Punch-to-Punch Variations in Stamping Processes

Bergs, Thomas; Niemietz, Philipp; Kaufman, Tobias; Trauth, Daniel

doi:10.1109/sami48414.2020.9108761

Cited by 18 publications

(8 citation statements)

References 14 publications

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“…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]. These sensors detect elastic structural deflections as complex vibration signals, which are emitted from various points in the tool structure and press machine (Fig.…”

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

confidence: 99%

“…The presented plots are found representative for several series of experiments conducted with the fine-blanking process. For further analysis, reliable metrics that can grasp the amount of variation on a stroke-to-stroke basis [85] as well as long-term trends [90] need to be researched in detail.…”

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

confidence: 99%

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

confidence: 99%

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.

Self Cite

View full text Add to dashboard Cite

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“…The Euclidean and Mahalanobis distance of the first two PCA components were used to detect anomalies in consecutive punches. The authors showed that the process is subject to extensive fluctuations even with constant process settings [36].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Molitor

Kubik

Hetfleisch

et al. 2022

Prod. Eng. Res. Devel.

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Blanking processes belong to the most widely used manufacturing techniques due to their economic efficiency. Their economic viability depends to a large extent on the resulting product quality and the associated customer satisfaction as well as on possible downtimes. In particular, the occurrence of increased tool wear reduces the product quality and leads to downtimes, which is why considerable research has been carried out in recent years with regard to wear detection. While processes have widely been monitored based on force and acceleration signals, a new approach is pursued in this paper. Blanked workpieces manufactured by punches with 16 different wear states are photographed and then used as inputs for Deep Convolutional Neural Networks to classify wear states. The results show that wear states can be predicted with surprisingly high accuracy, opening up new possibilities and research opportunities for tool wear monitoring of blanking processes.

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“…The focus is set on the analysis of sequences of time series data that contain characteristic seasonal patterns, where each profile represents a forming operation. Within these patterns, both stroke-to-stroke variations and long-term variations in large series of consecutive forming operations can be identified (Bergs et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Relating wear stages in sheet metal forming based on short- and long-term force signal variations

et al. 2022

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Monitoring systems in sheet metal forming cannot rely on direct measurements of the physical condition of interest because the space between the die component and the material is inaccessible. Therefore, in order to gain further insight into the forming or stamping process, sensors must be used to detect auxiliary quantities such as acoustic emission and force that relate to the physical quantities of interest. While it is known that changes in force data are related to physical parameters of the process material, lubricant used, and geometry, the changes in data over large stroke series and their relationship to wear are the subject of this paper. Previously, force data from different wear conditions (artificially introduced into the system and not occurring in an industry-like environment) were used as input for clustering and classifying high and low wear force data. This paper contributes to fill the current research gap by isolating structural properties of data as indicators of wear growth to quantify the wear evolution during ongoing production in industry-like scenarios. The selected methods represent either established methods in sheet metal forming force data analysis, dimensionality reduction for local structure separation or generic feature extraction. The study is conducted on a set of four experiments with each containing about 3000 strokes.

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Punch-to-Punch Variations in Stamping Processes

Cited by 18 publications

References 14 publications

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

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

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Relating wear stages in sheet metal forming based on short- and long-term force signal variations

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