Stamping Process Modelling in an Internet of Production

Niemietz, Philipp; Pennekamp, Jan; Kunze, Ike; Trauth, Daniel; Wehrle, Klaus; Bergs, Thomas

doi:10.1016/j.promfg.2020.06.012

Cited by 16 publications

(8 citation statements)

References 34 publications

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“…This can be due to different factors such as die wear [6], change in the properties of the material in the sheets obtained from the end of the coil or even due to a bad working condition of the press. The methodology to follow in the research is the development of condition based monitoring tools to diagnose the equipment status in real time with the aim of reducing costs [7].…”

Section: Methodsmentioning

confidence: 99%

In Process Measurement Techniques Based on Available Sensors in the Stamping Machines for the Automotive Industry

Peinado-Asensi

Montés

García

2022

KEM

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We are currently going through an industrial period in which connectivity, data collection of the process and its understanding to optimize it is becoming more and more common. The automotive industry is no exception as we are on the way towards connected factories where the digitization of the stamping process is a trend followed by manufacturers. A common problem often encountered is the high cost required to develop solutions by using this technology. Obtaining parameters of the manufacturing process is a challenge on many occasions. New solutions have been proposed from an opposite point of view, i.e., we evaluate what information can be extracted from the equipment and from the data obtained we can bring forward the possible tools to be developed without the need for extra investment. This article shows the verification of an experimental process, previously developed, with which we intend to find out the status of the press during the drawing process for each cycle that is carried out during production and also the status of the equipment at all times, up to the point of detecting if there is any problem both in the die and in the mechanical components of the press and verifying it with the developed tool, showing that we can know the status of the equipment by monitoring the data in real time.

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Section: Methodsmentioning

confidence: 99%

In Process Measurement Techniques Based on Available Sensors in the Stamping Machines for the Automotive Industry

Peinado-Asensi

Montés

García

2022

KEM

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“…Here, explainability refers to the amount of reliable gain of knowledge and allows deeper insights into the phenomena acting in processes [7]. While common black-box models like regressions and support vector machines (SVM) are comprehensible and therefore widely used in the production engineering environment, the results generated by a neural network training are usually no longer transparent for the Blanking (force and displacement) [41] Deep drawing (AE) [44] Blanking (acceleration and force) [47] Forging (force) [50] Stamping (force) [51] Roll forming (temperature and force) [42] Blanking (acceleration and force) [45] Progressive stamping (force) [48] Fine blanking (force) [52] Blanking (force and AE) [43] Deep drawing (AE) [46] Blanking (acceleration and force) [49] Blanking (force) [53] Feature selection…”

Section: Modellingmentioning

confidence: 99%

“…In the used fine-blanking press machine of this study, nine piezoelectric sensors have been integrated into the tool structure to measure the relevant process forces, namely punch, counterpunch and part holder force [52]. In addition, acoustic emission sensors have been applied to the upper pressure plate close to the punch positions [85].…”

Section: Long-and Short-term Variations In Fine-blanking Process Datamentioning

confidence: 99%

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Liewald

Bergs

Groche

et al. 2022

Prod. Eng. Res. Devel.

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Today, design and operation of manufacturing processes heavily rely on the use of models, some analytical, empirical or numerical i.e. finite element simulations. Models do reflect reality as best as their design and structure may appear, but in many cases, they are based on simplifying assumptions and abstractions. Reality in production, i.e. reflected by measures such as forces, deflections, travels, vibrations etc. during the process execution, is tremendously characterised by noise and fluctuations revealing a stochastic nature. In metal forming such kind of impact on produced product today in detail is neither explainable nor supported by the aforementioned models. In industrial manufacturing the game to deal with process data changed completely and engineers learned to value the high significance of information included in such digital signals. It should be acknowledged that process data gained from real process environments in many cases contain plenty of technological information, which may lead to increase efficiency of production, to reduce downtime or to avoid scrap. For this reason, authors started to focus on process data gained from numerous metal forming technologies and sheet metal blanking in order to use them for process design objectives. The supporting idea was found in a potential combination of conventional process design strategies with new models purely based on digital signals captured by sensors, actuators and production equipment in general. To utilise established models combined with process data, the following obstacles have to be addressed: (1) acquired process data is biased by sensor artifacts and often lacks data quality requirements; (2) mathematical models such as neural networks heavily rely on high quantities of training data with good quality and sufficient context, but such quantities often are not available or impossible to gain; (3) data-driven black-box models often lack interpretability of containing results, further opposing difficulties to assess their plausibility and extract new knowledge. In this paper, an insight on usage of available data science methods like feature-engineering and clustering on metal forming and blanking process data is presented. Therefore, the paper is complemented with recent approaches of data-driven models and methods for capturing, revealing and explaining previously invisible process interactions. In addition, authors follow with descriptions about recent findings and current challenges of four practical use cases taken from different domains in metal forming and blanking. Finally, authors present and discuss a structure for data-driven process modelling as an approach to extent existing data-driven models and derive process knowledge from process data objecting a robust metal forming system design. The paper also aims to figure out future demands in research in this challenging field of increasing robustness for such kind of manufacturing processes.

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“…While recent advances concerning CPS and the IIoT contribute to promising developments with an application in production [4], [10], [11], [14]- [16], [19], [21]- [23], [25], [31], [41], [45], [46], [52], [62], [65], [68], [70]- [72], [76], [77], [81], [83], [97]- [99], [108], [109], the need for security to enable novel forms of industrial collaboration must be especially considered for real-world deployments, where CPS are likely connected to the Internet. Apart from operational reasons (e.g., to ensure safety or privacy), secure approaches also convince traditionally conservative stakeholders, such as companies that fear for their competitiveness, to contribute to cross-domain and inter-company collaborations.…”

Section: A Towards An Internet Of Productionmentioning

confidence: 99%

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

Pennekamp¹,

Buchholz²,

Dahlmanns³

et al. 2020

Proceedings 2020 Learning From Authoritative Security Experiment Results Workshop

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Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research. We look forward to promising research initiatives, projects, and directions that emerge based on our methodological work.

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Stamping Process Modelling in an Internet of Production

Cited by 16 publications

References 34 publications

In Process Measurement Techniques Based on Available Sensors in the Stamping Machines for the Automotive Industry

In Process Measurement Techniques Based on Available Sensors in the Stamping Machines for the Automotive Industry

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

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