FEM-based neural network modeling of laser-assisted bending

Fetene, Besufekad N.; Shufen, Rajkumar; Dixit, Uday S.

doi:10.1007/s00521-016-2544-9

Cited by 33 publications

(17 citation statements)

References 24 publications

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“…In addition to that, having a deep network structure (i.e., many hidden layers) does not necessarily require any laborious feature selection and can work with raw data [29]. FE-based ANN models have been utilized for predicting stress distribution in a 3D printing process [30], bend angles in laser-guided bending [31], and performance of a thermoelectric generator [32], for example.…”

Section: Data-driven Surrogate Modelsmentioning

confidence: 99%

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

et al. 2020

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Section: Data-driven Surrogate Modelsmentioning

confidence: 99%

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

et al. 2020

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“…The optimization of the extreme values that can be found in the data set can be difficult. To facilitate optimization, minimize the impact of different dimensions, and achieve more effective results, all three input variables and the LDO were normalized using Equation (4) [63][64][65]. Different normalization formulas are also used in water quality modeling studies but there are no fixed rules as to which standardization approach should be used in particular circumstances [19,66].…”

Section: Model Development Applicationsmentioning

confidence: 99%

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Nacar

Bayram

BAKİ

et al. 2020

Water

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The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

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“…[1][2][3]6]); (ii) bend angles' prediction in laser forming processes (e.g. [7,10]); (iii) die roll height prediction in fine blanking (e.g. [43,48]); (iv) optimization of incremental sheet metal forming processes (e.g.…”

Section: Machine Learning Applications To Sheet Metal Formingmentioning

confidence: 99%

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Dib

Oliveira

Marques

et al. 2019

Neural Comput & Applic

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This paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

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FEM-based neural network modeling of laser-assisted bending

Cited by 33 publications

References 24 publications

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

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