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首页> 外文期刊>Procedia Manufacturing >Detecting process instabilities in industrial gas metal arc welding time series
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Detecting process instabilities in industrial gas metal arc welding time series

机译：检测工业气体金属弧焊时间序列工艺稳定性

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摘要

Gas metal arc welding is widely used in industrial series production for joining aluminum. A lot of factors, such as instabilities and complex dependencies, influence the quality of the resulting welding seams. It is challenging to identify the causes of welding defects, and the real reason is not always well understood. Ensuring the process stability helps production workers to increase the overall production efficiency. The process stability increases the process repeatability, so the welding performance is optimized and rejects are avoided. This paper presents a technique to detect process instabilities within the multivariate process variables automatically. An autoencoder architecture is implemented. The latent space of the autoencoder and reconstruction of the time series are used to detect process instabilities. Detected issues are visualized in a heatmap, including supportive metrics to describe deviations from the expected behavior. As a result, the proposed architecture supports process optimization and leads to an increase in production transparency.
机译：气金属电弧焊接广泛用于加入铝的工业系列生产中。许多因素，例如稳定性和复杂的依赖性，影响所得焊缝的质量。确定焊接缺陷的原因是挑战性的，并且实际原因并不总是很好地理解。确保过程稳定有助于生产工人提高整体生产效率。过程稳定性增加了过程重复性，因此焊接性能优化并避免了拒绝。本文介绍了一种自动检测多元过程变量内的过程稳定性的技术。实现了AutoEncoder架构。 AutoEncoder的潜空间和时间序列的重建用于检测过程稳定性。检测到的问题在热图中可视化，包括支持性指标，以描述与预期行为的偏差。因此，拟议的架构支持流程优化，并导致生产透明度的增加。

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