In-situ material classification in sheet-metal blanking using deep convolutional neural networks

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

doi:10.1007/s11740-019-00928-w

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Using ANN in sheet metal forming has been part of the literature for several decades. Many studies are concerned with the spring-back behavior in bending processes [48][49][50], the error diagnosis and process control in incremental forming [51], and characterization of material properties [52][53][54]. In contrast, ANN approaches are rarely applied to the identification of wear states in sheet metal forming.…”

Section: Data-driven Methods For Tool Wear Predictionmentioning

confidence: 99%

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Kubik

Becker

Molitor

et al. 2022

Prod. Eng. Res. Devel.

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Wear is one of the decisive factors for the economic efficiency of sheet metal forming processes. Thereby, progressive wear phenome lead on the one hand to a poor workpiece quality and on the other hand to tool failure resulting in high machine downtimes. This trend is intensified by processing high-strength materials and the reduction of lubricant up to dry forming. In this context, data-driven monitoring methods such as machine learning (ML) provide the potential of detecting wear at an early stage to overcome manual and cost-intensive process inspections. The presented study aims to provide a ML based inline quantification of wear states within sheet metal forming processes. The development of this monitoring approach is based on a procedure model the Knowledge Discovery in Time series and image data in Engineering Epplications (KDT-EA) which is validated on two forming processes, blanking and roll forming, that strongly differ in their physical process behavior and their acquired process data. The presented inline quantification allows an estimation of wear states with a deviation of less than 0.83% for the blanking process and 2.21% for the roll forming process from the actual wear state. Furthermore, it is shown that combining different feature extraction methods as well as a compensation of unbalanced data using data augmentation techniques are able to improve the performance of the investigated ML models.

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Section: Data-driven Methods For Tool Wear Predictionmentioning

confidence: 99%

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Kubik

Becker

Molitor

et al. 2022

Prod. Eng. Res. Devel.

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“…These measurements were used to predict different material properties, e.g., yield strength. Unterberg et al distinguish whether material specimen were extracted from the start, the middle, or the end of a sheet-metal coil using a machine learning algorithm with magnetic Barkhausen noise as input data [12]. However, magnetic Barkhausen noise has not yet been used for inline monitoring.…”

Section: Non-destructive Testing In Sheet-metal Forming (Rq1)mentioning

confidence: 99%

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

ORTJOHANN

2023

Materials Research Proceedings

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Abstract. Material properties can vary both along a sheet-metal coil and from coil to coil despite tight tolerances influencing the process stability of sheet-metal processes and the part quality, which leads to rejects and machine downtime [1]. This significantly affect the economic efficiency of the process. Monitoring fluctuations in material characteristics at regular intervals along sheet-metal coils is enabled directly by non-destructive testing (NDT) before the process offering conclusions on the material properties. Another material monitoring technique arises from monitoring process conditions of the upstream processes, e.g., cold rolling, leading indirectly to insights on material properties. In this work, a systematic literature review (SLR) [2] is conducted to investigate recent approaches for material monitoring and for the utilization of resulting material data for optimizing different sheet-metal forming processes. Existing approaches for different sheet-metal forming processes are critically appraised. Based on the SLR research gaps are revealed and research opportunities, e.g., arising from a potential transfer of existing solutions between different forming processes and recent advances in data-driven methods, are identified.

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“…Indirect measures of signals such as forces or vibration show considerable variations on a stroke-tostroke basis as well as long-term trends, but are influences not only by effect of wear, but also by e.g. inhomogeneous in-going material properties or dynamics of the machine tool [84]. Together with the sensitivity of the sensor opposes challenges to potential wear monitoring systems that need to filter important wear related information from auxiliary signals and derive estimators for specific wear effect in complex tool geometries.…”

Section: Digitisation In Sheet Metal Blanking Technologiesmentioning

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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In-situ material classification in sheet-metal blanking using deep convolutional neural networks

Cited by 13 publications

References 14 publications

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

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

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