Autoencoder based Wear Assessment in Sheet Metal Forming

Niemietz, Philipp; Unterberg, Martin; Trauth, Daniel; Bergs, Thomas

doi:10.1088/1757-899x/1157/1/012082

Cited by 15 publications

(5 citation statements)

References 11 publications

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“…Autoencoders have been effectively used to evaluate defect and machine degradation conditions by analysing process signals like acoustic emission and force data in stamping [14,15]. However, these investigations mainly considered stroke forces, and their findings were not cross validated using alternative unsupervised learning methods like clustering algorithms.…”

Section: Related Work and Proposed Frameworkmentioning

confidence: 99%

Anomaly Detection in the Automotive Stamping Process: An Unsupervised Machine Learning Approach

Zhang,

Creighton,

Lim

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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In metal forming, such as stamping of automotive parts, unsupervised machine learning models offer a transformative approach to real-time quality control, especially when labelled data are scarce. Leveraging clustering algorithms and autoencoders, we develop a machine learning system capable of autonomously monitoring sensor data and identifying deviations suggestive of potential defects. The system offers multiple benefits including rapid intervention, reduced part defects and lower stoppages required to rectify defects. The use of unsupervised machine learning models also adds a layer of adaptability, allowing the system to continually refine its understanding of what constitutes a ‘normal’ operation. Empirical evaluation demonstrates the potential of the developed system in detecting anomalies in production data collected from dynamic automotive manufacturing environments.

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Section: Related Work and Proposed Frameworkmentioning

confidence: 99%

Anomaly Detection in the Automotive Stamping Process: An Unsupervised Machine Learning Approach

Zhang,

Creighton,

Lim

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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“…However, traditional dimension reduction methods can further considerably reduce the amount of data (Bergs et al 2020). A domain knowledge-based approach combined with methods that independently learn features of time series data can already yield good monitoring capabilities utilizing only computational edge feasible models (Niemietz et al 2021).…”

Section: Data Stream Management and Analysismentioning

confidence: 99%

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Pennekamp,

Belova,

Bergs

et al. 2023

Interdisciplinary Excellence Accelerator Series

Self Cite

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The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today’s production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today’s production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL.

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“…Asahi et al present a Temporal Convolutional Network autoencoder for extracting features from raw time series and a SVN to classify 3 discrete wear states [35]. A similar framework is presented by Niemietz et al, leveraging the architecture of an autoencoder (AE) to track tool wear progression during fine blanking [36]. Another approach is shown by Molitor et al who determine the wear state of a blanking tool inline by capturing images of the processed parts.…”

Section: Data-based Prediction Of Tool Wear In Sheet Metal Formingmentioning

confidence: 99%

Towards a real-time tool state detection in sheet metal forming processes validated by wear classification during blanking

Kubik¹,

Molitor²,

Rojahn³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The potential of data for inline detection of changes in the physical state of sheet metal forming processes has been proven over the last decade. However, with production rates exceeding 300 parts per minute the time available for a workpiece-related processing of sensor data is reduced. Therefore, the analysis of large data sets is outsourced to the cloud taking advantage of the high computing power provided there. But within this cloud-based computing paradigm, the speed of data transmission hinders real-time analysis of data and causes latency between fault detection and its occurrence. To overcome this bottleneck, this study aims to evaluate a data-based monitoring (DBM) approach for estimating process states in high speed sheet metal forming in terms of their suitability for a decentralized analysis at the edge. Thereby, the DBM is evaluated according to the model accuracy and the absolute computing time. In order to quantify these key performance parameters and the applicability of the DBM on edge devices, a classification of 16 wear states during blanking is considered. Based on the key performance parameters, an optimal DBM approach for decentralized analysis is proposed and an empirical formulation is provided to estimate the absolute computing time depending on the computational resources used for data processing.

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Autoencoder based Wear Assessment in Sheet Metal Forming

Cited by 15 publications

References 11 publications

Anomaly Detection in the Automotive Stamping Process: An Unsupervised Machine Learning Approach

Anomaly Detection in the Automotive Stamping Process: An Unsupervised Machine Learning Approach

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Towards a real-time tool state detection in sheet metal forming processes validated by wear classification during blanking

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