Conditional StyleGAN modelling and analysis for a machining digital twin
Issue title: Selection of papers from the 21st EANN (Engineering Applications of Neural Networks) and 16th AIAI (Artificial Intelligence Applications and Innovations) Joint International Conference
Affiliations: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
Correspondence:
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Corresponding author: Visakan Kadirkamanathan, Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK. Tel.: +44 114 222 5680; Fax: +44 114 222 5661; E-mail: [email protected].
Abstract: 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.
Keywords: Generative adversarial networks, digital twin, machining, simulation, time-domain signals
