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Knowledge discovery for friction stir welding via data driven approaches. Part 1 ‐ correlation analyses of internal process variables and weld quality

机译:通过数据驱动的方法进行搅拌摩擦焊的知识发现。第1部分-内部过程变量与焊接质量的相关性分析

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摘要

For a comprehensive understanding towards friction stir welding (FSW) which would lead to a unified approach that embodies materials other than aluminium, such as titanium and steel, it is crucial to identify the intricate correlations between the controllable process conditions, the observable internal process variables, and the characterisations of the post-weld materials. In Part 1 of this paper, multiple correlation analyses techniques have been developed to detect new and previously unknown correlations between the internal process variables and weld quality of aluminium alloy AA 5083. Furthermore, a new exploitable weld quality indicator has, for the first time, been successfully extracted, which can provide an accurate and reliable indication of the as welded defects. All results relating to this work have been validated using real data obtained from a series of welding trials that utilised a new revolutionary sensory platform called ARTEMIS developed by TWI Ltd, the original inventors of the FSW process.
机译:为了全面了解摩擦搅拌焊(FSW),这将导致采用一种统一方法来体现除铝以外的其他材料,例如钛和钢,至关重要的是要确定可控工艺条件,可观察到的内部工艺变量之间的复杂关系。 ,以及焊后材料的特性。在本文的第1部分中,已经开发了多种相关分析技术来检测内部工艺变量与AA 5083铝合金的焊接质量之间的新的和以前未知的相关性。此外,一种新的可利用的焊接质量指示器首次出现了,已成功提取,可以准确准确地指示焊接缺陷。使用从一系列焊接试验中获得的真实数据,已经验证了与这项工作相关的所有结果,这些数据使用了FSW工艺的原始发明者TWI Ltd开发的一种名为ARTEMIS的新型革命性传感平台。

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