Application of artificial intelligence techniques in incremental forming: a state-of-the-art review

Nagargoje, Aniket; Kankar, Pavan Kumar; Jain, Prashant K.; Tandon, Puneet

doi:10.1007/s10845-021-01868-y

Cited by 11 publications

(6 citation statements)

References 100 publications

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“…In ISF, researchers used ML algorithms for predicting various aspects of the forming process such as the forming accuracy [11]. Although, Nagargoje et al [3] and Harfoush et al [12] independently of each other concluded that more process data is a necessity for increasing the prediction accuracy of the ML approaches, while reviewing the state-of-the-art. Most publications used only a handful of geometries with slight variations for establishing a process database because of the experimental expenditure.…”

Section: Machine Learning In Isfmentioning

confidence: 99%

“…The main cause for the low simulation accuracy are the various process parameters and their complex nonlinear relationships with each other [2]. Recently, researchers are focusing on the application of machine learning (ML) techniques for predicting several process specific dimensions [3]. These include forming accuracy [4], surface quality [5], tool load [6], forming temperature [7], the pillow effect [8] and the material flow curve [9].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Möllensiep,

Detering,

Kulessa

et al. 2024

Preprint

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While incremental sheet metal forming offers the potential for producing sheet metal parts in small lot sizes, the relatively low forming accuracy prevents a widespread industrial use. For improving the forming accuracy, research institutes are using machine learning techniques to predict the geometric accuracy and modify the toolpath based on the prediction. A critical challenge is it to ensure the generalisability of the prediction model as only a small amount of process data is available to train the model due to the lack of industrial collaborations. This publication presents a highly transferable feature engineering approach where surface representations of the part's geometry around each toolpath point are transferred into a standardized coordinate system. Several artificial neural networks were trained and used for predicting the forming accuracy and modifying the toolpath. During the validation experiments, the forming errors of parts which were independent of the training process were reduced by up to 68.5 %. The framework for computing the surface representations alongside with several pre-trained artificial neural networks is publicity available for download.

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Section: Machine Learning In Isfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Möllensiep,

Detering,

Kulessa

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Hartmann et al (2019) discussed the possibility of predicting the shape of metal sheet formed by incremental forming using multinodes ANN model. Nagargoje et al (2021) reviewed artificial intelligence techniques applied to incremental forming. Mandal et al (2007) estimated the average crystal grain size using the strain, strain rate, and temperature during dynamic recrystallization.…”

Section: Application Of Machine Learning In Metal Formingmentioning

confidence: 99%

Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Gondo

Arai

2022

J Intell Manuf

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In this study, an artificial neural network (ANN) model was constructed to investigate the relationship between the roller path parameters to form a cylindrical cup in multi-pass conventional spinning and the thickness distribution throughout the height of a workpiece. Furthermore, the path parameters that simultaneously realize multiple target values of the workpiece dimensions were calculated instantly by the iterative solution based on the constructed model. A systematic design of the path parameters for a constant thickness distribution was established as follows. First, the roller path was expressed using 12 parameters. Second, the workpieces were spun under various experimental conditions, which were determined by partial randomization of the orthogonal array based on the Taguchi method. Third, an ANN model was trained by considering seven path parameters as inputs and five forming result values as outputs (cup height, wall thickness at 25%, 50%, and 75% of the cup height, and residual path length). Finally, the path parameters required for realizing a constant thickness were determined using an ANN model with an iterative solution. Although several samples of the training dataset exhibited non-uniform thickness distributions, the workpieces that were spun under the parameters obtained via iteration exhibited a constant thickness distribution. The parameters responsible for stretching the material in the radial direction significantly affected the thickness distribution. The most influential parameter was the increment in the axial start position for each curved pass. Graphical abstract

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“…Artificial neural networks, support vector regression, decision trees, fuzzy logic, evolutionary algorithms, and particle swarm optimization solve IF-related issues. Hybrid approaches integrate some of the previous strategies (Nagargoje et al, 2021). Different intelligences with or without controlled manufacturing have been generated or developed as predictive models in end-milling machining, high-speed machining, and powder metallurgy (Amirjan et al, 2013;Ezugwu et al, 2005;Zain et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

Najm

Paniti

2022

J Intell Manuf

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Today the topic of incremental sheet forming (ISF) is one of the most active areas of sheet metal forming research. ISF can be an essential alternative to conventional sheet forming for prototypes or non-mass products. Single point incremental forming (SPIF) is one of the most innovative and widely used fields in ISF with the potential to form sheet products. The formed components by SPIF lack geometric accuracy, which is one of the obstacles that prevents SPIF from being adopted as a sheet forming process in the industry. Pillow effect and wall displacement are influential contributors to manufacturing defects. Thus, optimal process parameters should be selected to produce a SPIF component with sufficient quality and without defects. In this context, this study presents an insight into the effects of the different materials and shapes of forming tools, tool head diameters, tool corner radiuses, and tool surface roughness (Ra and Rz). The studied factors include the pillow effect and wall diameter of SPIF components of AlMn1Mg1 aluminum alloy blank sheets. In order to produce a well-established study of process parameters, in the scope of this paper different modeling tools were used to predict the outcomes of the process. For that purpose, actual data collected from 108 experimentally formed parts under different process conditions of SPIF were used. Neuron by Neuron (NBN), Gradient Boosting Regression (GBR), CatBoost, and two different structures of Multilayer Perceptron were used and analyzed for studying the effect of parameters on the factors under scrutiny. Different validation metrics were adopted to determine the quality of each model and to predict the impact of the pillow effect and wall diameter. For the calculation of the pillow effect and wall diameter, two equations were developed based on the research parameters. As opposed to the experimental approach, analytical equations help researchers to estimate results values relatively speedily and in a feasible way. Different partitioning weight methods have been used to determine the relative importance (RI) and individual feature importance of SPIF parameters for the expected pillow effect and wall diameter. A close relationship has been identified to exist between the actual and predicted results. For the first time in the field of incremental forming study, through the construction of Catboost models, SHapley Additive exPlanations (SHAP) was used to ascertain the impact of individual parameters on pillow effect and wall diameter predictions. CatBoost was able to predict the wall diameter with R2 values between the range of 0.9714 and 0.8947 in the case of the training and testing dataset, and between the range of 0.6062 and 0.6406 when predicting pillow effect. It was discovered that, depending on different validation metrics, the Levenberg–Marquardt training algorithm performed the most effectively in predicting the wall diameter and pillow effect with R2 values in the range of 0.9645 and 0.9082 for wall diameter and in the range of 0.7506 and 0.7129 in the case of the pillow effect. NBN has no results worthy of mentioning, and GBR yields good prediction only of the wall diameter.

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Application of artificial intelligence techniques in incremental forming: a state-of-the-art review

Cited by 11 publications

References 100 publications

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning

Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

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