Research on the Rapid Prototyping Process Optimization Technology of Complex Heterogeneous Mould Oriented by Manufacturing Big Data

Authors

  • Yan Cao
  • Liang Huang
  • Yu Bai
  • Jiang Du

DOI:

https://doi.org/10.57118/creosar/978-1-915740-01-4_7

Keywords:

Complex Shaped Mould, FDM Rapid Prototyping Technology, Cloud Manufacturing, Big Data Management, Forming Parameters

Abstract

In order to meet the flexible market needs of complex heterogeneous moulds in recent years, and for slove the defects of small batch and multi-variety moulds in traditional CNC milling. This chapter proposes an additive mould manufacturing method based on Fused Deposition Modelling (FDM) rapid forming technology, which can further reduce the cost and cycle of such mould manufacturing while ensuring the quality of moulds. This method first combines with the cloud manufacturing design concept, and constructs the qualitative relationship between the process data and the forming quality and forming efficiency in the FDM overall forming process; Secondly, according to the forming mechanism model, construct a quantitative mapping model between process parameters on forming quality and efficiency; Finally, through the complex mould digital intelligent manufacturing system under the guidance of cloud manufacturing big data, to lay the foundation and contribution to the future cloud manufacturing technology of complex and heterogeneous products.

References

M. Bermudez, O. Gomozov, X. Kestelyn, et al, “Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines,” Mathematics and Computers in Simulation, vol. 158, pp. 148-161, 2019

G. Wallace, S. Polcyn, P.P. Brooks, et al, “RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI,” NeuroImages, vol. 257, pp. 1-10, 2022

A. Sharma, A. Rai “A Fused deposition modelling (FDM) based 3D & 4D Printing: A state of art review,” Materials Today Proceedings, 2021, vol. 52, pp. 1-15

M. Doshi, A. Mahale, S. K. Singh, “Printing parameters and materials affecting mechanical properties of FDM-3D printed Parts: Perspective and prospects,” Materials Today Proceedings, 2022, vol. 50, issue 5, pp. 2269-2275

A. Belhadi, K. Zkik, A. Cherrafi, “Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies,” Computers & Industrial Engineering, 2021, vol. 137, pp. 5-10

M. Hasan, K. Ogan, B. Starly, “Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency,” Procedia Manufacturing, 2021, vol. 53, pp. 594-605

D. Meiyou, Y. Ye, “Establishment of big data evaluation model for green and sustainable development of enterprises,” Journal of King Saud University - Science, 2022, vol. 34, issue 5, pp. 5-12

L. Huang, Y. Cao, F. Jia, et al, “Research on spur face gear by electrochemical machining based on the complex surface mesh,” Chaos, Solitons & Fractals, 2020, vol. 130, pp. 1-6

A. Patel, P. Shaky, “Spur gear crack modelling and analysis under variable speed conditions using variational mode decomposition,” Mechanism and Machine Theory, 2021, vol. 4164, pp. 5-12

R. W. Joliat, M. Tschamper, R. Kontic, “Water-soluble sacrificial 3D printed molds for fast prototyping in ceramic injection molding,” Additive Manufacturing, 2021, vol. 48, pp. 55-62

A. Arora, A. Pathak, A. Juneja, “Design and analysis of multi cavity injection mould using solidworks,” Materials Today Proceedings, 2022, vol. 56, issue 6, pp. 3648-3650

A. Phogat, D. Chhabra, et al, “Analysis of wear assessment of FDM printed specimens with PLA, multi-material and ABS via hybrid algorithms,” Materials Today Proceedings, 2022, vol. 56, pp. 11-15

M. Hallmann, S. Goetz, B. Schleich, “Mapping of GD&T information and PMI between 3D product models in the STEP and STL format,” Computer-Aided Design, 2019, vol. 115, pp. 293-306

T. L. Leirmoa, O.Semeniuta, K. Martinsen, “Tolerancing from STL data: A Legacy Challenge,” Procedia CIRP, 2020, vol. 92, pp. 218-223

S. Khan, K. Joshi, S. Deshmukh, “A comprehensive review on effect of printing parameters on mechanical properties of FDM printed parts,” Materials Today Proceeding, 2022, vol. 50, issue 5, pp. 2119-2127

A. N. Kivia, M. R. Ayatollahi, P. Rezaeian, et al, “Investigating the effect of printing speed and mode mixity on the fracture behavior of FDM-ABS specimens,” Theoretical and Applied Fracture Mechanics, 2021, vol. 118, pp. 27-30

J. Singh, et al, “Effect of filling percentage and raster style on tensile behavior of FDM produced PLA parts at different build orientation,” Materials Today Proceedings, 2022, vol. 56, pp. 391-400

A. Szust, G. Adamski, “Using thermal annealing and salt remelting to increase tensile properties of 3D FDM prints,” Engineering Failure Analysis, 2022, vol. 132, pp. 1231-1238

R. Patel,, C. Desai,, S. Kushwah, “A review article on FDM process parameters in 3D printing for composite materials,” Materials Today Proceedings, 2022, vol. 56, pp. 2162-2166

R. Wichniarek, A. Hamrol, W.Kuczko, et al, “ABS filament moisture compensation possibilities in the FDM process,” CIRP Journal of Manufacturing Science and Technology, 2021, vol. 35, pp. 550-559

Published

2022-11-24

How to Cite

Cao, Y., Huang, L., Bai, Y., & Du, J. (2022). Research on the Rapid Prototyping Process Optimization Technology of Complex Heterogeneous Mould Oriented by Manufacturing Big Data. Artificial Intelligence Impressions, 1, 135–156. https://doi.org/10.57118/creosar/978-1-915740-01-4_7