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Artificial Intelligence for advanced non-conventional machining processes

机译：用于高级非传统加工过程的人工智能

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摘要

Non- conventional machining processes play a critical role in the manufacturing of advanced components for high-added value sectors such as aerospace and bioengineering. Zero defect manufacturing is a key objective in these sectors, which requires more efficient monitoring techniques than those classically used in other sectors. In the classical approach, the engineer him/herself has to decide the statistical variables from which relevant information about the process will be obtained. Scientific literature shows that threshold levels were manually set for the voltage signal for monitoring and control of the Wire Electrical Machining (WEDM) process applied to aerospace components. However, now that the amount of data available is extremely large (Big Data), the decision on the statistics is not always straightforward. In this context, unsupervised Artificial Intelligence (AI) techniques provide a very interesting approach to the problem. In this paper, unsupervised machine learning techniques are used to extract relevant information from the voltage signal in the wire electrical discharge machining process.
机译：非常规加工过程在高附加值扇区的制造中发挥着关键作用，例如航空航天和生物工程。零缺陷制造是这些扇区的关键目标，这需要比在其他扇区的典型使用的那些更有效的监控技术。在古典方法中，工程师他/她自己必须决定将获得关于该过程的相关信息的统计变量。科学文献表明，手动设置阈值水平，用于电压信号，用于监测和控制应用于航空航天部件的电线电加工（WEDM）过程。但是，现在可以提供的数据量非常大（大数据），对统计数据的决定并不总是很简单。在这种情况下，无监督的人工智能（AI）技术提供了对问题的非常有趣的方法。在本文中，无监督的机器学习技术用于从导线电气放电加工过程中的电压信号中提取相关信息。

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