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A digital modeling framework for the motorcycle industry with advanced computer design
- Source :
- Soft Computing. 25:12465-12476
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- A digital modeling framework for motorcycles (DMF) involves applying advanced computer design, computer-assisted analysis, and computer-assisted development. Data processing technology simulated prototype product, fast prototype technology, standard technology, etc. It is an interdisciplinary, systematic technology in the entire process of product design, production, and manufacture. DMF solves single-model production and constrained capacity building to satisfy the industry's demand for individual and diverse motorcycle goods. DMF sets forward several approaches within it, including parametric technologies for design, technology dependent on experience, design application, and data integration trends. The DMF provides the basis for a synthesis of motorcycles' production and demand. The result illustrates that digital concept strategies and approaches can be used in the motorcycle industry in reality with the highest efficiency (97.32%), emission rate (25.16%), speed rate (95.23%), exhaust rate (41.32%), heat loss rate (6.18%), performance metrics rate (9.32%), and error rate (5.44%), compared to other popular methods. For the 100 motorcycles, the average charging capacity, speed, distance traveled, and implementation cost ratio are observed at 97.11%, 85–100 km/h, 80–100 km, and 9.32%, respectively.
- Subjects :
- 0209 industrial biotechnology
Data processing
Product design
Computer science
Process (computing)
Word error rate
02 engineering and technology
computer.software_genre
Automotive engineering
Theoretical Computer Science
Product (business)
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
Production (economics)
020201 artificial intelligence & image processing
Geometry and Topology
computer
Software
Data integration
Parametric statistics
Subjects
Details
- ISSN :
- 14337479 and 14327643
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- Soft Computing
- Accession number :
- edsair.doi...........ecad3d8fdd23c228bf5e7d3c1224d5df