at present, but it is disadvantaged by its complexity, big inertia, and so on. In the multi-objective parameters optimization, it is very difficult to select good parameters to achieve excellent performance of the mechanism. In this paper, a statistics parameters optimization method based on index atlases is presented for a novel 5-DOF gasbag polishing machine tool. In the position analyses, the structure and workspace for a novel 5-DOF gasbag polishing machine tool is developed, where the gasbag polishing machine tool is advantaged by its simple structure, lower inertia and bigger workspace. In the kinematics analyses, several kinematics performance evaluation indices of the machine tool are proposed and discussed, and the global kinematics performance evaluation atlases are given. In the parameters optimization process, considering the assembly technique, a design scheme of the 5-DOF gasbag polishing machine tool is given to own better kinematics performance based on the proposed statistics parameters optimization method, and the global linear isotropic performance index is 0.5, the global rotational isotropic performance index is 0.5, the global linear velocity transmission performance index is 1.012 3 m/s in the case of unit input matrix, the global rotational velocity transmission performance index is 0.102 7 rad/s in the case of unit input matrix, and the workspace volume is 1. The proposed research provides the basis for applications of the novel 5-DOF gasbag polishing machine tool, which can be applied to the modern industrial fields requiring machines with lower inertia, better kinematics transmission performance and better technological efficiency.

Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

A New Chaotic Parameters Disturbance Annealing Neural Network for Solving Global Optimization Problems

Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.

Optimization of Cutting Parameters in Helical Milling of Carbon Fiber Reinforced Polymer

To investigate cutting performance in the helical milling of carbon fiber reinforced polymer(CFRP),experiments were conducted with unidirectional laminates.The results show that the influence of cutting parameters is very significant in the helical milling process. The axial force increases with the increase of cutting speed, which is below 95 m/min; otherwise, the axial force decreases with the increase of cutting speed. The resultant force always increases when cutting speed increases; with the increase of tangential and axial feed rates, cutting forces increase gradually. In addition, damage rings can appear in certain regions of the entry edges; therefore, the relationship between machining performance(cutting forces and holemaking quality) and cutting parameters is established using the nonlinear fitting methodology. Thus, three cutting parameters in the helical milling of CFRP, under the steady state, are optimized based on the multi-objective genetic algorithm, including material removal rate and machining performance. Finally, experiments were carried out to prove the validity of optimized cutting parameters.

Mechanism and optimization of fuel injection parameters on combustion noise of DI diesel engine

Combustion noise takes large proportion in diesel engine noise and the studies of its influence factors play an important role in noise reduction. Engine noise and cylinder pressure measurement experiments were carried out. And the improved attenuation curves were obtained, by which the engine noise was predicted. The effect of fuel injection parameters in combustion noise was investigated during the combustion process. At last, the method combining single variable optimization and multivariate combination was introduced to online optimize the combustion noise. The results show that injection parameters can affect the cylinder pressure rise rate and heat release rate, and consequently affect the cylinder pressure load and pressure oscillation to influence the combustion noise. Among these parameters, main injection advance angle has the greatest influence on the combustion noise, while the pilot injection interval time takes the second place, and the pilot injection quantity is of minimal impact. After the optimal design of the combustion noise, the average sound pressure level of the engine is distinctly reduced by 1.0 d B(A) generally. Meanwhile, the power, emission and economy performances are ensured.

Optimization of investment casting process parameters to reduce warpage of turbine blade platform in DD6 alloy

The large warping deformation at platform of turbine blade directly affects the forming precision. In the present research, equivalent warping deformation was firstly presented to describe the extent of deformation at platform. To optimize the process parameters during investment casting to minimize the warping deformation of the platform, based on simulation with Pro CAST, the single factor method, orthogonal test, neural network and genetic algorithm were subsequently used to analyze the influence of pouring temperature, shell mold preheating temperature, furnace temperature and withdrawal velocity on dimensional accuracy of the platform of superalloyDD6 turbine blade. The accuracy of investment casting simulation was verified by measurement of platform at blade casting. The simulation results with the optimal process parameters illustrate that the equivalent warping deformation was dramatically reduced by 21.8% from 0.232295 mm to 0.181698 mm.

Optimal choice of parameters for particle swarm optimization

The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.