Computer-Aided Manufacturing and Design

doi:10.3390/books978-3-03943-135-9

Download Url(s)

https://mdpi.com/books/pdfview/book/3109

Contributor(s)

Choi, Seung-Kyum (editor)

Gorguluarslan, Recep M. (editor)

Zhou, Qi (editor)

Language

English

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Abstract

Recent advancements in computer technology have allowed for designers to have direct control over the production process through the help of computer-based tools, creating the possibility of a completely integrated design and manufacturing process. Over the last few decades, "artificial intelligence" (AI) techniques, such as machine learing and deep learning, have been topics of interest in computer-based design and manufacturing research fields. However, efforts to develop computer-based AI to handle big data in design and manufacturing have not yet been successful. This Special Issue aims to collect novel articles covering artificial intelligence-based design, manufacturing, and data-driven design. It will comprise academics, researchers, mechanical, manufacturing, production and industrial engineers and professionals related to engineering design and manufacturing.

URI

https://directory.doabooks.org/handle/20.500.12854/69317

Keywords

product service system (PSS); availability; field repair kit; gradient-based algorithm; robust genetic algorithm; warpage; design of experiments; fringe pattern; birefringence; automatic design; intelligent optimization method; CFD; fluid machinery; pumps; multi-function console; data-driven design; mismatch equation; anthropometric measures; algorithmic approach; optimal design; stretchable antenna-based strain sensor; structural optimization; structural health monitoring; dimension reduction; entropy-based correlation coefficient; multidisciplinary design and analysis; uncertainty-integrated and machine learning-based surrogate modeling; additive manufacturing; complexity; modular design; part consolidation; product recovery; product image design; Kansei Engineering; integrated decision system; qualitative decision model; quantitative decision model; train seats; measurement-assisted assembly; coordination space; assemblability; small displacement torsor; Kriging; lower confidence bounding; entropy theory; product design; simulation-based design optimization; convolutional neural network; object detection; piping and instrument diagram; unsupervised learning; n/a