Silicon Carbide Surface Quality Prediction Based on Artificial Intelligence Methods on Multi-sensor Fusion Detection Test Platform

Zhang, Yawei; Li, Beizhi; Yang, Jianguo; Liu, Xiao; Zhou, Jinqiang

doi:10.1080/10910344.2018.1486414

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…e details inside the clothes are usually the structural elements outside the frame, including collar, sleeve, pocket, front stand, side swing, split line, local road, pleats, stitches, back seams, cuffs, decorative arts, and crafts. By carefully designing these local details, not only the function of the clothes can be improved but also the beauty of various shapes of the clothes can be reflected [7,8].…”

Section: Interior Detailsmentioning

confidence: 99%

Fusion Mode and Style Based on Artificial Intelligence and Clothing Design

Qiu

2021

Mathematical Problems in Engineering

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With the upgrading of intelligent manufacturing, industrial robots will play an important role in the garment industry. The purpose of this article was to study the pattern and style based on the integration of artificial intelligence and clothing design. In this article, the digital modeling of clothing design and the case analysis of intelligent clothing design are described using the method of comparative experiment. The experimental results are obtained from the analysis of fuzzy number of clothing design language evaluation, three-dimensional human body construction clothing size, clothing design elements and auxiliary functions, and the analysis of the advantages and disadvantages of clothing design system. The popular clothing sample is D4 (0.4862), which is 20% higher than other products. It can be concluded that the model proposed in this article can grasp the needs of consumers and select the right one according to the market positioning. The fabric mass production fashion brand can significantly improve the efficiency and satisfaction of the fabric selection decision-making process. It provides enough technical support and style model for intelligent clothing design.

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Section: Interior Detailsmentioning

confidence: 99%

Fusion Mode and Style Based on Artificial Intelligence and Clothing Design

Qiu

2021

Mathematical Problems in Engineering

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AI for tribology: Present and future

Yin,

Yang,

Liu

et al. 2024

Friction

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With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI) can assist researchers in swiftly extracting valuable patterns, trends, and associations from subjective information. Tribological behaviors are characterized by dependence on systems, evolution with time, and multidisciplinary coupling. The friction process involves a variety of phenomena, including mechanics, thermology, electricity, optics, magnetics, and so on. Hence, tribological information possesses the distinct characteristics of being multidisciplinary, multilevel, and multiscale, so that the application of AI in tribology is highly extensive. To delineate the scope, classification, and recent trends of AI implementation in tribology, this review embarks on exploration of the tribology research domain. It comprehensively outlines the utilization of AI in basic theory of tribology, intelligent tribology, component tribology, extreme tribology, bio-tribology, green tribology, and other fields. Finally, considering the emergence of “tribo-informatics” as a novel interdisciplinary field, which combines tribology with informatics, this review elucidates the future directions and research framework of “AI for tribology”. In this paper, tribo-system information is divided into 5 categories: input information (I), system intrinsic information (S), output information (O), tribological state information (Ts), and derived state information (Ds). Then, a fusion method among 5 types of tribo-system information and different AI technologies (regression, classification, clustering, and dimension reduction) has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization. The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics. Ultimately, it aspires to enhance the efficiency of problem-solving in tribology.

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