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

authored by: Mathias Liewald, Thomas Bergs, Peter Groche, Bernd Arno Behrens, David Briesenick, Martina Müller, Philipp Niemietz, Christian Kubik, Felix Müller
Abstract: 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.
Organisation(s): Institute of Metal Forming and Metal Forming Machines
External Organisation(s): University of Stuttgart
RWTH Aachen University
Technische Universität Darmstadt
Type: Article
Journal: Production Engineering
Volume: 16
Pages: 607-625
No. of pages: 19
ISSN: 0944-6524
Publication date: 10.2022
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Mechanical Engineering, Industrial and Manufacturing Engineering
Electronic version(s): https://doi.org/10.1007/s11740-022-01115-0 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{92f44c07407c416185c1ce373d93a3e7,
title = "Perspectives on data-driven models and its potentials in metal forming and blanking technologies",
abstract = "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.",
keywords = "Data science, Explainable artificial intelligence, Machine Learning, Metal forming, Sheet metal blanking",
author = "Mathias Liewald and Thomas Bergs and Peter Groche and Behrens, {Bernd Arno} and David Briesenick and Martina M{\"u}ller and Philipp Niemietz and Christian Kubik and Felix M{\"u}ller",
note = "Funding Information: The authors would like to thank the German Federal Ministry for Economic Affairs and Energy within the framework of the Programme for Competition Digital Technologies for Business via the research project “EMuDig 4.0” and the project “Mittelstand 4.0 - Kompetenzzentrum Darmstadt”. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy–EXC-2023 Internet of Production–390621612. Furthermore, funded within the project DFG-397768783",
year = "2022",
month = oct,
doi = "10.1007/s11740-022-01115-0",
language = "English",
volume = "16",
pages = "607--625",
journal = "Production Engineering",
issn = "0944-6524",
publisher = "Springer Verlag",
number = "5",
}