Simultaneous Development of a Self-learning Engineering Assistance System

Sauer, Ch.; Schleich, Benjamin; Wartzack, Sandro

doi:10.1007/978-3-030-61902-2_6

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…BERTONI et al [1] bilden dagegen durch die Analyse von Nutzungsdaten Zusammenhänge diverser Leistungsparameter eines Radladers auf Basis seiner Betriebsvariablen mittels Regression ab. Auch zur Absicherung der Fertigbarkeit von Gestaltentwürfen im Sinne des fertigungsgerechten Konstruierens können maschinelle Lernverfahren zum Einsatz kommen [24]. Darüber hinaus bietet die Entwicklung von virtuellen Sensoren, die redundant, substituierend oder erweiternd für reale Sensoren eingesetzt werden [25], große Potentiale zur Reduzierung von Kosten und Applikationsaufwand realer Sensoren oder zur Informationserweiterung in frühen und späten experimentellen Absicherungsprozessen [26].…”

Section: Datengetriebene Methoden In Der Produktentwicklungunclassified

Voraussetzungen für den Einsatz datengetriebener Methoden in der Produktentwicklung

Mehlstäubl¹,

Nicklas²,

Gerschütz³

et al. 2021

DS 111: Proceedings of the 32nd Symposium Design for X

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Section: Datengetriebene Methoden In Der Produktentwicklungunclassified

Voraussetzungen für den Einsatz datengetriebener Methoden in der Produktentwicklung

Mehlstäubl¹,

Nicklas²,

Gerschütz³

et al. 2021

DS 111: Proceedings of the 32nd Symposium Design for X

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“…For this purpose an engineering workbench was created, which facilitates the support for the scientists and later product developers in conjunction with the SBMF technology. This engineering workbench is called SLASSY (self-learning assistance system) [ 17 ]. SLASSY enables the storage of expert knowledge about the manufacturing process in its database via prediction models and analysis functions for current parts in design.…”

Section: Problem Definitionmentioning

confidence: 99%

Fringe Projection Profilometry in Production Metrology: A Multi-Scale Comparison in Sheet-Bulk Metal Forming

Hinz

Metzner

Müller

et al. 2021

Sensors

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Fringe projection profilometry in combination with other optical measuring technologies has established itself over the last decades as an essential complement to conventional, tactile measuring devices. The non-contact, holistic reconstruction of complex geometries within fractions of a second in conjunction with the lightweight and transportable sensor design open up many fields of application in production metrology. Furthermore, triangulation-based measuring principles feature good scalability, which has led to 3D scanners for various scale ranges. Innovative and modern production processes, such as sheet-bulk metal forming, thus, utilize fringe projection profilometry in many respects to monitor the process, quantify possible wear and improve production technology. Therefore, it is essential to identify the appropriate 3D scanner for each application and to properly evaluate the acquired data. Through precise knowledge of the measurement volume and the relative uncertainty with respect to the specimen and scanner position, adapted measurement strategies and integrated production concepts can be realized. Although there are extensive industrial standards and guidelines for the quantification of sensor performance, evaluation and tolerancing is mainly global and can, therefore, neither provide assistance in the correct, application-specific positioning and alignment of the sensor nor reflect the local characteristics within the measuring volume. Therefore, this article compares fringe projection systems across various scale ranges by positioning and scanning a calibrated sphere in a high resolution grid.

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“…An excerpt of an M5P regression model tree is shown in Figure 6. For more specific use cases, as for example with so-called local metamodels, deep learning methods are implemented [47], as they have shown even better prediction quality.…”

Section: Implemented Metamodel Algorithmsmentioning

confidence: 99%

SLASSY—An Assistance System for Performing Design for Manufacturing in Sheet-Bulk Metal Forming: Architecture and Self-Learning Aspects

Sauer

Breitsprecher

Küstner

et al. 2021

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Substantial efforts have been made to integrate manufacturing- and design-relevant knowledge into product development processes. A common approach is to provide the relevant knowledge to the design engineers using a knowledge-based system (KBS) that, in turn, becomes the engineering assistance system. Keeping the knowledge up to date is a critical issue, making knowledge acquisition a bottleneck of developing and maintaining KBS. This article presents a robust metamodel optimization and performance estimation architecture for developing and maintaining a KBS useful for design-for-manufacturing from the context of sheet-bulk metal forming. It is shown that the presented KBS or engineering assistance system helps achieve performing design-for-manufacturing, integrating both design and manufacturing knowledge. Using the presented approach helps overcome the bottleneck of knowledge acquisition and knowledge update through its self-learning component based on data mining and knowledge discovery.

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Simultaneous Development of a Self-learning Engineering Assistance System

Cited by 4 publications

References 10 publications

Voraussetzungen für den Einsatz datengetriebener Methoden in der Produktentwicklung

Voraussetzungen für den Einsatz datengetriebener Methoden in der Produktentwicklung

Fringe Projection Profilometry in Production Metrology: A Multi-Scale Comparison in Sheet-Bulk Metal Forming

SLASSY—An Assistance System for Performing Design for Manufacturing in Sheet-Bulk Metal Forming: Architecture and Self-Learning Aspects

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