Wear monitoring in fine blanking processes using feature based analysis of acoustic emission signals

Bergs, Thomas; Unterberg, Martin; Voigts, Herman; Weiser, Ingo Felix; Feuerhack, Andreas; Trauth, Daniel

doi:10.1016/j.procir.2021.11.028

Cited by 21 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further studies show that information in raw sensor time series is often redundant, yet classical feature engineering methods based on domain knowledge only partially work (Niemietz et al 2022). Similarly, features selected by experts are highly redundant and unsuitable for further compression (Unterberg et al 2021). However, traditional dimension reduction methods can further considerably reduce the amount of data (Bergs et al 2020).…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The library has been used for feature engineering across industrial applications. Unterberg et al utilized tsfresh in an exploratory analysis of tool wear during sheet-metal blanking [22]. Sendlbeck et al built a machine learning model to predict gear wear rates using the library [23].…”

Section: Feature Engineeringmentioning

confidence: 99%

Machine Learning in CNC Machining: Best Practices

Hahn

Mechefske

2022

Machines

View full text Add to dashboard Cite

Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work is to practically illustrate several best practices, and challenges, discovered while building an ML system to detect tool wear in metal CNC machining. Namely, one should focus on the data infrastructure first; begin modeling with simple models; be cognizant of data leakage; use open-source software; and leverage advances in computational power. The ML system developed in this work is built upon classical ML algorithms and is applied to a real-world manufacturing CNC dataset. The best-performing random forest model on the CNC dataset achieves a true positive rate (sensitivity) of 90.3% and a true negative rate (specificity) of 98.3%. The results are suitable for deployment in a production environment and demonstrate the practicality of the classical ML algorithms and techniques used. The system is also tested on the publicly available UC Berkeley milling dataset. All the code is available online so others can reproduce and learn from the results.

show abstract

“…The curse of dimensionality. Progressive die stamping is especially challenging because multiple tools and multiple signals are the cause of a cumbersome "curse of dimensionality" [21] to any control or optimization process. To make things worse, when progressive die stamping involves tiny components, such as washers [22], a single tooling setup can include more than 50 tools simultaneously operating and the stamping rate can be as high as several hundreds of strokes per minutes.…”

Section: Introductionmentioning

confidence: 99%

A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping

Farioli

Kaya

Fumagalli³

et al. 2023

JMMP

View full text Add to dashboard Cite

The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring progressive wear or at the detection of sudden failures or anomalies. Early detection of tool failure is the method preferred by the recent literature on data management in sheet metal stamping. However, the stamping of small parts poses challenges due to multiple tools and signals and limited visibility of die wear, requiring management of multiple sensors and data sources. This paper proposes a failure prevention approach for progressive die stamping using global and local force sensors with upper bounds for maximum values to indicate unhealthy conditions. The methodology was tested on millions of small washers made of carbon steel. The stamping process was implemented using a servo-press with a high rate. The press was equipped with eight in-process sensors, including strain gauges, thin foil force sensors, and acoustic sensors. The data of material properties, maintenance reports, statistical process control data, and in-process sensors were collected and stored in a data lake. By combining the in-process sensor acquisition with the corresponding log events and maintenance data in the same time span, it is possible to look for correlations among the variables and build an effective tool health prevention policy.

show abstract

Wear monitoring in fine blanking processes using feature based analysis of acoustic emission signals

Cited by 21 publications

References 20 publications

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

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

Machine Learning in CNC Machining: Best Practices

A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping

Contact Info

Product

Resources

About