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.
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Alazab M (2020) Cyber security researcher and practitioner with industry and academic experience. Cybernomics 2(1):56–56
Balaanand M, Karthikeyan N, Karthik S (2020) Designing a framework for communal software: based on the assessment using relation modeling. Int J Parallel Prog 48(2):329–343
Cai W, Lai KH, Liu C, Wei F, Ma M, Jia S et al (2019) Promoting the sustainability of the manufacturing industry through lean energy-saving and emission-reduction strategy. Sci Total Environ 665:23–32
Capriglione D, Carratu M, Pietrosanto A, Sommella P (2019) Online fault detection of rear stroke suspension sensor in a motorcycle. IEEE Trans Instrum Meas 68(5):1362–1372
Caraballo SC, Fernandez RA (2020) A performance-based design framework for enhancing decision-making at the conceptual phase of a motorcycle rear suspension development. Optim Eng 21(4):1283–1317
Farid A, Ksaibati K (2021) Modeling severities of motorcycle crashes using random parameters. J Traff Transp Eng (English Edition) 8(2):225–236
Fischer D, Harbrecht A, Surmann A, McKenna R (2019) Electric vehicles’ impacts on residential electric local profiles—a stochastic modeling approach considering socio-economic, behavioural and spatial factors. Appl Energy 233:644–658
Frizziero L, Liverani A, Nannini L (2019) Design for six sigma (DFSS) applied to a new eco-motorbike. Machines 7(3):52
Gao X, Lee GM (2019) Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning. Comput Ind Eng 128:60–69
Huang HB, Wu JH, Huang XR, Yang ML, Ding WP (2020) A generalized inverse cascade method to identify and optimize vehicle interior noise sources. J Sound Vib 467:115062
Jin JG, Nieto H, Lu L (2020) Robust bike-sharing stations allocation and path network design: a two-stage stochastic programming model. Transp Lett 12(10):682–691
Julsrud TE, Farstad E (2020) Car sharing and transformations in households travel patterns: insights from emerging proto-practices in Norway. Energy Res Soc Sci 66:101497
Kumar A, Abhishek K, Nerurkar P, Ghalib MR, Shankar A, Cheng X (2020) Secure smart contracts for cloud-based manufacturing using Ethereum blockchain. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4129
Leach F, Kalghatgi G, Stone R, Miles P (2020) The scope for improving the efficiency and environmental impact of internal combustion engines. Transp Eng 1:100005
Li J, Dai B, Li X, Xu X, Liu D (2019) A dynamic Bayesian network for vehicle maneuver prediction in highway driving scenarios: framework and verification. Electronics 8(1):40
Liu C, Wang G, Zhou H, Mei Y, Li Y, Li Y, Zhang L (2020) Design of two-wheeled motorcycle tire crown contour bioinspired by cat paw pads. Appl Bionics Biomech. https://doi.org/10.1155/2020/8898413
Liu Y, Zhang J, Li Y, Hansen P, Wang J (2021) Human–computer collaborative interaction design of intelligent vehicle—a case study of HMI of adaptive cruise control
Molano JIR, Lovelle JMC, Montenegro CE, Granados JJR, Crespo RG (2018) Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. J Ambient Intell Hum Comput 9(3):709–723
Polanía-Restrepo S, Jaramillo-González S, Osorio-Gómez G (2020) Electric hybridization kit for modification of a manual transmission motorcycle. Int J Interact Des Manuf IJIDeM. https://doi.org/10.1007/s12008-020-00649-w
Purwanto K, Hariadi TK, Muhtar MY (2019) Microcontroller-based RFID, GSM and GPS for motorcycle security system. Int J Adv Comput Sci Appl 10(3):447–451
Sornalakshmi M, Balamurali S, Venkatesulu M, Navaneetha Krishnan M, Ramasamy LK, Kadry S et al (2020) Hybrid method for mining rules based on enhanced Apriori algorithm with minimal sequential optimization in healthcare industry. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04862-2
Spanoudakis P, Christenas E, Tsourveloudis NC (2020) Design and structural analysis of a front single-sided swingarm for an electric three-wheel motorcycle. Appl Sci 10(17):6063
Trianni A, Cagno E, Accordini D (2019) Energy efficiency measures in electric motors systems: a novel classification highlighting specific implications in their adoption. Appl Energy 252:113481
Wu HC, Ai CH, Chang YY (2021) What drives experiential sharing intentions towards motorcycle touring? The case of Taiwan. J China Tour Res 17(1):90–119
Xiao H, Muthu B, Kadry SN (2020) Artificial intelligence with robotics for the advanced manufacturing industry using robot-assisted mixed-integer programming model. Intell Serv Robot 1–10
Xu X, Han M, Nagarajan SM, Anandhan P (2020) Industrial Internet of Things for smart manufacturing applications using hierarchical trustful resource assignment. Comput Commun 160:423–430
Zhang K, Chen Y, Li C (2019) Discovering the tourists’ behaviours and perceptions in a tourism destination by analyzing photos’ visual content with a computer deep learning model: the case of Beijing. Tour Manag 75:595–608
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This research was supported by the National Key R&D Program of China (Grant No. 2019YFE0197000).
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All the authors reviewed and approved the paper for submission. PW, XT, GW, JL, QS, CS, and YZ have contributed to the paper in terms of framework modeling, strategizing according to the analysis, and illustrating a highly efficient approach.
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Wang, P., Tan, X., Wang, G. et al. A digital modeling framework for the motorcycle industry with advanced computer design. Soft Comput 25, 12465–12476 (2021). https://doi.org/10.1007/s00500-021-05966-0
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DOI: https://doi.org/10.1007/s00500-021-05966-0