A neural network-based build time estimator for layer manufactured objects
A neural network-based build time estimator for layer manufactured objects
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DOI：
10.1007/s00170-011-3284-8
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发表时间：
2011-11-01
影响因子：
3.4
通讯作者：
Di Stefano, Paolo
中科院分区：
文献类型：
--
作者：
Di Angelo, Luca;Di Stefano, Paolo
正确预测构建时间对于计算层制造对象的准确成本至关重要。在文献中提出的方法有两种类型：详细分析和基于参数的方法。前者需要知道大量与机器的运动学和动力学性能有关的数据。另一方面，参数化模型是通用的，实现起来相对简单；然而，文献中提出的参数化方法只提供了总构建时间的一小部分。因此，他们的表演在任何情况下都是不合适的。为了克服这些限制，本文提出了一种参数化方法，该方法使用了一组更完整的构建时驱动因素。此外，考虑到参数构建时间函数的复杂性，采用了人工神经网络，提高了方法的灵活性。测试用例的分析表明，即使在关键用例和需要支持时，所建议的方法也提供了相当准确的构建时间估计。
A correct prediction of build time is essential to calculate the accurate cost of a layer manufactured object. The methods presented in literature are of two types: detailed-analysis- and parametric-based approaches. The former require that a lot of data, related to the kinematic and dynamic performance of the machine, should be known. Parametric models, on the other hand, are of general use and relatively simple to implement; however, the parametric methods presented in literature only provide a few of the components of the total build time. Therefore, their performances are not properly suited in any case. In order to overcome these limitations, this paper proposes a parametric approach which uses a more complete set of build-time driving factors. Furthermore, considering the complexity of the parametric build time function, an artificial neural network is used so as to improve the method flexibility. The analysis of the test cases shows that the proposed approach provides a quite accurate estimation of build time even in critical cases and when supports are required.