Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

Filippis, Luigi Alberto Ciro De; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

doi:10.3390/ma9110915

Cited by 41 publications

(26 citation statements)

References 25 publications

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“…It is well known that welding processes have multiple responses. The relationship between the processing parameters is non-linear, thus mutli-object optimization techniques are necessary to be utilized [16,17]. In order to optimize a process with multiple responses, various multi-objective optimization techniques based on statistical and intelligent models provide good results.…”

Section: Introductionmentioning

confidence: 99%

“…In order to optimize a process with multiple responses, various multi-objective optimization techniques based on statistical and intelligent models provide good results. The model based on artificial neural networks could help to identify the relation between process parameters and quality of weld [16]. Naghibi et al [17] used the neural network and genetic algorithm based model to realize the optimization of tensile properties of AA 5052 to AISI 304 dissimilar FSW joints.…”

Section: Introductionmentioning

confidence: 99%

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Microstructure and Mechanical Properties of Al/Steel Friction Stir Lap Weld

Wan

Huang

2017

Metals

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Abstract:The friction stir welding tool with convex pin tip was designed to realize the lap joining of 6082-T6 aluminum alloy and Q235A steel. With decreasing welding speed and increasing rotation speed, the basic constitutions of mixed stir zone changed from α-Fe fine grains, thin intermetallic compound (IMC) and Al/Fe composite structure to hook-like and chaotic mixed layered structure, resulting in joint deterioration. The maximum shear load can reach 7500 N and is predominately affected by the morphology of the IMC layers, which in turn depend on rotation speed, welding speed and other parameters. Nano-hardness decreases from about 3.9 GPa in the upper steel surface layer to about 1.3 GPa in the steel base material. Microhardness profile reveals that the maximum hardness occurs at the interface zone. The morphology of layered structure, FeAl 3 IMC thickness and steel grain size can be controlled by choosing suitable welding parameters and tool shape.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microstructure and Mechanical Properties of Al/Steel Friction Stir Lap Weld

Wan

Huang

2017

Metals

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“…Among the artificial neural networks applications to the production processes, this section describes the research of De Filippis et al [74] in which a simulation model was developed for the monitoring, controlling and optimization of a particular solid-state welding process called friction stir welding (FSW). The approach based on the use of neural networks, using the FSW technique, has allowed identifying the relationships between the process parameters (input variable) and the mechanical properties (output responses) of the AA5754 H111 welded joints.…”

Section: Case Study: "Prediction Of the Vickers Microhardness And Ultmentioning

confidence: 99%

ANN Modelling to Optimize Manufacturing Process

Filippis¹,

Serio²,

Facchini³

et al. 2018

Advanced Applications for Artificial Neural Networks

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Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted.

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“…The higher the weights, the higher the impact of the input node. It is used for modeling on prediction or estimation of strength of capacity of structures [23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Artificial Neural Network In Structural Engineering and Matementioning

confidence: 99%

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

2018

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An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch-catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson's Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content.

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Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

Cited by 41 publications

References 25 publications

Microstructure and Mechanical Properties of Al/Steel Friction Stir Lap Weld

Microstructure and Mechanical Properties of Al/Steel Friction Stir Lap Weld

ANN Modelling to Optimize Manufacturing Process

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

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