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.

Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.

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.

Multi-objective process parameter optimization for energy saving in injection molding process

This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of variance(ANOVA),a process modeling algorithm by artificial neural network(ANN),and a multi-objective parameter optimization algorithm by genetic algorithm(GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization(MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process(IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization,though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior.

Multi-Objective Optimization Analysis of Auxiliary Flux Modulator Magnetic Gear with Unequal Magnetic Poles

To enhance the output torque and minimize the torque ripple of coaxial magnetic gear(CMG),a novel auxiliary flux modulator CMG with unequal magnetic poles is proposed.This design incorporates an inner rotor with an asymmetric sector and a trapezoidal combined N-S pole structure,featuring Halbach arrays for the arrangement of permanent magnets(PMs).The outer rotor PMs adopt a Spoke-type configuration.To optimize the CMG for high output torque and low torque ripple,a sensitivity analysis is conducted to identify key size parameters that significantly influence the optimization objectives.Based on the sensitivity hierarchy of these parameters,a multi-objective optimization analysis is performed using a genetic algorithm(GA)to determine the optimal structural parameter values of the CMG.In addition,a coaxial magnetic gear(CMG)topology with 4 inner and 17 outer pole pairs is adopted,and the parametric model is established.Finally,the electromagnetic properties of the CMG are evaluated using the finite element method.The results indicate a remarkable reduction in torque ripple,specifically by 46.15%.

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.

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.

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.

OPTIMIZATION OF PROCESSING PARAMETERS DURING ISM PROCESS OF Ti-15-3

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Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning

The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays.This paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time delay.It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time delays.Since the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization algorithms.The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned ADRC.The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.

Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology

The heat treatable aluminum-copper alloy AA2014 finds wide application in the aerospace and defence industry due to its high strength-toweight ratio and good ductility. Friction stir welding(FSW) process, an emerging solid state joining process, is suitable for joining this alloy compared to fusion welding processes. This work presents the formulation of a mathematical model with process parameters and tool geometry to predict the responses of friction stir welds of AA 2014-T6 aluminum alloy, viz yield strength, tensile strength and ductility. The most influential process parameters considered are spindle speed, welding speed, tilt angle and tool pin profile. A four-factor, five-level central composite design was used and a response surface methodology(RSM) was employed to develop the regression models to predict the responses.The mechanical properties, such as yield strength(YS), ultimate tensile strength(UTS) and percentage elongation(%El), are considered as responses. Method of analysis of variance was used to determine the important process parameters that affect the responses. Validation trials were carried out to validate these results. These results indicate that the friction stir welds of AA 2014-T6 aluminum alloy welded with hexagonal tool pin profile have the highest tensile strength and elongation, whereas the joints fabricated with conical tool pin profile have the lowest tensile strength and elongation.

Optimization of processing parameters for rheo-casting AZ91D magnesium alloy

A design of experiment technique was used to optimize the microstructure of the AZ91D alloy produced by rheo-casting. The experimental design consists of four parameters(pouring temperature,shearing temperature,shearing time and shearing rate) with three levels.The grain size and shape factor measurements of primaryα-Mg particles were conducted to determine the microstructure.The contribution of each parameter shows that pouring temperature is the most significant parameter affecting the grain size,and the shape factor highly depends on the shearing temperature.The optimized rheo-casting processing parameters are 650 C for pouring temperature,585℃for shearing temperature,40 s for shearing time,and 600 r/min for shearing rate.Under the optimized processing parameters,the average grain size is 28.53μm,and the shape factor is 0.591.

Dynamic Multi-objective Optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

Review of Design of Process Parameters for Squeeze Casting

Squeeze casting(SC)is an advanced net manufacturing process with many advantages for which the quality and properties of the manufactured parts depend strongly on the process parameters.Unfortunately,a universal efficient method for the determination of optimal process parameters is still unavailable.In view of the shortcomings and development needs of the current research methods for the setting of SC process parameters,by consulting and analyzing the recent research literature on SC process parameters and using the CiteSpace literature analysis software,manual reading and statistical analysis,the current state and characteristics of the research methods used for the determination of SC process parameters are summarized.The literature data show that the number of pub-lications in the literature related to the design of SC process parameters generally trends upward albeit with signifi-cant fluctuations.Analysis of the research focus shows that both“mechanical properties”and“microstructure”are the two main subjects in the studies of SC process parameters.With regard to materials,aluminum alloys have been extensively studied.Five methods have been used to obtain SC process parameters:Physical experiments,numeri-cal simulation,modeling optimization,formula calculation,and the use of empirical values.Physical experiments are the main research methods.The main methods for designing SC process parameters are divided into three categories:Fully experimental methods,optimization methods that involve modeling based on experimental data,and theoreti-cal calculation methods that involve establishing an analytical formula.The research characteristics and shortcomings of each method were analyzed.Numerical simulations and model-based optimization have become the new required methods.Considering the development needs and data-driven trends of the SC process,suggestions for the develop-ment of SC process parameter research have been proposed.

Simulation Research on the Effect of Spreading Process Parameters on the Quality of Lunar Regolith Powder Bed in Additive Manufacturing

Lunar surface additive manufacturing with lunar regolith is a key step in in-situ resource utilization.The powder spreading process is the key process,which has a major impact on the quality of the powder bed and the precision of molded parts.In this study,the discrete element method(DEM)was adopted to simulate the powder spreading process with a roller.The three powder bed quality indicators,including the molding layer offset,voidage fraction,and surface roughness,were established.Besides,the influence of the three process parameters,which are roller’s translational speed,rotational speed,and powder spreading layer thickness on the powder bed quality indicators was also analyzed.The results show that with the reduction of the powder spreading layer thickness and the increase of the rotational speed,the offset increased significantly;when the translational speed increased,the offset first increased and then decreased,which resulted in an extreme value;with the increase of the layer thickness and the decrease of the translational speed,the values for voidage fraction and surface roughness significantly reduced.The powder bed quality indicators were adopted as the optimization objective,and the multi-objective parameter optimization was carried out.The predicted optimal powder spreading parameters and powder bed quality indicators were then obtained.Moreover,the optimal values were then verified.This study can provide informative guidance for in-situ manufacturing at the moon in future deep space exploration missions.

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.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.