Regression-based compensation of part inaccuracies in incremental sheet forming at elevated temperatures

Regression-based compensation of part inaccuracies in incremental sheet forming at elevated temperatures
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基于回归的高温增量板材成形零件误差补偿

DOI:
10.1007/s00170-020-05625-y
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发表时间:
1928
期刊:
The International Journal of Advanced Manufacturing Technology
影响因子:
--
通讯作者:
Kuhlenkötter
Kuhlenkötter
中科院分区:
--
文献类型:
--
作者:
Möllensiep;Kulessa;Thyssen;Kuhlenkötter

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板料渐进成形是一种适用于小批量的板料成形工艺,其原理是无模成形。它的一种工艺变体包括对板材进行局部加热,以抵消一些工艺限制(可成形材料、成形力、可实现的变形)。尽管高温成形具有许多优点,但由于局部加热和冷却引起的收缩效应,成形零件的几何精度仍然较低。这本出版物提出了一种数据驱动的方法,其中收集过程数据并在回归学习中使用,以预测收缩效应产生的几何精度。为了成功地应用回归学习,需要覆盖广泛可能的过程状态的大量过程数据。为此,设计并实施了由54个单独的成形实验组成的特定实验系列。基于成形零件的三维数字化,建立了包含408,296条记录的工艺数据库,每条记录代表一个刀具轨迹点。该过程数据库被用来训练19个不同的回归模型。研究了它们对几何偏差预测能力的表现。提出了一种通过对刀具轨迹进行基于预测的修改来提高几何精度的补偿方法。验证实验表明,该方法提高了成形零件的几何精度,具有较好的通用性。
Incremental sheet forming is a sheet forming process for small lot sizes due to its dieless principle. One of its process variants includes local heating of the sheet to counteract some of the process restrictions (formable materials, forming forces, achievable deformations). Although forming at elevated temperatures provides various advantages, the geometric accuracy of the formed part remains low due to shrinking effects caused by local heating and cooling. This publication presents a data-driven approach where process data is gathered and used in regression learning to predict the geometric accuracy resulting from the shrinking effects. To successfully apply regression learning, a big amount of process data is needed covering a wide range of possible process states. Therefore, a specific experimental series, consisting of 54 individual forming experiments, is designed and carried out. Based on the 3D digitization of the formed parts, a process database is built up comprising 408,296 records, each representing a toolpath point. This process database is used to train 19 different regression models. The performance of their ability to predict the geometric deviations is investigated. A compensation approach is presented that improves the geometric accuracy through a prediction-based modification of the toolpath. Validation experiments demonstrate the improvement of the geometric accuracy of the formed part and the generalizability of the approach.
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影响因子: 3.4
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DOI: 10.1007/s00170-016-9786-7
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影响因子: 3.4
作者:
Magnus, Christian S.
通讯作者: Magnus, Christian S.
DOI: 10.4028/www.scientific.net/kem.473.919
发表时间: 2011
期刊: Key Engineering Materials
影响因子: --
作者:
Taleb-Araghi B;Göttmann A;Bergweiler G;Saeed-Akbari A;Bültmann J;Zettler J;Bambach M;Hirt G
通讯作者: Hirt G