Classification and selection of sheet forming processes with machine learning

Hamouche, E.; Loukaides, Evripides G.

doi:10.1080/0951192x.2018.1429668

Cited by 33 publications

(24 citation statements)

References 24 publications

(26 reference statements)

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“…Sheet metal forming includes simple processes, such as bending, stretch forming and spinning, and more complex processes, like roll forming and deep drawing [16]. Each type of process has its specifications and parameters, including the tools geometry.…”

Section: Sheet Metal Formingmentioning

confidence: 99%

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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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Section: Sheet Metal Formingmentioning

confidence: 99%

“…[40,45]). In the context of early design stages, neural network classifiers have been applied in automating the sheet forming selection process, as an alternative to rulebased programmes [16]. Moreover, the robustness of the process design is questionable when neglecting the sources of scatter inherent to the process.…”

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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show abstract

“…However, apart from the aforementioned traditional methods for solving inverse problem, some deep learning methods increasingly attract researchers' attentions due to its great success in image classification [16], natural language processing (NLP) [17], object detection [18] , PDE solver [19], [20] and image restoration [21] et al One major advantage of deep learning based method is that it could learn the hidden relationship between high-dimensional data and nonlinear model, which is difficultly dug by the traditional methods. Therefore, some deep learning models have been applied in many traditional design areas such as mechanical design [22]- [27], optics [28], [29], fluid simulation [30], [31], biomedical science [32], [33] and materials [34]. For example, in the area of mechanical design, Sosnovik and Oseledets [22] constructed a convolutional encoder-decoder network to predict the optimal design from the intermediate design schemes obtained during evolution, which greatly accelerates the process of topological optimization (TO).…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [26] used the Feature Pyramid Network (FPN) model to learn the mapping from the heat source layout to temperature field as a surrogate to reduce the cost of optimization. The deep convolutional neural network was first utilized by Hamouche et al [27] to identify the manufacturing process that formed a part solely from the final geometry. In optics, Peurifoy et al [28] utilized artificial neural networks to approximate light scattering by multilayer nanoparticles, which could be used to solve nanophotonic inverse design problems.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Method for Heat Source Layout Inverse Design

Sun

Zhang²,

Zhang³

et al. 2020

IEEE Access

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Heat source layout design is an effective technique to enhance the thermal performance in the whole system, which has become a vital part in many engineering fields, e.g. satellite layout design and integrated circuit design. Traditionally, the optimal design is obtained by searching the design space with the optimization technique which repeatedly runs the thermal simulation to compare the performance of each scheme. Due to the extremely large computational burden with this method, the optimization is greatly limited. To overcome the challenge, heat source layout inverse design (HSLID) is proposed in this paper to directly generate the layout scheme with the given thermal performance requirement. A novel method for heat source layout inverse design, denoted as SAR-HSLID, is proposed based on the recently deep learning technique, Show Attend and Read (SAR) model. Firstly, regarding the mapping from the required temperature field to layout scheme as an image-to-location task, this paper introduces SAR model, which is good at sequence predicting, to generate the layout scheme. The trained SAR is capable of learning the underlying physics of the design problem, thus can efficiently predict the design under given requirement without any physical simulation. Secondly, to ensure that the designed heat source layout exactly satisfies the input temperature field requirement, based on the layout predicted by SAR, we further utilize a simple but efficient optimization process to conduct few post-processing. Finally, a heat source layout inverse design task in a typical two-dimensional heat conduction problem is investigated to demonstrate the feasibility and effectiveness of the proposed method.

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“…With deep learning, instead of designing hand-crafted features as the input, the workpiece geometry information is fed into the network to achieve end-to-end learning and reduce the potential bias introduced in the design of input representation. It was believed to outperform shallow neural network learning and traditional machine learning techniques, such as KNN algorithm, when handling AI-level tasks with high data dimensions and complex functions [33]. Consequently, researchers in sheet metal forming industry start resorting to deep neural network (DNN), which is an approximation model used in deep learning and consisting of relatively deep layers of neurons, for high-level learning or optimization.…”

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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Classification and selection of sheet forming processes with machine learning

Cited by 33 publications

References 24 publications

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

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

A Deep Learning-Based Method for Heat Source Layout Inverse Design

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

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