Squeeze Cast Process Parameters Research Articles

Optimization of Squeeze Casting Process Parameters on AA8011 Based Hmmcs Under NaCl Environment

Optimization of Squeeze Casting Process Parameters on AA8011 Based Hmmcs Under NaCl Environment

Intelligent optimization design of squeeze casting process parameters based on neural network and improved sparrow search algorithm

Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization. To adapt to diverse optimization applications of different process and target parameters, the backpropagation (BP) neural network and existing process data are utilized to intelligently establish the incompletely determined correlations between process parameters and squeeze cast quality or properties. An improved sparrow search algorithm (LCSSA) is developed and integrated to optimize both the aforementioned model structure and intelligently obtain the optimal solution, that is, the two stages of optimization. For the purpose of assessing the effect of each performance on the combination of process parameters, the information entropy weight approach is used. Various application cases and experiments have been conducted to evaluate the effectiveness of the suggested intelligent optimization design framework. It is indicated that the proposed method is practicable and can intelligently achieve ideal process parameters with high accuracy even based on small-scale data samples. The solving efficiency and optimization accuracy of LCSSA are superior than those of other intelligent optimization algorithms like the genetic algorithm (GA). The proposed framework outperforms the mixture of the BP neural network and other traditional optimization algorithms.

Investigation of the Influence of Indirect Squeeze Casting Process Parameters on the Solidification Microstructure and Properties of A356.2 Aluminum Alloy

Investigation of the Influence of Indirect Squeeze Casting Process Parameters on the Solidification Microstructure and Properties of A356.2 Aluminum Alloy

Optimization of squeeze casting process parameters on mechanical properties of SiCp reinforced LM25 composites through Taguchi technique

Abstract The aim of the present work is to examine the influence of processing parameters on fabricated composites of LM25 alloy with SiC particle reinforcement through a squeeze casting technique. The following process parameters, like stirring speed from 550 to 750 rpm, SiCP (4 wt% to 8 wt %), and melting temperatures (from 600 to 700 °C) were employed. Then, the processed composites were subjected to microscopy analysis and mechanical tests to ascertain their metallurgical and mechanical properties. SEM micrographs of an LM 25 composite sample show better bonding of SiC particles with matrix, which is due to homogeneous dispersion of SiC particles in the stir casting process. The maximum tensile strength (211 MPa) and hardness (91 Hv) were achieved on the composite samples with processing parameters of 750 rpm stirring speed, 8% SiC proportions, and 650 °C melting temperature, respectively. From the design of the experiment by the Taguchi method, it is observed that the stirring speed plays a significant role in achieving a better distribution of SiC particles in the composite samples than other parameters like SiC weight ratios and the melting temperature of the alloy.

Optimization of squeeze casting process parameters for the production of Al composite reinforced with bone powder

Al6061 alloy is most commonly used in automotive, marine, and aerospace applications to reduce weight and improve the strength of the composite. Al6061 alloy is a precipitation-hardened aluminium alloy used as a matrix material. Beef bone is a bio-waste which pollutes the environment and the people who live in the vicinity of its manufacturing and disposal sites. Bio-waste has been used in a variety of ways by researchers in recent years, including activated carbon, water purification, reinforcement in composites, fillers, medicines, food products and additives etc. Recycled beef bones that had been abandoned as waste were collected, cleaned, and ground into a fine powder with a particle size ranges between 5 and 50 µm and used as reinforcement to produce the Al composite. Because, beef bone has good tensile and compression strength and is light in weight, which helps improve the strength without much increase in cost of the Al composite. The aluminium composite are fabricated using squeeze casting process and the design of experiment based on Taguchi design are imposed to produce the high quality Al composite. The aluminium composite is fabricated and nine samples were successfully obtained for testing and analysis. The prepared substrates are analysed for hardness and tensile strength. Based on the experimental run the optimized process parameter conditions are found also regression equation is provided for the design. The optimized process parameters are found for melt temperature, squeeze pressure, reinforcement percentage, stirring speed as 700 °C, 100 MPa, 5%, 600 rpm respectively. Also, the highest contribution for improving the strength of the material is percentage of reinforcement.

Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters

Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters

An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique

Aluminum matrix composites known for their superior mechanical properties finds its use as liners in engine cylinders, discs, drum brakes, and pistons in automotive applications. This paper investigates the influence of squeeze casting process parameters on AA6061/Al2O3/SiC/Gr hybrid metal matrix composite using the encapsulated feeding technique. Four levels of factors selected for the L16 orthogonal array to optimize the process parameters were squeeze pressure (60, 80, 100, 120 MPa), melt temperature (700,750,800,850 °C), die temperature (100, 150, 200, 250 °C) and pressure holding time (5, 10, 15, 20 s). Hardness and tensile strength were measured for the designed experiments. Scanning electron microscope with energy-dispersive X-ray spectroscopy identified surface morphologies and elemental analysis. Optimized results were predicted using the artificial neural network. The melt temperature and squeeze pressure exhibited a significant contribution in controlling the mechanical behaviour of the hybrid composites. Taguchi analysis suggested that SP3, MT2, DT4 and HT2 casting conditions presented the optimal process parameter level that showed the maximum hardness of 131 HV and the tensile strength of 329 MPa. Scanning electron microscopy and energy-dispersive X-ray spectroscopy showed uniform distribution of reinforcement in the encapsulation process compared with the regular feeding technique. ANN predicted the hardness and tensile strength with 95 % accuracy. Compared with the regression model and experimental data, the ANN prediction was more accurate. The defined hybrid metal matrix composite stands out as the substitute for AA6061 alloy that meets the demands of the modern automotive industry in engine cylinder liner applications.

Influence of squeeze cast process parameters on wear behavior of AA6061

The automobile and aeronautical sectors have simulated the emergence of processes that yield defect free components. Squeeze casting is one such novel process that is mostly adopted to produce light weight alloys of Aluminium and Magnesium. The influence of squeeze cast process parameters on wear behavior of AA6061 was investigated in this study by employing Taguchi method. The process parameters considered in this study were pressure, die temperature and pressure duration respectively. L9(34) orthogonal array of Taguchi method was chosen to conduct the experiments and the squeeze cast components were produced. The pins were made from these components and they were subjected to wear test. The wear rate was obtained from the weight loss of the pin. The analysis showed that the parameters such as pressure and die temperature caused appreciable improvement towards the wear resistance exhibited by AA6061 components.

Optimization of stir–squeeze casting parameters for production of metal matrix composites using a hybrid analytical hierarchy process–Taguchi-Grey approach

ABSTRACTA hybrid optimization approach using analytical hierarchy process (AHP) combined with Taguchi-Grey has been developed and tested for the first time to optimize the stir–squeeze casting process parameters in the production of aluminium metal matrix composites reinforced with alumina for automotive brake disc application. The AHP method was used for assigning a weight to the response variables, which was based on experts’ opinion on the importance of the response variables for brake disc application. For producing brake discs, the optimum process parameters are found to be a squeeze pressure of 100 MPa, squeeze time of 45 s, die preheating temperature of 250°C and stirrer speed of 525 rpm. Also, a confirmatory analysis was carried out to validate these optimum process parameters, and the results indicated enhanced compressive strength of 433 MPa (18.5% increase) and reduced porosity of 5.29% (13.5% decrease) in the composite.

Micro-structure and Air-tightness of Squeeze Casting Motor housing for New Energy Vehicle

In order to improve the performance of automobile parts, the influence of squeeze casting process parameters on casting defects, material structure and air-tightness of aluminum alloy motor housing for new energy vehicle was studied. The results show that the density of the castings increases with the increase in pressure and mold temperature. With increase in pouring temperature, it increases first and then decreases. Pressure has the greatest influence on the density of the castings. Under a certain pressure, with moderate increase in casting temperature and mold temperature, the grain growth begins to increase; the dendrites become less, the new α - Al grains are spherical and granular, the micro-structure is uniform. Also, with increase in pressure, this effect is more pronounced, the air-tightness of castings improve. In conclusion, when the pressure is 110MPa, pouring temperature is 680° C, mold temperature is 280° C, pressure holding for 30s, and punch speed of 0.1m/s, there is no clear shrinkage in the casting, the structure is uniform, the qualified rate of air-tightness of production reaches 86%, and the performance is excellent.

Optimization of Squeeze Casting Process Parameters Using Taguchi in LM13 Matrix B4C Reinforced Composites

Aluminium metal matrix composite has widely used in aerospace and automobile industry due to its high strength to weight ratio. Aluminium LM13 matrix B4C reinforced composites are mainly manufactured by Squeeze casting process. The present work optimizes squeeze casting process parameters using Taguchi method. The control factors used for the experiments were Squeeze Pressure, Die Preheat Temperature and weight percentage of Boron Carbide (B4C) along with multiple performance characteristic of Hardness, Ultimate Tensile Strength (UTS) and Yield Strength (YS). The L27 orthogonal array was used for experimental design. Analysis of variance (ANOVA) is used to determine the significant factor and found out that Squeeze pressure is the most significant factor followed by percentage of B4C.

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

Modelling and multi-objective optimisation of squeeze casting process using regression analysis and genetic algorithm

In the present work, an attempt has been made using statistical tools to develop a non-linear regression model and to identify the significant contribution of squeeze cast process parameters on surface roughness, hardness and tensile strength. Microstructure examination performed on the squeeze cast samples has revealed that a maximum of 100 MPa pressure is good enough to eliminate all possible casting defects. Accuracy of the developed models has been tested with the help of ten test cases. It is important to note that the developed models predict responses with a reasonably good accuracy and the developed mathematical input–output relationship helps the foundry-man to make better predictions. The present work comprises four objectives, which are conflicting in nature. Hence, mathematical formulation is used to convert four objective functions into a single objective function. The popular evolutionary algorithm, that is genetic algorithm has been utilised to determine the optimal process parameters.

Prediction of Secondary Dendrite Arm Spacing in Squeeze Casting Using Fuzzy Logic Based Approaches

Abstract The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.

Modelling in Squeeze Casting Process-Present State and Future Perspectives

The growing demand in today’s competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology.

Air Conditioning Compressor Front Cover of Squeeze Casting Process Design and Numerical Simulation

This paper is mainly on the research of squeeze casting production automobile air conditioner front cover FS170C, due to the development and optimization of the part in the production process, design and manufacture of low cost mould, the rules of structure and properties of castings is lack of corresponding research, seriously affected the development and market competition ability of the air conditioning compressor front cover. In order to solve the above problems, and to provide reference data and process scheme is necessary, this study to optimize the design of squeeze casting technology of the front cover of the air conditioner, finalized a most reasonable extrusion casting scheme, namely the gating system is more suitable and exhaust system design, and the squeeze casting process parameters, in this scheme next, carries on the mold design, production of squeeze casting of high quality.

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

Enhancing Wear Resistance of Squeeze Cast AC2A Aluminum Alloy

The effect of squeeze casting process parameters on wear behavior of AC2A aluminium alloy was primarily investigated in this experimental study. Five process parameters, namely squeeze pressure, pouring temperature, die temperature, die material and compression time, each at four levels were chosen and sixteen experimental runs based on L16 orthogonal array were performed. From analysis of variance (ANOVA) and F-test, it was observed that squeeze pressure, die temperature and compression time were the parameters making significant improvement in wear resistance. A mathematical model relating the effect of significant parameters with wear behavior was developed for the process using nonlinear regression analysis with the help of MINITAB software. Taguchi method, Microsoft XL Solver and MATLAB genetic algorithm were employed to optimize the process. The result show that parametric conditions obtained through the optimization tools exhibit about 20% enhancement in wear resistance compared to gravity casting condition.

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.

Tensile Strength of Squeeze Cast Carbon Fibers Reinforced Al-Si Matrix Composites

Squeeze casting is a pressure casting process in which molten metal is solidified under the direct action of a pressure. In squeeze casting, the relationship between the process parameters and the quality of the squeeze cast components is not fully understood; thus the need for more studies in this area of technology for better understanding of the process. The present work encompasses studying the effect of direct squeeze casting process parameters on the production of (3 and 20٪) volume fraction carbon fibers (CF) reinforced Al-Si matrix composites. The evaluated process parameters are squeeze pressure in the range (7.5-53) MPa, die preheating temperature (100,200,300)°C, pouring temperature (700,780)°C, squeeze time (30 sec.), and delay time (5 sec.). The results show a good distribution of the matrix between the carbon fibers when using higher casting pressures of (38 and 53MPa), lower pouring temperature of (700°C) and lower die temperatures of (100 and 200°C). Increasing the carbon fibers volume fraction had led to increasing the tensile strength. The using of higher pressure (53MPa), lower pouring temperature (700°C), and lower die temperature (200°C) have increased the ultimate tensile strength of the CF/Al-Si composites to (183MPa) when compared to that of the nonreinforced alloy which was (168MPa) because of the increased bonding, decreased shrinkage defects and fibers degradation based on the results. Also, UTS is increased at P=38MPa, Tp=700°C, and Td=100°C.