The sustainability of neural network applications within finite element analysis in sheet metal forming: A review

Jamli, Mohamad Ridzuan; Farid, Nurhidayat

doi:10.1016/j.measurement.2019.02.034

Cited by 33 publications

(21 citation statements)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Viswanathan et al (2003) predicted springback using material properties, thickness, and friction conditions as input parameters during steel channel forming. Jamli and Farid (2019) reviewed the reports on the application examples of ANN for the prediction of springback, and indicated that the existing approach using ANN cannot incorporate all the factors affecting the analysis results. Srivastava et al (2004) predicted the final forging load using the ram velocity, billet temperature, and friction coefficient.…”

Section: Application Of Machine Learning In Metal Formingmentioning

confidence: 99%

Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Gondo

Arai

2022

J Intell Manuf

View full text Add to dashboard Cite

In this study, an artificial neural network (ANN) model was constructed to investigate the relationship between the roller path parameters to form a cylindrical cup in multi-pass conventional spinning and the thickness distribution throughout the height of a workpiece. Furthermore, the path parameters that simultaneously realize multiple target values of the workpiece dimensions were calculated instantly by the iterative solution based on the constructed model. A systematic design of the path parameters for a constant thickness distribution was established as follows. First, the roller path was expressed using 12 parameters. Second, the workpieces were spun under various experimental conditions, which were determined by partial randomization of the orthogonal array based on the Taguchi method. Third, an ANN model was trained by considering seven path parameters as inputs and five forming result values as outputs (cup height, wall thickness at 25%, 50%, and 75% of the cup height, and residual path length). Finally, the path parameters required for realizing a constant thickness were determined using an ANN model with an iterative solution. Although several samples of the training dataset exhibited non-uniform thickness distributions, the workpieces that were spun under the parameters obtained via iteration exhibited a constant thickness distribution. The parameters responsible for stretching the material in the radial direction significantly affected the thickness distribution. The most influential parameter was the increment in the axial start position for each curved pass. Graphical abstract

show abstract

Section: Application Of Machine Learning In Metal Formingmentioning

confidence: 99%

Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Gondo

Arai

2022

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…Furthermore, neural networks have also been widely employed in several scientific and technological fields. Among the research studies found in the literature it is worth mentioning the application of neural networks for prediction of biogases concentration using spiking neural networks [45], feature recognition and process planning of casting dies [46], quality control in manufacturing processes [47], prediction of springback in sheet metal forming [48], aerodynamic data modeling [49], detection of skin diseases [50], automatic control of house elements [51,52] and energy forecasting in the manufacturing sector [53], among many other applications.…”

Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

View full text Add to dashboard Cite

In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.

show abstract

“…In addition to these two basic approaches, the use of so-called artificial neutral networks (ANN) in the material science has become more widespread in recent years, e.g., Pouraliakbar [35] developed an ANN model to predict the toughness of HSLA steel. Nowadays, in light of springback prediction, utilisation of ANN represents an alternative tool for springback prediction, especially regarding that in light of nonlinear recovery, FEM has become quite complicated to achieve reliable results as it is concluded, e.g., by [36]. However, it is still a developing procedure, so, e.g., Angsuseranee [37] compared the efficiency of springback and sidewall curl prediction of AHSS in the U-bending process by the FEM and ANN and it was found that FEM was more efficient than the ANN approach.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Computational Model and Mesh Strategy on the Springback Prediction of the Sandwich Material

2022

View full text Add to dashboard Cite

The effect of the computational model and mesh strategy on the springback prediction of the thin sandwich material made of micro-alloyed steel was investigated in this paper. To verify the chosen computational strategy, a comparison of the experimentally obtained specimen (U-bending) with the FEA result was performed. The Vegter yield criterion combined both with the isotropic and kinematic hardening law was used for the calculation. In addition, the effect of the deformation mesh element (surface and volume) on the accuracy of the springback prediction was investigated. It was concluded that the choice of the volume deformation mesh does not significantly improve the accuracy of the results. Moreover, it is quite a time-consuming approach. The much greater influence was monitored by concerning the selection of hardening law, where the anisotropic one was more suitable to be used on the springback prediction of a given sandwich material.

show abstract

The sustainability of neural network applications within finite element analysis in sheet metal forming: A review

Cited by 33 publications

References 118 publications

Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Effect of the Computational Model and Mesh Strategy on the Springback Prediction of the Sandwich Material

Contact Info

Product

Resources

About