Computer-Aided Manufacturing and Design
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 intelli...
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| HerausgeberIn: | |
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| Year of Publication: | 2020 |
| Language: | English |
| Physical Description: | 1 electronic resource (198 p.) |
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| 100 | 1 | |a Choi, Seung-Kyum |4 edt | |
| 245 | 1 | 0 | |a Computer-Aided Manufacturing and Design |
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| 520 | |a 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. | ||
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| 653 | |a gradient-based algorithm | ||
| 653 | |a robust genetic algorithm | ||
| 653 | |a warpage | ||
| 653 | |a design of experiments | ||
| 653 | |a fringe pattern | ||
| 653 | |a birefringence | ||
| 653 | |a automatic design | ||
| 653 | |a intelligent optimization method | ||
| 653 | |a CFD | ||
| 653 | |a fluid machinery | ||
| 653 | |a pumps | ||
| 653 | |a multi-function console | ||
| 653 | |a data-driven design | ||
| 653 | |a mismatch equation | ||
| 653 | |a anthropometric measures | ||
| 653 | |a algorithmic approach | ||
| 653 | |a optimal design | ||
| 653 | |a stretchable antenna-based strain sensor | ||
| 653 | |a structural optimization | ||
| 653 | |a structural health monitoring | ||
| 653 | |a dimension reduction | ||
| 653 | |a entropy-based correlation coefficient | ||
| 653 | |a multidisciplinary design and analysis | ||
| 653 | |a uncertainty-integrated and machine learning-based surrogate modeling | ||
| 653 | |a additive manufacturing | ||
| 653 | |a complexity | ||
| 653 | |a modular design | ||
| 653 | |a part consolidation | ||
| 653 | |a product recovery | ||
| 653 | |a product image design | ||
| 653 | |a Kansei Engineering | ||
| 653 | |a integrated decision system | ||
| 653 | |a qualitative decision model | ||
| 653 | |a quantitative decision model | ||
| 653 | |a train seats | ||
| 653 | |a measurement-assisted assembly | ||
| 653 | |a coordination space | ||
| 653 | |a assemblability | ||
| 653 | |a small displacement torsor | ||
| 653 | |a Kriging | ||
| 653 | |a lower confidence bounding | ||
| 653 | |a entropy theory | ||
| 653 | |a product design | ||
| 653 | |a simulation-based design optimization | ||
| 653 | |a convolutional neural network | ||
| 653 | |a object detection | ||
| 653 | |a piping and instrument diagram | ||
| 653 | |a unsupervised learning | ||
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| 700 | 1 | |a Gorguluarslan, Recep M. |4 oth | |
| 700 | 1 | |a Zhou, Qi |4 oth | |
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