Trade-off among mechanical properties and energy consumption in multi-pass friction stir processing of Al7075 alloy employing neural network–based genetic optimization

2016 ◽  
Vol 231 (1) ◽  
pp. 129-139 ◽  
Author(s):  
G Hussain ◽  
M Ranjbar ◽  
S Hassanzadeh
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Tensile Strength ◽  
Friction Stir Processing ◽  
Electrical Energy ◽  
Processing Technique ◽  
Genetic Optimization ◽  
Trade Off ◽  
Friction Stir ◽  
Artificial Neural

Friction stir processing is a novel material processing technique. In this study, neural network–based genetic optimization is applied to optimize the process performance in terms of post-friction stir processing mechanical properties of Al7075 alloy and the energy cost. At first, the experimental data regarding the properties (i.e. elongation, tensile strength and hardness) and the consumed electrical energy are obtained by conducting tests varying two process parameters, namely, feed rate and spindle speed. Then, a numerical model making use of empirical data and artificial neural networks is developed, and multiobjective multivariable genetic optimization is applied to find a trade-off among the performance measures of friction stir processing. For this purpose, the properties like elongation, tensile strength and hardness are maximized and the cost of consumed electrical energy is minimized. Finally, the optimization results are verified by conducting experiments. It is concluded that artificial neural network together with genetic algorithm can be successfully employed to optimize the performance of friction stir processing.

scholarly journals Fabrication of AA7075 Hybrid Green Metal Matrix Composites by Friction Stir Processing Technique

2020 ◽  
Vol 44 (4) ◽  
pp. 295-300
Author(s):  
Sanjay Kumar ◽  
Ashish Kumar Srivastava ◽  
Rakesh Kumar Singh
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Friction Stir Processing ◽  
Research Work ◽  
Processing Technique ◽  
Matrix Composites ◽  
Avant Garde ◽  
Friction Stir ◽  
Surface Composites ◽  
Tool Tilt Angle

Friction stir processing is an avant-garde technique of producing new surface composite or changing the different properties of a material through intense, solid-state localized material plastic deformation. This change in properties depends upon the deformation formed by inserting a non-consumable revolving tool into the workpiece and travels laterally through the workpiece. This research work highlights the effect of process parameters on mechanical properties of fabricated surface composites by friction stir processing. By using various reinforcing materials like Ti, SiC, B4C, Al2O3 with waste elements like waste eggshells, rice husks, coconut shell and coir will be used to fabricate the green composites which are environmentally friendly and reduces the problem of decomposition. The parameter for this experiment is considered as the reinforcing materials, tool rotation speed and tool tilt angle. The SiC/Al2O3/Ti along with eggshell are selected asreinforcement materials. The main effect of the reinforcement is to improve mechanical properties, like hardness, impact strength and strength. The results revealed that the process parameters significantly affect the mechanical properties of friction stir processed surface composites.

scholarly journals Friction Stir Processing Of TIG And Friction Stir Welded Dissimilar Alloy Joints: A Review

2019 ◽  
Author(s):  
Sipokazi Mabuwa ◽  
Velaphi Msomi
Keyword(s):  
Mechanical Properties ◽  
Friction Stir Welding ◽  
Friction Stir Processing ◽  
Aluminium Alloys ◽  
Food Packaging ◽  
Processing Technique ◽  
Tig Welding ◽  
Automotive Electronics ◽  
Friction Stir ◽  
Dissimilar Alloys

The use of aluminium alloys continues to grow in many applications to mention a few aerospace, automotive, electronics, electricity, construction and food packaging. With so much demand there is a new interest in welding of dissimilar aluminium alloys. Some of the welding techniques used to join dissimilar aluminium alloys include friction stir welding and TIG welding. The welding of dissimilar alloys affects the mechanical properties negatively due to porosity and cracking during the welding. This then suggests that there should be a process which can be used to improve the dissimilar alloys mechanical properties post its production. Friction stir processing was found to be one of the mechanical techniques that could be used to improve the mechanical properties of the material. This paper reports on the literature on the friction stir welding, TIG welding and friction stir processing techniques published so far, with the aim to identify the gap in the use of friction stir process as a post processing technique of the weld joints.

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.

scholarly journals Effect of Multiple Passes on Microstructural and Mechanical Properties of Surface Composite Al 2024/SiC Produced by Friction Stir Processing

2020 ◽  
Vol 44 (6) ◽  
pp. 421-426
Author(s):  
Ashish Kumar Srivastava ◽  
Nagendra Kumar Maurya ◽  
Manish Maurya ◽  
Shashi Prakash Dwivedi ◽  
Ambuj Saxena
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Friction Stir Processing ◽  
Hardness Test ◽  
Mechanical Tests ◽  
Matrix Composites ◽  
Application Range ◽  
Friction Stir ◽  
Morphological Aspects ◽  
Processed Sample

The application range in defense, aerospace and automotive sectors have enabled aluminium metal matrix composites to emerge in different technological fields due to enhanced micro structural and mechanical characteristics. In the present study, friction stir processing is used to fabricate Al2024/SiC composite with one, two and three passes of the cylindrical tool. Optical microscopy and scanning electronic microscope (SEM) were used to validate the processed sample and to justify the morphological aspects. Energy dispersive spectroscopy (EDS) analysis has also performed to confirm the presence of SiC particles in the composite. It also includes the analysis of mechanical properties such as tensile strength, Rockwell hardness test and nanoindentation to characterize the prepared samples. Improvement in tensile strength with a maximum of 443 MPa, the hardness of 121 HRB and nanoindentation of the specimen was depicted through the mechanical tests.

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.

Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys

2016 ◽  
Vol 232 (4) ◽  
pp. 333-342 ◽  
Author(s):  
Saurabh Kumar Gupta ◽  
KN Pandey ◽  
Rajneesh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Tensile Strength ◽  
Aluminium Alloys ◽  
Multi Objective Optimization ◽  
Friction Stir ◽  
Multi Objective ◽  
Artificial Neural

The present research investigates the application of artificial intelligence tool for modelling and multi-objective optimization of friction stir welding parameters of dissimilar AA5083-O–AA6063-T6 aluminium alloys. The experiments have been conducted according to a well-designed L27 orthogonal array. The experimental results obtained from L27 experiments were used for developing artificial neural network-based mathematical models for tensile strength, microhardness and grain size. A hybrid approach consisting of artificial neural network and genetic algorithm has been used for multi-objective optimization. The developed artificial neural network-based models for tensile strength, microhardness and grain size have been found adequate and reliable with average percentage prediction errors of 0.053714, 0.182092 and 0.006283%, respectively. The confirmation results at optimum parameters showed considerable improvement in the performance of each response.

Sign in / Sign up

Export Citation Format

Share Document