Predictive modeling approaches in laser-based material processing
Predictive modeling approaches in laser-based material processing
复制标题
激光材料加工中的预测建模方法
DOI：
10.1063/5.0018235
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
E. Stratakis
中科院分区：
文献类型：
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作者：
M. Velli;G. Tsibidis;A. Mimidis;E. Skoulas;Yannis Pantazis;E. Stratakis
预测建模是一个新兴的领域，它结合了现有的和新的方法，旨在快速了解物理机制，同时开发新的材料、工艺和结构。在目前的研究中，在一项关键的使能技术-基于激光的制造中，以前从未探索过的预测建模旨在自动化和预测激光加工对材料结构的影响。重点放在典型的统计和机器学习算法在预测一系列材料的激光加工结果方面的表现。对实验数据的结果表明，预测模型能够令人满意地学习激光输入变量与观测材料结构之间的映射关系。这些结果进一步与模拟数据相结合，旨在阐明激光与材料相互作用的多尺度物理过程。因此，由于增加了采样点的数量，我们将调整后的模拟数据扩充到实验中，并显著提高了预测性能。同时，提出了一种识别和量化预测不确定性高的区域的度量标准，揭示了过渡边界附近发生了高不确定性。我们的结果可以为降低材料设计、测试和生产成本的系统方法奠定基础，方法是用精确的预制预测工具取代昂贵的基于试错的制造程序。
Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes and structures. In the current study, previously-unexplored predictive modelling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser processing on a range of materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser input variables and the observed material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser-material interaction. As a consequence, we augmented the adjusted simulated data to the experimental and substantially improved the predictive performance, due to the availability of increased number of sampling points. In parallel, a metric to identify and quantify the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology towards reducing material design, testing and production cost via the replacement of expensive trial-and-error based manufacturing procedure with a precise pre-fabrication predictive tool.