Using artificial neural networks to model aluminium based sheet forming processes and tools details

Mekras, N. D.

doi:10.1088/1742-6596/896/1/012090

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…They also established that the use of all types of holes produces more accurate result for the prediction of springback: case errors are fewer than in the case of training each hole separately. Mekras [26] implemented an ANNs model for successful process set-up and used it in the scope of sheet metal forming theory. In the model, set-up parameters including aluminum alloy type, sheet thickness, pressing speed, and the tools' geometrical details were considered.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Najm

Paniti

2021

Int J Adv Manuf Technol

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Incremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Najm

Paniti

2021

Int J Adv Manuf Technol

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“…Thus, in this section, a new experiment was designed to have a total number of 21 examples of dataset, with loading stroke ranging from 11 to 31 mm with unit increment. The whole dataset was then split into a training set and test set, with data pairs with odd numbers of loading stroke (i.e., 11,13,15,17,19,21,23,25,27,29,31 training data were also designed to be structurally symmetric (includes stroke values of 11, 21 and 31 mm) and asymmetric (includes stroke values of 11, 13 and 31 mm) for four-point bending of AA6082 like in Section III-B. The models were also applied to applications of 1) four-point bending process with a new material of SS400 and 2) air bending process with the same material of AA6082 to comprehensively evaluate the performance and learning consistency of the data-driven DNNs and TG-DNN.…”

Section: Learning With Scarce Training Datamentioning

confidence: 99%

“…The proposed BPNN-Spline model was demonstrated to outperform the traditional ANN in predicting the bending angles at different punch displacements. Viswanathan et al [23] implemented a threelayer neural network to predict the stepped binder force trajectory at different punch displacement, thus realizing the control of springback in a plane strain channel forming process. Apart from the prediction of springback or forming parameters, shallow learning has also been used to substitute or reinforce the constitutive model for metallic material [24]- [26].…”

mentioning

confidence: 99%

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Liu

Xia

Shi

et al. 2021

IEEE/CAA J. Autom. Sinica

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Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift's law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce and scattered experiment data are available for training which is often the case in practice. The theory-guided DNN could also be applicable to other sheet metal forming processes. It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.

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“…[18]), including artificial neural networks (ANNs) (e.g. [26,34]). In the particular case of forming processes, researchers are also trying to explore the large amount of data generated (both experimental and numerical) while designing new products, to guide the process design from its early stage with the help of ANN meta-models to predict product feasibility (e.g.…”

Section: 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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Using artificial neural networks to model aluminium based sheet forming processes and tools details

Cited by 7 publications

References 5 publications

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

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

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