Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

Stanke, Joachim; Trauth, Daniel; Feuerhack, Andreas; Klocke, Fritz

doi:10.1088/1742-6596/896/1/012096

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Considering the qualitative and quantitative results of the validation experiments, the FE simulation results are in good agreement with the experimental results. For further information on the FE model, refer to [20] The process parameters received by the FE simulation server are initially available in the SQL database of the service management. A controller script written in PHP runs on the FE simulation server and searches the SQL database for new FE simulation jobs with the status "paid" (see Fig.…”

Section: Architecture Concept and Implementationmentioning

confidence: 99%

Development of a hybrid DLT cloud architecture for the automated use of finite element simulation as a service for fine blanking

Stanke

Unterberg

Trauth

et al. 2020

Int J Adv Manuf Technol

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Networking and digitization in manufacturing enable novel methods of data-driven analysis and optimization of processes through cross-process data availability. The creation of digital twins plays an important role in this. However, not all data relevant for a digital twin can be measured directly in the process. Therefore, methods are needed that enable the modelling of quantities that are difficult or impossible to measure directly in the process, such as the finite element method. In many companies, however, neither the know-how nor the necessary IT infrastructure for finite element simulations is available. External commissioning processes are also not suitable for achieving the goals of higher productivity and agility pursued with the digitization and networking of manufacturing processes. In this contribution, an architecture is presented that enables the fully automated use of finite element simulation as a service. The architecture is developed using the case study of fine blanking. First, the requirements of the architecture to be created are determined. Important characteristics of the architecture should be scalability as well as interfaces and means of payment suitable for machine communication. In addition, ensuring data integrity is an important requirement when creating the digital twin. Based on the identified requirements, an architecture is then presented that meets these requirements by using cloud computing and distributed ledger technologies and interfaces that can directly process measurement signals from the process and communicate with the architecture. Finally, the capability of the architecture is tested, possible applications and limitations are discussed, and future extensions are considered.

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Section: Architecture Concept and Implementationmentioning

confidence: 99%

Development of a hybrid DLT cloud architecture for the automated use of finite element simulation as a service for fine blanking

Stanke

Unterberg

Trauth

et al. 2020

Int J Adv Manuf Technol

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“…Thus, thicker sheet metal strips and secondary machining must be used to compensate for the die roll and improve dimensional accuracy. There are several process parameters that influence the die roll size such as the design of the tool used, the work piece geometry and thickness, material properties [19], the cutting speed, and other process parameters that are set during the process [20][21][22]. Thus, the fine blanking process is an intricate process with no generic process setup for each component produced.…”

Section: Case Study: a Fine Blanking Linementioning

confidence: 99%

A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems

Glebke¹,

Henze²,

Wehrle³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Large-scale cyber-physical systems such as manufacturing lines generate vast amounts of data to guarantee precise control of their machinery. Visions such as the Industrial Internet of Things aim at making this data available also to computation systems outside the lines to increase productivity and product quality. However, rising amounts and complexities of data and control decisions push existing infrastructure for data transmission, storage, and processing to its limits. In this paper, we exemplarily study a fine blanking line which can produce up to 6.2 Gbit/s worth of data to showcase the extreme requirements found in modern manufacturing. We consequently propose integrated data processing which keeps inherently local and small-scale tasks close to the processes while at the same time centralizing tasks relying on more complex decision procedures and remote data sources. Our approach thus allows for both maintaining control of field-level processes and leveraging the benefits of "big data" applications.

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“…Other authors predict that artificial intelligence and machine learning will have enormous effects on blanking processes [5], while the first applications have already been presented. For example, neural networks can be used to approximate the effects of fluctuating process parameters on geometric product properties, whereby training data can be obtained through FEM simulations, as presented by Stanke et al [6] or Hambli and Guerin [7]. It was also shown that wear conditions can be accurately classified by extracting features from force signals and inputting them into Support Vector Machines [8].…”

Section: Introductionmentioning

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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Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

Cited by 7 publications

References 12 publications

Development of a hybrid DLT cloud architecture for the automated use of finite element simulation as a service for fine blanking

Development of a hybrid DLT cloud architecture for the automated use of finite element simulation as a service for fine blanking

A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems

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

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