Optimization Design and Research of Injection Molding Process of Raspberry + Pi+2+ Shell

DOI: https://doi.org/10.62517/jiem.202503303

Author(s)

Dengping Hu1, Qian Leng2,*, Dandan Wang3, Longwei Zhong1, Zesi Yao1, Han Zhang1

Affiliation(s)

1Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, Guangdong, China 2Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China 3School of Foreign Studies, Guangzhou University, Guangzhou, Guangdong, China *Corresponding Author

Abstract

Raspberry Pi+ Pi+2+ shell is composed of a typical plate shell appearance plastic body, which is a thin-walled plastic part with holes. When the surface size of the plastic part changes during injection molding, it is easy to warpage deformation, which seriously affects the surface quality and assembly accuracy of the plastic part. In order to make the product more appropriate to the actual engineering results, this study uses a solid grid with higher calculation accuracy to simulate it. In order to improve cooling efficiency, the Raspberry Pi shell is cooled using a partition water channel and a form-following cooling channel. Using Moldflow software, the injection molding process of a thin-walled Raspberry Pi shell was simulated and analyzed. An orthogonal test table with 4 factors and 4 levels was designed, and 16 simulation analysis experiments were conducted on it. Pearson correlation analysis and range analysis in mathematical analysis methods were used to screen important influences and select the best injection molding scheme. The results show that the degree of influence of each factor on warping deformation is holding pressure > holding pressure time > cooling time > melt temperature. The optimal process parameters are A1B4C4D4, that is, the melt temperature is 200℃, the holding pressure is 60MPa, the holding time is 14s, and the cooling time is 22s. The maximum warpage deformation decreased from 0.8515mm before optimization to 0.6939mm after optimization, with a decrease of about 18.5%. Because the Raspberry PI is loaded with extremely small high-precision components, the dimensional tolerance assigned to each precision component will be reduced by one precision level, and the precision of the detection and control of precision components will be a qualitative leap.

Keywords

Raspberry Pi Shell; Thin-Walled Parts; Bulkhead Waterway; Form-Following Cooling Taguchi Test; Warping Deformation Optimization

References

[1].Hyie K M, Budin S, Wahab M. Effect of injection moulding parameters in reducing the shrinkage of polypropylene product using Taguchi analysis[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 505(1): 012060. [2].Wang B, Cai A. Influence of mold design and injection parameters on warpage deformation of thin-walled plastic parts[J]. Polimery, 2021, 66(5): 283-292. [3].Zhang J, Yin X, Liu F, et al. The simulation of the warpage rule of the thin-walled part of polypropylene composite based on the coupling effect of mold deformation and injection molding process[J]. Science and Engineering of Composite Materials, 2018, 25(3): 593-601. [4].Hamdi A. Assessing the suitability of various grades of polypropylene for injection molding through flow-length measurements[J]. Korea-Australia Rheology Journal, 2024, 36(1): 33-43. [5].Usman Jan Q M, Habib T, Noor S, et al. Multi response optimization of injection moulding process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage’s using integrated approach of S/N ratio and composite desirability function[J]. Cogent Engineering, 2020, 7(1): 1781424. [6].Li X, Wei Q, Li J, et al. Numerical simulation on crystallization-induced warpage of injection-molded PP/EPDM part[J]. Journal of Polymer Research, 2019, 26: 1-11. [7].Willerer T, Brinkmann T, Drechsler K. Development and Application of a Cooling Rate Dependent PVT Model for Injection Molding Simulation of Semi Crystalline Thermoplastics[J]. Polymers, 2024, 16(22): 3194. [8].Kumar S, Singh A K. Volumetric shrinkage estimation of benchmark parts developed by rapid tooling mold insert[J]. Sādhanā, 2020, 45: 1-9. [9].Lin W C, Fan F Y, Huang C F, et al. Analysis of the warpage phenomenon of micro-sized parts with precision injection molding by experiment, numerical simulation, and grey theory[J]. Polymers, 2022, 14(9): 1845. [10].Dastjerdi M, Dastjerdi A M A, Dastjerdi P M A, et al. Feasibility of developing green water batteries based on poly-lactic acid and polybutylene succinate: A computational study on groasis waterboxx technology[J]. Materials Today Communications, 2023, 36: 106537. [11].Saedon J B, Azlan M Z, Adenan M S, et al. CAE analysis for disposable mouth mirror based on autodesk moldflow plastic insight[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2020, 834(1): 012060. [12].Tsai H H, Wu S J, Liu J W, et al. Filling-balance-oriented parameters for multi-cavity molds in polyvinyl chloride injection molding[J]. Polymers, 2022, 14(17): 3483. [13].Abdul R, Guo G, Chen J C, et al. Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design[J]. International Journal on Interactive Design and Manufacturing (IJIDeM), 2020, 14: 345-357. [14].Kuo C F J, Su T L, Li Y C. Construction and analysis in combining the Taguchi method and the back propagation neural network in the PEEK injection molding process[J]. Polymer-Plastics Technology and Engineering, 2007, 46(9): 841-848. [15].Shen C, Wang L, Cao W, et al. Investigation of the effect of molding variables on sink marks of plastic injection molded parts using Taguchi DOE technique[J]. Polymer-Plastics Technology and Engineering, 2007, 46(3): 219-225.