Integration of feedforward neural network and finite element in the draw-bend springback prediction

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elasticallydriven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data

Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics

Recent advances and applications of machine learning in metal forming processes

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The Sustainability Of Neural Network Applications Within Finite Element Analysis In Sheet Metal Forming: A Review

The prediction of springback in sheet metal is vital to ensure economical metal forming. The latest nonlinear recovery in finite element analysis is used to achieve accurate results, but this method has become more complicated and requires complex computational programming to develop a constitutive model. Having the potential to assist the complexity, computational intelligence approach is often regarded as a statistical method that does not contribute to the development of a constitutive model. To provide a reference for researchers who are studying the potential application of computational intelligence in springback research, a review of studies into the development of sheet metal forming and the application of neural network to predict springback is presented in this research paper. It can be summarized as: (1) Springback is influenced by various factors that are involved in the sheet metal forming process. (2) The main complexity in FE analysis is the development of a constitutive model of a material that has the potential to be solved by using the computational intelligence approach. (3) The existing neural network approach for solving springback predictions is unable to represent all the factors that affect the results ofthe analysi

A Methodology for Data-Informed Process Control in Progressive Die Sheet Metal Forming

This thesis investigates the coupled relationship between the strip transfer and forming operations in progressive die sheet metal forming, including the effects of the strip layout geometry, and its effect on the process speed and accuracy. Servo-actuated strip lifters and feeder are considered to assist in minimizing the dynamic response of the strip during the transfer process. A methodology is proposed for identifying suitable trajectories to prescribe the motion of active strip lifters and feeder to obtain consistent part quality without risk of process failures for a progressive die operation. Multiple iterations of a finite element (FE) model were constructed in LS-DYNA to simulate a progressive die operation. Various FE analysis techniques were used to reduce the computational cost of the simulations to allow for enough data to be generated for machine learning applications. Both explicit and implicit time-integration schemes were considered in iterations of the FE model. Both single and dual carrier strip layouts were considered. The results of the FE simulations suggest that the single carrier strip layouts produce larger predicted dynamic displacements and rotations of the work-piece as compared to the dual carrier strip layouts during strip transfer. Furthermore, the single carrier strip layout is shown to be susceptible to strip misalignment. The final version of the FE model utilized geometry based on a demonstrator tool being deployed at the Technische Universität München. A total of 1000 simulations were generated, 500 each for the ‘I’ and ‘O’ stretch-web types using a single carrier strip layout. Each simulation considered a unique permutation of control inputs sampled from the set of possible strokes rates and trajectories for the lifters and feeder. Cubic splines were used to generate the trajectories for the strip lifter and feeder by varying the position of two knots used to define the shape of the spline. The results from the 1000 simulations indicate that in general the ‘S’ stretch-web produces a larger variance in the predicted dynamic response and ‘work-piece placement as compared to the ‘I’ stretchweb. Furthermore, the stroke rate and lifter trajectory were shown to have a large influence on the overshooting of the work-pieces during strip transfer and the probability of whether tooling collisions occurred. Multiple machine learning models were trained on the data generated by the final FE model. Two types of classifiers were constructed using neural network and XGBoost architectures. The first classifier predicts whether the clearance between the strip and binder are within a specified tolerance (to prevent collision with the tooling) during strip transfer. The second classifier predicts whether the placement accuracy of the work-piece on the forming die after strip transfer is within a specified tolerance. A range of tolerances were considered when labeling the data for both classifiers. Nestedcross fold validation was used to select the hyperparameter tuning and model selection. The machine learning classifiers were used to test all possible control inputs using a ‘minimum feed clearance’ of 10 mm and a maximum ‘work-piece placement error of 0.11 mm. The maximum stroke rate at which a given pair of lifter and feeder trajectories can operate was identified for all permutations. Five permutations that achieved the highest predicted stroke rate were simulated for an additional five strokes. The classifiers showed a reasonable ability to predict the ‘minimum feed clearance’ and ‘workpiece placement in the extended FE simulations for the selected trajectories, but, was unable to account for the strip misalignment which occurred after several strokes in all simulations. This research successfully demonstrates a methodology for using machine learning models trained on FE simulations to predict process outcomes of a progressive die operation with variable feeder and lifter trajectories. The FE simulations used to train the machine learning models were generated by adopting computationally-effective FE modelling techniques in a single press stroke model. The machine learning models were shown to reasonably predict the process outcomes of novel input permutations in a multi-stroke FE simulation. One of the largest constraints in this research is the FE simulation time which limited the model complexity that could be considered in the training set generation. Furthermore, the demonstration of the machine learning predictions for a multi-stroke process was limited due to the susceptibility of the single carrier strip layout to misalign after strip progression. Future work should consider the use of dual carrier strip layouts for the generation of the training data. Alternative approaches may also be considered, such as a machine learning framework for directly predicting the forward dynamics of the progressive die operation or a co-simulation approach in which a robust controller interacts directly with the FE simulation

Avaliação do retorno elástico no processo de dobramento empregando aço SAE 1006

Este trabalho tem como foco principal avaliar o retorno elástico utilizando a técnica do Processo de Dobramento em L com ressalto e sem no punção, através de uma matriz para realizar o dobramento do aço SAE 1006. Realizou-se a caracterização do material como: ensaios de tração, metalografia e análise química do material para embasamento para comparar resultados com a literatura e utilizar as informações para cálculos analíticos e simulação numérica. Nos ensaios de dobramento foram possíveis identificar as deformações do lado externo das fibras dos corpos de prova com 1,5 mm de espessura, tal como o retorno elástico nos sentidos de laminação de 0° e 90°. No processo de dobramento foram utilizados dois modelos de punção, sem e com ressalto de 0,25 mm de altura e 1,2 mm de largura, para averiguar o fator do retorno elástico (K), as deformações verdadeiras () e relativas () que ocorreram no lado externo das fibras, para realizar uma avaliação comparativa dos valores teóricos. Os resultados experimentais das deformações verdadeiras () / relativas () com o punção sem ressalto foram 0,18[-]/20% para o sentido da laminação 0° e 0,26[-]/30% para o sentido da laminação 90°, já para o punção com ressalto foram 0,34[-]/40% para laminação a 0° e 0,38[-]/45% para laminação a 90°. O valor teórico da deformação relativa () resultou em uma variação máxima de 9% e a deformação verdadeira () em 0,7 [-] quando comparados aos resultados experimentais. Através dos dados obtidos, comprovou-se que a Linha Neutra (LN) da dobra da chapa gerou uma deformação assimétrica e constatou-se que as maiores deformações ocorreram no sentido de laminação a 90°. Os resultados obtidos experimentalmente do fator do retorno elástico (K) mostraram compatibilidade com os dados obtidos teoricamente, evidenciando que ao utilizar o método do punção com ressalto diminui o retorno elástico, demostrando que o maior valor do fator de retorno elástico ocorre no sentido de laminação a 90°. Além disso, foi realizada a simulação numérica através do software Simufact Forming®, utilizada para comparação da deformação verdadeira () e o fator no retorno elástico (K) contra os dados experimentais. Os resultados do comportamento da deformação visual na simulação numérica ficaram similares com a análise metalográfica, porém os resultados da deformação verdadeira () ficaram altos comparados aos dados dos ensaios, gerando um erro relativo médio de 40,5% para o punção sem ressalto e 21,5% para o punção com ressalto. Entretanto os resultados obtidos do fator do retorno elástico (K) ficaram próximos, apresentado um erro relativo médio de 0,96% para o punção sem ressalto e 0,25% com ressalto.The main focus of this work is to evaluate the springback using the technique of the LBending Process with step and without the punch, through a matrix to perform the bending of SAE 1006 steel. The characterization of the material was carried out as: tests tensile, metallography and chemical analysis of the basement material to compare results with the literature and use the information for analytical calculations and numerical simulation. In the bending tests, it was possible to identify the deformations on the external side of the fibers of the 1.5 mm thick specimens, such as the springback in the rolling directions of 0° and 90°. In the bending process, two punch models were used, without and with a 0.25 mm high and 1.2 mm wide step, to determine the springback factor (K), the true () and relative deformations () that occurred on the external side of the fibers, to carry out a comparative evaluation of the theoretical values. The experimental results of true ()/relative () strains with the punch without step were 0.18[-]/20% for the 0° rolling direction and 0.26[-]/30% for the rolling direction. rolling at 90°, for the punch with step it was 0.34[-]/40% for rolling at 0° and 0.38[-]/45% for rolling at 90°. The theoretical value of the relative strain (ε) resulted in a maximum variation of 9% and the true strain () in 0.7[-] when compared to the experimental results. Through the obtained data, it was verified that the Neutral Line (LN) of the sheet bend generated an asymmetrical deformation and it was verified that the greatest deformations occurred in the rolling direction at 90°. The results obtained experimentally of the springback factor (K) showed compatibility with the data obtained theoretically, showing that when using the punching method with step reduces springback, showing that the highest value of the springback factor occurs in the rolling direction at 90°. In addition, numerical simulation was performed using the Simufact Forming® software, used to compare the true strain () and the springback factor (K) against the experimental data. The results of the deformation behavior in the numerical simulation were similar with the metallographic analysis, however the results of the true deformation () were high compared to the test data, generating an average relative error of 40% for the punch without step and 21.5 % for the punch with step. However, the results obtained from the springback factor (K) were close, with an average relative error of 0.96% for the punch without step and 0.25% with step

A novel marine radar targets extraction approach based on sequential images and Bayesian Network

This research proposes a Bayesian Network-based methodology to extract moving vessels from a plethora of blips captured in frame-by-frame radar images. First, the inter-frame differences or graph characteristics of blips, such as velocity, direction, and shape, are quantified and selected as nodes to construct a Directed Acyclic Graph (DAG), which is used for reasoning the probability of a blip being a moving vessel. Particularly, an unequal-distance discretisation method is proposed to reduce the intervals of a blip’s characteristics for avoiding the combinatorial explosion problem. Then, the undetermined DAG structure and parameters are learned from manually verified data samples. Finally, based on the probabilities reasoned by the DAG, judgments on blips being moving vessels are determined by an appropriate threshold on a Receiver Operating Characteristic (ROC) curve. The unique strength of the proposed methodology includes laying the foundation of targets extraction on original radar images and verified records without making any unrealistic assumptions on objects' states. A real case study has been conducted to validate the effectiveness and accuracy of the proposed methodology

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations