Online Learning of Stability Lobe Diagrams in Milling

Friedrich, Jens; Torzewski, Jonas; Verl, Alexander

doi:10.1016/j.procir.2017.12.213

Cited by 23 publications

(3 citation statements)

References 11 publications

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“…Data-driven modelling addresses this issue by making use of the big data produced by the various manufacturer's CPSs and automating the modelling process, thus aligning the digital twin state with the evolutionary changes in the modelled systems. Therefore, despite the wide success of physics-based simulation models for prediction of abnormal conditions during milling and turning processes, data-driven simulation methods offer greater flexibility at adapting to a broader range of conditions, including dynamically changing ones [20].…”

Section: Simulation and Data-driven Modellingmentioning

confidence: 99%

Conditional StyleGAN modelling and analysis for a machining digital twin

Зотов¹,

Tiwari²,

Kadirkamanathan³

2021

ICA

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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.

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Section: Simulation and Data-driven Modellingmentioning

confidence: 99%

Conditional StyleGAN modelling and analysis for a machining digital twin

Зотов¹,

Tiwari²,

Kadirkamanathan³

2021

ICA

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“…Several studies focus on predicting the stability of processes, i.e. the occurrence of chatter, thus representing a stability lobe diagram (SLD): Cheruruki et al use ANN for chatter prediction in turning [12], Friedrich et al estimate a stability lobe diagram using support vector machines (SVM) and ANN [13], Denkena et al use kernel interpolation [14]. All these approaches model a setup with a single combination of one machine and one tool.…”

Section: Experimental Identification and Data-driven Approachesmentioning

confidence: 99%

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Barton

Fleischer²

2020

Lecture Notes in Production Engineering

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Vibrations have a significant influence on quality and costs in metal cutting processes. Existing methods for predicting vibrations in machine tools enable an informed choice of process settings, however they rely on costly equipment and specialised staff. Therefore, this contribution proposes to reduce the modelling effort required by using machine learning based on data gathered during production. The approach relies on two sub-models, representing the machine structure and machining process respectively. A method is proposed for initialising and updating the models in production.

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“…The automation of the modelling process attainable with data-driven modelling circumvents this issue, aligning the states of a physical object and its digital twin. Thus, while physics-based simulations are successful at machining abnormalities prediction, data-driven approaches enable higher adaptability to the dynamic internal and environmental conditions ( Friedrich et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

Зотов

Kadirkamanathan

2021

Front. Artif. Intell.

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Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

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Online Learning of Stability Lobe Diagrams in Milling

Cited by 23 publications

References 11 publications

Conditional StyleGAN modelling and analysis for a machining digital twin

Conditional StyleGAN modelling and analysis for a machining digital twin

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

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