Application of artificial neural network in predicting the wear rate of copper surface composites produced using friction stir processing

2020 ◽  
pp. 1-12 ◽  
Author(s):  
I. Dinaharan ◽  
R. Palanivel ◽  
N. Murugan ◽  
R. F. Laubscher
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wear Rate ◽  
Friction Stir Processing ◽  
Copper Surface ◽  
Friction Stir ◽  
Surface Composites ◽  
Artificial Neural

Predicting the wear rate of AA6082 aluminum surface composites produced by friction stir processing via artificial neural network

2019 ◽  
Vol 16 (2) ◽  
pp. 409-423
Author(s):  
Isaac Dinaharan ◽  
Ramaswamy Palanivel ◽  
Natarajan Murugan ◽  
Rudolf Frans Laubscher
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wear Rate ◽  
Friction Stir Processing ◽  
Aluminum Surface ◽  
Content Type ◽  
Friction Stir ◽  
Surface Composites ◽  
Input Parameters ◽  
Artificial Neural

Purpose Friction stir processing (FSP) as a solid-state process has the potential for the production of effective aluminum matrix composites (AMCs). In this investigation, various ceramic particles including B4C, TiC, SiC, Al2O3 and WC were incorporated as the dispersed phase within AA6082 aluminum alloy by FSP. The wear rate of the composite is then investigated experimentally by making use of a design of experiments technique where wear rate is evaluated as the output parameter. The input parameters considered include tool rotational speed, traverse speed, groove width and ceramic particle type. An artificial neural network (ANN) simulation was then used to describe the wear rate of the surface composites. The weights of the network were adjusted to minimize the mean squared error using a feed forward back propagation technique. The effect of the individual input parameters on wear rate was then inferred from the ANN models. Trends are presented and related to the associated microstructures observed. The TiC infused AMC displayed the lowest wear rate whereas the Al2O3 infused AMC displayed the highest, within the scope of the current investigation. The paper aims to discuss these issues. Design/methodology/approach The paper used ANN for the research study. Findings The finding of this paper is that the wear rate of AA6063 aluminum surface composites is influenced remarkably by FSP parameters. Originality/value Original work of authors.

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.

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.

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