KNOWLEDGE-BASED DATA IDENTIFICATION FOR MACHINE LEARNING USE CASES
نویسندگان
چکیده
Abstract The number of digital solutions based on machine learning has increased in recent years. In many industrial sectors, they try to enhance automation manual or repetitive tasks provide decision support for complex problems. Data plays an essential role the selection and implementation ML algorithms, as it determines quality training results. As data drive models, selecting correct with suitable algorithm a given use case is crucial but challenging. This paper reviews application embodiment design phase addressing challenge. work focuses applications conventional product development non-conventional additive manufacturing processes. Based literature review, required knowledge implement algorithms been derived presented systematic approach. highlights importance initial analysis existing engineering processes order proper algorithms.
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ژورنال
عنوان ژورنال: Proceedings of the Design Society
سال: 2023
ISSN: ['2732-527X']
DOI: https://doi.org/10.1017/pds.2023.240