Experimental analysis of tool geometry and tool rotation in SPIF process on AA7075-O alloy using ML and ANN approach

Kumar, Parveen; Singh, Hari

doi:10.1007/s12008-023-01535-x

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…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]. Due to the lack of industrial ISF production lines, the data used for training the ML models has to be gathered by the research institutes themselves.…”

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

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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: 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

show abstract

Cluster analysis for systematic database extension to improve machine learning performance in double-sided incremental sheet forming

Möllensiep,

Schäfer,

Pasch

et al. 2024

Int J Adv Manuf Technol

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Incremental sheet forming is a process for the production of sheet metal parts in small batch sizes. Due to the relatively low geometrical accuracy and the lack of precise and fast finite element analysis simulations of the process, industrial use cases are rare. Recently, a vast amount of scientific approaches simulated the process by utilizing machine learning techniques. Their success is limited by the quantity and quality of the used process data. Research institutes are struggling to gather enough data without industrial cooperations. For maximizing the distribution of process data in an experimental series and therefore their applicability for machine learning, the authors present a novel cluster analysis approach to systematically extend an existing database. The whole established process database consisting of 70 forming experiments and their toolpaths and digitizations is published to be used as a foundation for similar research.

show abstract

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

Möllensiep,

Detering,

Kulessa

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

While incremental sheet metal forming offers the potential for producing sheet metal parts in small lot sizes, the relatively low forming accuracy prevents 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 generalizability 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.

show abstract

Experimental analysis of tool geometry and tool rotation in SPIF process on AA7075-O alloy using ML and ANN approach

Cited by 5 publications

References 36 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

Cluster analysis for systematic database extension to improve machine learning performance in double-sided incremental sheet forming

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

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