Investigation of a composite ring rolling process by FEM and experiment

Seitz, Joachim; Schwich, Gideon; Guenther, Stefan

doi:10.1051/matecconf/20168015011

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…Gheisari et al [7] conducted a 3D-FE analysis in hot forming conditions, obtaining an optimization guide of process parameters and quality control. Seitz et al [8] investigated the ring rolling process for realizing seamless composite rings to maximize the mechanical properties. Considering the optimization of final quality, Anjami et al [9] have demonstrated that tool size influences strain and temperature distribution, finding the optimal configuration and reducing the fishtail effect.…”

Section: /1mentioning

confidence: 99%

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Multivariable regression and gradient boosting algorithms for energy prediction in the radial-axial ring rolling (rarr) process

Mirandola¹,

Berti²,

Caracciolo³

et al. 2021

Esaform 2021

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Energy prediction and starvation have become an essential part of process planning for the XXI century manufacturing industry due to cost-saving policies and environmental regulations. To this aim, the research presented in this paper details how machine learning-based algorithms can be an effective way to predict and minimize the energy consumptions in the widely spread radial-axial ring rolling (RARR) process. To analyze this bulk metal forming process, 380 numerical simulations have been developed using the commercial SW Simufact Forming 15 and considering three largely utilized materials, the 42CrMo4 steel, the IN 718 superalloy, and the AA6082 aluminum alloy. To create the database for both multi-variable regression and machine learning models, ring outer diameters ranging from 650 mm to 2000 mm and various process conditions including different sets of tool speeds and initial temperatures have been considered. For the case of the multi-variable regression model, to account for all the cross-influences between all the parameters, a second-order function including 26 parameters has been developed, resulting in a reasonable average accuracy (94 %) but also in an impractical huge equation. On the other hand, the machine learning model based on the Gradient Boosting (GB) approach allows obtaining a similar accuracy (96 %) but its compact form allows a more practical utilization and its training can be expanded almost indefinitely, by adding more results from both numerical simulations and experiments. The proposed approach allows to quickly and precisely predict the energy consumption in the RARR process and can be extended to other manufacturing processes.

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Section: /1mentioning

confidence: 99%

“…Considering the optimization of final quality, Anjami et al [9] have demonstrated that tool size influences strain and temperature distribution, finding the optimal configuration and reducing the fishtail effect. ation of the ring rolling pr olling process [6,8,12]. ocess [6,8,12].…”

Section: /1mentioning

confidence: 99%