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Titlebook: Data-Driven Engineering Design; Ang Liu,Yuchen Wang,Xingzhi Wang Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusiv

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樓主: Autopsy
31#
發(fā)表于 2025-3-27 00:45:44 | 只看該作者
User-Generated Content Analysis for Customer Needs Elicitation,ng purchase decisions. UGC also contains invaluable information for product designers on customers’ behaviours, needs and opinions. In this chapter, a UGC analysis framework is proposed for researchers and practitioners to extract large quantities of information from UGC data related to product feat
32#
發(fā)表于 2025-3-27 02:46:02 | 只看該作者
Data-Driven Conceptual Design,objective and analytical. This chapter introduces the data-driven conceptual design. There are three data services: (1) helping novice designers accumulate experience, knowledge, and cognitions. (2) Helping designers plan, redesign, and improve the design process. (3) Helping designers verify design
33#
發(fā)表于 2025-3-27 05:57:38 | 只看該作者
Management of Constraints, Complexities, and Contradictions in the Data Era, analysis techniques are becoming critical tools for managing the increasingly complex concept improvement process. The advantages of the data-driven approach lie in its objectivity, as traditionally, design decisions depend on designers’ subjective opinions. This chapter introduces a data-driven ap
34#
發(fā)表于 2025-3-27 10:49:20 | 只看該作者
35#
發(fā)表于 2025-3-27 16:01:49 | 只看該作者
36#
發(fā)表于 2025-3-27 18:48:34 | 只看該作者
37#
發(fā)表于 2025-3-27 22:20:26 | 只看該作者
38#
發(fā)表于 2025-3-28 03:29:36 | 只看該作者
39#
發(fā)表于 2025-3-28 10:11:39 | 只看該作者
Book 2022 showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation..Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design..
40#
發(fā)表于 2025-3-28 11:18:49 | 只看該作者
https://doi.org/10.1007/978-3-662-46402-1 data is used to specify concept improvement design targets. These benefits are explained through constraint, complexity, and contradiction management methodologies. Finally, a case study of a robot vacuum cleaner illustrates this data-driven approach to the concept improvement.
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