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.

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.

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.

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 steel casting feeding system based on BP neural network and genetic algorithm

The trial-and-error method is widely used for the current optimization of the steel casting feeding system, which is highly random, subjective and thus ineff icient. In the present work, both the theoretical and the experimental research on the modeling and optimization methods of the process are studied. An approximate alternative model is established based on the Back Propagation(BP) neural network and experimental design. The process parameters of the feeding system are taken as the input, the volumes of shrinkage cavities and porosities calculated by simulation are simultaneously taken as the output. Thus, a mathematical model is established by the BP neural network to combine the input variables with the output response. Then, this model is optimized by the nonlinear optimization function of the genetic algorithm. Finally, a feeding system optimization of a steel traveling wheel is conducted. No shrinkage cavities and porosities are induced through the optimization. Compared to the initial design scheme, the process yield is increased by 4.1% and the volume of the riser is decreased by 5.48×10~6 mm3.

Multi-objective quality control method for cold-rolled products oriented to customized requirements

To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization(PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company's cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression(MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression(SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index,which were more in line with actual production process requirements.

Hot deformation behavior and process parameters optimization of Ti-6Al-7Nb alloy using constitutive modeling and 3D processing map

The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940-1030℃and the strain rate ranges of 0.001-10 s^(-1).Meanwhile,the activation energy of thermal deformation was computed.The results show that the flow stress of Ti-6Al-7Nb alloy increases with increasing the strain rate and decreasing the deformation temperature.The activation energy of thermal deformation for Ti-6Al-7Nb alloy is much greater than that for self-diffusion ofα-Ti andβ-Ti.Considering the influence of strain on flow stress,the strain-compensated Arrhenius constitutive model of Ti-6Al-7Nb alloy was established.The error analysis shows that the model has higher accuracy,and the correlation coefficient r and average absolute relative error are 0.9879 and 4.11%,respectively.The processing map(PM)of Ti-6Al-7Nb alloy was constructed by the dynamic materials model and Prasad instability criterion.According to PM and microstructural observation,it is found that the main form of instability zone is local flow,and the deformation mechanisms of the stable zone are mainly superplasticity and dynamic recrystallization.The optimal processing parameters of Ti-6Al-7Nb alloy are determined as follows:960-995℃/0.01-0.18 s^(-1)and 1000-1030℃/0.001-0.01 s^(-1).

Optimization of Inactivation Conditions of High Hydrostatic Pressure Using Response Surface Methodology

Response surface methodology (RSM) was employed in the present work and a second orderquadratic equation for high hydrostatic pressure (HHP) inactivation was built. Theadequacy of the model equation for predicting the optimum response values was verifiedeffectively by the validation data. Effects of temperature, pressure, and pressureholding time on HHP inactivation of Escherichia coli ATCC 8739 were explored. Byanalyzing the response surface plots and their corresponding contour plots as well assolving the quadratic equation, the optimum process parameters for inactivation E. coliof six log cycles were obtained as: temperature 32.2℃, pressure 346.4 MPa, and pressureholding time 12.6min.

Experimental research on optimization of compression molding process parameters of pineapple rind residue

Currently,the process parameters for compression molding of pineapple rind residue are not clear.In view of this problem,a single die hole compression molding test device was designed in this study,and the force of material in a mold hole was analyzed.Using the test device,a three-factor three-level orthogonal test was carried out by using the particle size,moisture content,and die hole length-to-diameter ratio of pineapple rind residue as the factors and the particle molding rate,relax density,and specific energy consumption as the indicators.The test results were analyzed by range analysis,variance analysis,and fuzzy comprehensive evaluation.The test results show that the main and secondary factors affecting the comprehensive performance of pineapple rind residue compression molding are length-to-diameter ratio,particle size,and moisture content.The optimal parameter combination is the material particle size of 6-9 mm,moisture content of 16%,and length-to-diameter ratio of 4:1.The best indicators under these conditions are particle molding rate of 97.80%,relax density of 1.32 g/cm,and specific energy consumption of 44.17 J/g.These research results can provide a reference for the selection of processing parameters and the design of molding equipment.