scholarly journals Experimental Study and Response Surface Methodology for Investigation of FSP Process

2014 ◽  
Vol 61 (4) ◽  
pp. 539-552
Author(s):  
Marek Stanisław Weglowski
Keyword(s):  
Experimental Study ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Friction Stir Processing ◽  
Rotational Speed ◽  
Parent Material ◽  
Quadratic Model ◽  
Friction Stir ◽  
Microstructure Refining ◽  
Quadratic Models

Abstract The article presents the effect of rotational and travelling speed and down force on the spindle torque acting on the tool in Friction Stir Processing (FSP) process. The response surface methodology (RSM) was applied to find a dependence combining the spindle torque acting on the tool with the rotational speed, travelling speed and the down force. The linear and quadratic models with interaction between parameters were used. A better fitting was achieved for a quadratic model. The studies have shown that the increase in rotational speed causes a decrease in the torque while the increase in travelling speed and down force causes an increase in the torque. The tests were conducted on casting aluminium alloy AlSi9Mg. Metallography examination has revealed that the application of FSP process results in a decrease in the porosity in the modified material and microstructure refining in the stir zone. The segregation of Si and Fe elements was evident in the parent material, while in the friction stir processed area this distribution was significantly uniform.

An Experimental Study on the Friction Stir Processing Process of Aluminium Alloy

2013 ◽  
Vol 554-557 ◽  
pp. 1787-1792 ◽  
Author(s):  
Marek Stanislaw Węglowski
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Artificial Neural Networks ◽  
Aluminium Alloy ◽  
Friction Stir Processing ◽  
Rotational Speed ◽  
Friction Stir ◽  
Measurement Head ◽  
Microstructure Refining ◽  
The Relationship

The effect of rotational and travelling speeds and down force on the torque in Friction Stir Processing (FSP) process are presented. To find a dependence combining the spindle torque acting on the tool with the rotational speed, travelling speed and the down force, the artificial neural networks have been applied. Studies have shown that the increase in the rotational speed causes decrease in the torque while the increase in the travelling speed and down force causes the increase in the torque at the same time. The relationship between parameters of the process and the temperature of the tool, based on measurement head TermSTIR, were presented. Tests were conducted on casting aluminium alloy AlSi9Mg. Application of FSP process resulted in a decrease in the porosity in the modified material and microstructure refining

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

2021 ◽  
pp. 095440892110368
Author(s):  
Ravi Butola ◽  
Ranganath M. Singari ◽  
Qasim Murtaza ◽  
Lakshay Tyagi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Rotational Speed ◽  
Total Elongation ◽  
Tool Rotational Speed ◽  
Friction Stir ◽  
Artificial Neural

In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L16 design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B4C reinforcement particles are well distributed also; with increasing tool rotational speed grain size decreases up to 1200 r/min; on further increasing the tool rotational speed particles starts clustering.

Estimation of mechanical properties of friction stir processed Al 6061/Al2O3-Tib2 hybrid metal matrix composite layer via artificial neural network and response surface methodology

2021 ◽  
pp. 146442072110345
Author(s):  
Vahid M Khojastehnezhad ◽  
Hamed H Pourasl ◽  
Arian Bahrami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Friction Stir Processing ◽  
Wear Behavior ◽  
Friction Stir ◽  
Al 6061 ◽  
Artificial Neural

Friction stir processing is one of the solid-state processes which can be used to modify the structure and properties of alloys. In addition, it has become one of the most promising techniques for the preparation of the surface layer composites. To pursue cost savings and a time-efficient design, the mathematical model and optimization of the process can represent a valid choice for engineers. Friction stir processing was employed to generate an Al 6061/Al2O3-TiB2 hybrid composite layer, and mechanical properties such as the hardness and wear behavior were also measured. The relationship between the hardness and wear behavior, process parameters of friction stir processing were evaluated using an artificial neural network and response surface methodology. The rotational speed (1500–1800 rpm), traverse speeds (25, 50, 100 mm/min), and the number of passes (1–4) with constant axial force (2.61 kN) were used as the input, while the hardness and weight loss values were the output. Experimentally, the results showed that the process parameters have significant effect on hardness and wear behavior of Al 6061/Al2O3-TiB2. In addition, the developed artificial neural network and response surface methodology models can be employed as alternative methods to compute the hardness and weight loss for given process parameters. The results of both models showed that the estimated values for the hardness and wear behavior of the processed zone had an error less than 0.60%, which indicated reliability, and an evaluation of the estimated values of both models and the experimental values confirmed that the artificial neural network is a better model than response surface methodology.

Micro-hardness prediction of friction stir processed magnesium alloy via response surface methodology

2017 ◽  
Vol 13 (3) ◽  
pp. 377-390 ◽  
Author(s):  
Ahmed Naser ◽  
Basil Darras
Keyword(s):  
Response Surface Methodology ◽  
Magnesium Alloy ◽  
Response Surface ◽  
Process Parameters ◽  
Rotational Speed ◽  
Micro Hardness ◽  
Content Type ◽  
Friction Stir ◽  
Effective Use ◽  
Hardness Values

Purpose The purpose of this paper is to present a model to predict the micro-hardness of friction stir processed (FSPed) AZ31B magnesium alloy using response surface methodology (RSM). Another objective is to identify process parameters and through-thickness position which will give higher micro-hardness values. Moreover, the study aims at defining the factor that exhibits the most effect on the micro-hardness. Friction stir processing (FSP) machine can then be fed with the optimized parameters to achieve desirable properties. Design/methodology/approach An experimental setup was designed to conduct FSP. Several AZ31B magnesium samples were FSPed at different combinations of rotational and translational speeds. The micro-hardness of all the combinations of process parameters was measured at different through-thickness positions. This was followed by an investigation of the three factors on the resulting micro-hardness. RSM was then used to develop a model with three factors and three levels to predict the micro-hardness of FSPed AZ31 magnesium alloy within the covered range. The analyses of variance in addition to experimental verification were both used to validate the model. This was followed by an optimization of the response. Findings The model showed excellent capability of predicting the micro-hardness values as well as the optimum values of the three factors that would result in better micro-hardness. The model was able to capture the effects of rotational speed, translational speed, and through-thickness position. Results suggest that micro-hardness values were mostly sensitive to changes in tool rotational speed. Originality/value FSP is considered to be one of the advanced microstructural modification techniques which is capable of enhancing the mechanical properties of light-weight alloys. However, the lack of accurate models which are capable of predicting the resulted properties from process parameters hinders the widespread utilization of this technique. At the same time, RSM is considered as a vital branch of experimental design due to its ability to develop new processes and optimize their performance. Hence, the developed model is very beneficial and is meant to save time and experimental effort toward effective use of FSP to get the desired/optimum micro-hardness distribution.

scholarly journals Optimization of process parameters of friction stir welded AA 5083-O aluminum alloy using Response Surface Methodology

2015 ◽  
Vol 63 (4) ◽  
pp. 851-855 ◽  
Author(s):  
S. Jannet ◽  
P. Koshy Mathews ◽  
R. Raja
Keyword(s):  
Tensile Strength ◽  
Response Surface Methodology ◽  
Ultimate Tensile Strength ◽  
Response Surface ◽  
Process Parameters ◽  
Welding Speed ◽  
Rotational Speed ◽  
Strength Analysis ◽  
Aluminum Compound ◽  
Friction Stir

Abstract A methodology was exhibited to create the experimental model for assessing the Ultimate Tensile Strength of AA 5083-O aluminum compound which is broadly utilized as a part of boat building industry by Friction Stir Welding (FSW). FSW process parameters, such as: tool rotational speed, welding speed, and axial force were optimized for better results. FSW was completed considering three-component 3-level Box Behnekn Design. Response surface Methodology (RSM) was implemented to obtain the relationship between the FSW process parameters and ultimate Tensile Strength. Analysis of Variance (ANOVA) procedure was utilized to check the aptness of the created model. The FSW process parameters were additionally streamlined utilizing Response Surface Methodology (RSM) to augment tensile strength. The joint welded at a rotational speed of 1100 rpm, a welding speed of 75 mm/min and a pivotal energy of 2.5 t displays higher tensile strength compared with different joints in comparison with other joints.

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