Characterization and application of assistance systems in digital engineering

Stark, Rainer; Brandenburg, Elisabeth; Lindow, Kai

doi:10.1016/j.cirp.2021.04.061

Cited by 8 publications

(3 citation statements)

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“…Engineering represents knowledge by rules, frameworks, and heuristics (Preidel et al, 2018a). In order to drive automatization in engineering, this data, information, and knowledge must be made accessible to ML algorithms (Stark, 2022). The use of ML in engineering creates challenges on both sides.…”

Section: Automatization With Machine Learningmentioning

confidence: 99%

“…The discipline of virtual product development has emerged, in which most technical solutions are first developed virtually, meaning they do not yet exist in the physical world. Engineers use IT systems to make these virtual solutions existent, visible, and executable (Stark, 2022). The IT systems contain important information and data about the product, from geometric information in Computer-Aided Design (CAD) to product behaviour in simulations, to manufacturing processes in Computer Aided Process Planning (CAPP), but also multi-disciplinary systems such as Product Data Management (PDM) or Enterprise Resource Planning (ERP).…”

Section: Engineering Processmentioning

confidence: 99%

“…Activities can be supported by the possible use of machine learning in the engineering processes. The type of support varies from feedback to assistance to automatization (Stark et al, 2021). Data must be collected and evaluated to raise the level of engineering support from manual activities to feedback.…”

Section: Automatization With Machine Learningmentioning

confidence: 99%

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Knowledge-Based Data Identification for Machine Learning Use Cases

Ebel,

Ben Hassine,

Stark

2023

Proc. Des. Soc.

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The number of digital solutions based on machine learning has increased in recent years. In many industrial sectors, they try to enhance automation in manual or repetitive tasks or provide decision support for complex problems. Data plays an essential role in the selection and implementation of ML algorithms, as it determines the quality of the training and the results. As data drive ML models, selecting the correct data with the suitable ML algorithm for a given use case is crucial but challenging. This paper reviews the application of machine learning in the embodiment design phase addressing the challenge. The work focuses on ML applications in conventional product development and non-conventional additive manufacturing processes. Based on the literature review, the required knowledge to implement the ML algorithms has been derived and presented in a systematic approach. This work highlights the importance of an initial analysis of the existing knowledge in the engineering and additive manufacturing processes in order to implement the proper ML algorithms.

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Section: Automatization With Machine Learningmentioning

confidence: 99%

Section: Engineering Processmentioning

confidence: 99%