Bending Angle Prediction Model Based on BPNN‐Spline in Air Bending Springback Process

Guo, Zhefeng; Tang, Wencheng

doi:10.1155/2017/7834621

Cited by 11 publications

(8 citation statements)

References 20 publications

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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%

“…To date, research on shallow learning dominates the development of machine learning in sheet metal forming [20]. For example, Narayanasamy et al [21] compared the performance of a multi linear regression model and a four-layer artificial neural network (ANN) in predicting the springback angle of an air bending process, from which the ANN exhibited higher prediction accuracy than the regression model. Guo et al [22] developed a combination of error back propagation neural network and spline function (BPNN-Spline) to predict the springback angle in a V-die bending process, in which the BPNN took sheet metal thickness, punch radius, die radius and a material indication parameter as inputs.…”

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confidence: 99%

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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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Section: Learning With Scarce Training Datamentioning

confidence: 99%

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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“…Dib et al (2020) proposed single and ensemble classifiers to predict the springback and maximum thinning of the U channel and the maximum equivalent plastic strain and maximum thinning of the square cup. Guo and Tang (2017) presented a springback bending angle prediction model, based on the combination of error backpropagation neural network and spline function (BPNN-Spline) to rapidly and accurately predict the springback bending angle in the V-die air bending process. It is evident that the machine learning approach is widely used in plate cold forming of different plate types and different machining parameters, providing good prediction results.…”

Section: Carried Outmentioning

confidence: 99%

Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

Zhao

et al. 2021

Mech. Sci.

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Abstract. Springback is an inevitable problem in the local bending process of hull plates, which leads to low processing efficiency and affects the assembly accuracy. Therefore, the prediction of the springback effect, as a result of the local bending of hull plates, bears great significance. This paper proposes a springback prediction model based on a backpropagation neural network (BPNN), considering geometric and process parameters. Genetic algorithm (GA) and improved particle swarm optimization (PSO) algorithms are used to improve the global search capability of BPNN, which tends to fall into local optimal solutions, in order to find the global optimal solution. The result shows that the proposed springback prediction model, based on the BPNN optimized by genetic algorithm, is faster and offers smaller prediction error on the springback due to local bending.

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“…This subsection provides an overview of the literature on ML applications in sheet metal forming. Table 1 shows a comparative outline of ML applications in the prediction of forming defects [9,13,15,20,22,25,28,30,32,39,41]), which is the focus of the current work. Additional applications include: (i) material parameters' identification (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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Bending Angle Prediction Model Based on BPNN‐Spline in Air Bending Springback Process

Cited by 11 publications

References 20 publications

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

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

Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

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

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