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Symbolic semantic design of industrial products based on Big data technology

   | 27 sept. 2023
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Castellucci, H. E., Viviani, C., Arezes, P., et al. (2020). Applied anthropometry for common industrial settings design: Working and ideal manual handling heights. International Journal of Industrial Ergonomics, 102963.Search in Google Scholar

Cignitti, S., Mansouri, S., Woodley, J. M., et al. (2018). Systematic Optimization-Based Integrated Chemical Product–Process Design Framework. Industrial & Engineering Chemistry Research, acs.iecr.7b04216.Search in Google Scholar

Ferreira, V. N., Cupertino, A. F., Pereira, H. A., et al. (2018). Design and Selection of High Reliability Converters for Mission Critical Industrial Applications: A Rolling Mill Case Study. IEEE Transactions on Industry Applications, 1-1.Search in Google Scholar

Auernhammer, J., Roth, B. (2021). The origin and evolution of Stanford University’s design thinking: From product design to design thinking in innovation management. Journal of Product Innovation Management, 38(6), 623-644.Search in Google Scholar

Bhla, B., Csl, B., Hu, L. B., et al. (2020). Industrial design and implementation of a large-scale dual-axis sun tracker with a vertical-axis-rotating-platform and multiple-row-elevation structures - ScienceDirect. Solar Energy, 199, 596-616.Search in Google Scholar

Repka, M. A., Butreddy, A., Bandari, S. (2020). Quality-by-design in hot melt extrusion based amorphous solid dispersions: An industrial perspective on product development. European Journal of Pharmaceutical Sciences,158.Search in Google Scholar

Shi, C., Reilly, L. T., Kumar, V. S. P., et al. (2021). Design principles for intrinsically circular polymers with tunable properties. Chem, 7(11), 2896-2912.Search in Google Scholar

Aheleroff, S., Xu, X., Zhong, R. Y., et al. (2021). Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Advanced Engineering Informatics, 47, 101225.Search in Google Scholar

Hr, A., Sr, B., Eag, C., et al. (2021). Additive manufacturing in drug delivery: Innovative drug product design and opportunities for industrial application. Advanced drug delivery reviews, 178, 113990.Search in Google Scholar

Wang, T. (2020). A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction. International Journal of Industrial Ergonomics, 76.Search in Google Scholar

Azman, M. A., Asyraf, M. R. M., Khalina, A., et al. (2021). Natural fiber reinforced composite material for product design: A short review. Polymers, 13(12), 1917.Search in Google Scholar

Wang, Y., Luo, L., Liu, H.. (2020). Bridging the Semantic Gap Between Customer Needs and Design Specifications Using User-Generated Content. IEEE Transactions on Engineering Management, PP(99), 1-13.Search in Google Scholar

Frutiger, J., Cignitti, S., Abildskov, J., et al. (2019). Computer-aided molecular product-process design under property uncertainties - A Monte Carlo based optimization strategy. Computers & Chemical Engineering, 122(MAR.4), 247-257.Search in Google Scholar

Kadir, B. A., Broberg, O. (2021). Human-centered design of work systems in the transition to industry 4.0. Applied ergonomics, 92, 103334.Search in Google Scholar

Müller, J. M., Buliga, O., Voigt, K. I. (2021). The role of absorptive capacity and innovation strategy in the design of industry 4.0 business Models-A comparison between SMEs and large enterprises. European Management Journal, 39(3), 333-343.Search in Google Scholar

Lo, C. K., Chen, C. H., Zhong, R. Y. (2021). A review of digital twin in product design and development. Advanced Engineering Informatics, 48, 101297.Search in Google Scholar

Enyoghasi, C., Badurdeen, F. (2021). Industry 4.0 for sustainable manufacturing: Opportunities at the product, process, and system levels. Resources, conservation and recycling, 166, 105362.Search in Google Scholar

Lougheed, J. P., Vlisides-Henry, R. D., Crowell, S. E. (2021). Advancing Models and Methods of Emotional Concordance. Biological Psychology, 162(4), 108112.Search in Google Scholar

Feng, K., Yang, L., Su, B., Feng, W., & Wang, L. (2021). An integration model for converter molten steel end temperature prediction based on bayesian formula. steel research international.Search in Google Scholar

Tellaeche, Iglesias, A., Campos, Anaya, M. Á., Pajares, Martinsanz, G., et al. (2021). On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures. Sensors, 21(10), 3339.Search in Google Scholar

eISSN:
2444-8656
Langue:
Anglais
Périodicité:
Volume Open
Sujets de la revue:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics