Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model.

中文翻译：

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加工数字孪生体的条件 StyleGAN 建模和分析

制造数字化是向工业 4.0 过渡的关键部分。数字孪生作为一种工具，能够以数字方式访问有关物理对象的精确实时信息，并通过将与其相关的大数据转换为可操作的信息来支持相关流程的优化，因此发挥着重要作用。研究文献中提出了许多框架和概念模型来解决数字双胞胎的要求和好处，但对它们的应用的探索较少。本文提出了一种基于生成对抗网络（GAN）的时域加工振动模型作为数字孪生组件。开发的条件 StyleGAN 架构能够 (1) 从现有模型中提取知识和 (2) 适用于生产过程优化的数据驱动模拟。然后开发了一种应对 GAN 分析挑战的新解决方案，其中生成准确性和敏感性图的比较揭示了这些指标之间的相似性模式。敏感性分析还扩展到中层网络级别，识别异常生成行为的来源。这提供了基于灵敏度的模拟不确定性估计，这对于验证从建议模型导出的最佳工艺条件很重要。其中生成准确性和敏感性地图的比较揭示了这些指标之间的相似性模式。敏感性分析还扩展到中层网络级别，识别异常生成行为的来源。这提供了基于灵敏度的模拟不确定性估计，这对于验证从建议模型导出的最佳工艺条件很重要。其中生成准确性和敏感性地图的比较揭示了这些指标之间的相似性模式。敏感性分析还扩展到中层网络级别，识别异常生成行为的来源。这提供了基于灵敏度的模拟不确定性估计，这对于验证从建议模型导出的最佳工艺条件很重要。