Multi-Objective Optimization of Injection Molding Parameters for Manufacturing Thin-Walled Composite Connector Terminals
Abstract
:1. Introduction
2. Methodology
2.1. Simulation Model
- 3D CAD model of injection mold: Identify the research subject and conduct an initial analysis of the dimensional structure and characteristics of the model.
- Finite element model of injection molding: Creating a finite element model before simulating injection molding involves several steps.
- Plastic injection molding simulation: Utilize the commercial software Moldflow for conducting injection molding simulations to acquire comprehensive CAE feature data of injection-molded components.
- Data optimization: Aggregate simulation outcomes pertaining to warpage and volume shrinkage across diverse process parameters. Perform sensitivity analysis on parameters and undertake multi-objective optimization on the collected data.
- Model simplification: Optimizing the 3D CAD model is imperative prior to meshing. Simplification plays a pivotal role in achieving equilibrium between the overall mesh quantity and quality of the model. By eliminating features with minimal impact, the model is optimized to enhance the efficiency of injection molding simulation analysis. This approach ensures the simulation process retains accuracy and depth while circumventing unnecessary computational intricacies associated with features that exert minimal influence on overall quality characteristics [24].
- Mesh division: The utilization of double-layer meshes for injection molding simulation analysis, particularly applicable to thin-walled components with width-to-thickness ratios surpassing 4, represents a pragmatic and efficacious methodology. Employing double-layer meshes also recognized as shell elements, proves advantageous in augmenting computational efficiency and adhering to the specialized demands of thin-walled components.
- Boundary conditions: In the injection molding process, the primary consideration revolves around gate positioning and the number of gates. Varied gate positions yield distinct directions and lengths of molten flow, directly impacting stress distribution and shrinkage alterations on the surface of injection-molded parts. Maintaining equilibrium in melt flow aids in averting localized overpressure occurrences and facilitates warping deformation control. At the same time, straightforward parts allow for gate positioning based on experience or precedent cases, complex structured parts pose challenges in effectively pinpointing the optimal gate position.
- Material characteristics: The Moldflow material library categorizes the accuracy of materials used in filling, pressure holding, and warping analysis through a quality indicator. When dealing with specialized materials, it becomes imperative to reconstruct mathematical models to uphold simulation accuracy.
- Solver calculation: Upon finalizing the mold structure analysis model, the next step involves utilizing the finite element solver embedded within the commercial software for computation.
- Simulation analysis results: After completing the simulation calculation, it is imperative to analyze the variations and distribution of warpage and volume shrinkage. Initially, validate the simulation results to identify evident defects such as insufficient filling, weld lines, and air entrapment.
2.2. Parameter Optimization
- Determine the reference sequence and comparison sequence
- 2.
- Data standardization
- 3.
- Grey correlation coefficient calculation
- 4.
- Grey correlation degree calculation
3. Case Study
3.1. Injection Molding Simulation
3.2. Taguchi Orthogonal Arrays
4. Results and Discussion
4.1. ANOVA Analysis
4.2. The Influence of Process Parameters on Shrinkage
4.3. The Influence of Process Parameters on Warping
4.4. Grey Relational Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Liu, S.J.; Chen, Y.S. The manufacturing of thermoplastic composite parts by water-assisted injection-molding technology. Compos. Part A Appl. Sci. Manuf. 2004, 35, 171–180. [Google Scholar] [CrossRef]
- Fu, H.; Xu, H.; Liu, Y.; Yang, Z.; Kormakov, S.; Wu, D.; Sun, J. Overview of injection molding technology for processing polymers and their composites. ES Mater. Manuf. 2020, 8, 3–23. [Google Scholar] [CrossRef]
- Khosravani, M.R.; Nasiri, S. Injection molding manufacturing process: Review of case-based reasoning applications. J. Intell. Manuf. 2020, 31, 847–864. [Google Scholar] [CrossRef]
- Zhao, N.Y.; Lian, J.Y.; Wang, P.F.; Xu, Z.B. Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: A review. Int. J. Adv. Manuf. Technol. 2022, 120, 85–101. [Google Scholar] [CrossRef] [PubMed]
- Walale, A.; Chauhan, A.S.; Satyanarayana, A.; Venkatachalam, G.; Pradyumna, R. Analysis of shrinkage & warpage in ceramic injection molding of HPT vane leading edge core of a gas turbine casting. Mater. Today Proc. 2018, 5, 19471–19479. [Google Scholar]
- Pachorkar, P.; Singh, G.; Agarwal, N.; Srivastava, A. Multi response optimization of injection moulding process to reduce sink marks and cycle time. Mater. Today Proc. 2023, 72, 1089–1093. [Google Scholar] [CrossRef]
- Guerra, N.B.; Reis, T.M.; Scopel, T.; de Lima, M.S.; Figueroa, C.A.; Michels, A.F. Influence of process parameters and post-molding condition on shrinkage and warpage of injection-molded plastic parts with complex geometry. Int. J. Adv. Manuf. Technol. 2023, 128, 479–490. [Google Scholar] [CrossRef]
- Karagöz, İ. An effect of mold surface temperature on final product properties in the injection molding of high-density polyethylene materials. Polym. Bull. 2021, 78, 2627–2644. [Google Scholar] [CrossRef]
- Gim, J.; Han, E.; Rhee, B.; Friesenbichler, W.; Gruber, D.P. Causes of the gloss transition defect on high-gloss injection-molded surfaces. Polymers 2020, 12, 2100. [Google Scholar] [CrossRef]
- Wang, B.W.; Nian, S.C.; Huang, M.S. Strategies for adjusting process parameters in CAE simulation to meet real injection molding condition of screw positions and cavity pressure curves. Int. J. Adv. Manuf. Technol. 2022, 122, 1339–1351. [Google Scholar] [CrossRef]
- Kuram, E.; Tasci, E.; Altan, A.I.; Medar, M.M.; Yilmaz, F.; Ozcelik, B. Investigating the effects of recycling number and injection parameters on the mechanical properties of glass-fibre reinforced nylon 6 using Taguchi method. Mater. Des. 2013, 49, 139–150. [Google Scholar] [CrossRef]
- Xu, G.; Yang, Z.T.; Long, G.D. Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization. Int. J. Adv. Manuf. Technol. 2012, 58, 521–531. [Google Scholar] [CrossRef]
- Hentati, F.; Hadriche, I.; Masmoudi, N.; Bradai, C. Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation. Int. J. Adv. Manuf. Technol. 2019, 104, 4353–4363. [Google Scholar] [CrossRef]
- Lin, W.C.; Fan, F.Y.; Huang, C.F.; Shen, Y.K.; Wang, H. Analysis of the warpage phenomenon of micro-sized parts with precision injection molding by experiment, numerical simulation, and grey theory. Polymers 2022, 14, 1845. [Google Scholar] [CrossRef] [PubMed]
- Bademlioglu, A.H.; Canbolat, A.S.; Kaynakli, O. Multi-objective optimization of parameters affecting Organic Rankine Cycle performance characteristics with Taguchi-Grey Relational Analysis. Renew. Sustain. Energy Rev. 2020, 117, 109483. [Google Scholar] [CrossRef]
- Gomes, G.F.; de Almeida, F.A.; da Silva Lopes Alexandrino, P.; da Cunha, S.S.; de Sousa, B.S.; Ancelotti, A.C. A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation. Eng. Comput. 2019, 35, 519–535. [Google Scholar] [CrossRef]
- Cao, Y.; Fan, X.; Guo, Y.; Li, S.; Huang, H. Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods. J. Polym. Eng. 2020, 40, 360–371. [Google Scholar] [CrossRef]
- Moayyedian, M.; Dinc, A.; Mamedov, A. Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques. Polymers 2021, 13, 4158. [Google Scholar] [CrossRef] [PubMed]
- Zhou, H.; Zhang, S.; Wang, Z. Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method. Int. J. Adv. Manuf. Technol. 2021, 114, 423–449. [Google Scholar] [CrossRef]
- Li, S.; Fan, X.; Huang, H.; Cao, Y. Multi-objective optimization of injection molding parameters, based on the Gkriging-NSGA-vague method. J. Appl. Polym. Sci. 2020, 137, 48659. [Google Scholar] [CrossRef]
- Zeng, W.; Yi, G.; Zhang, S.; Wang, Z. Multi-objective optimization method of injection molding process parameters based on hierarchical sampling and comprehensive entropy weights. Int. J. Adv. Manuf. Technol. 2024, 133, 1481–1499. [Google Scholar] [CrossRef]
- Cao, Y.; Fan, X.; Guo, Y.; Ding, W.; Liu, X.; Li, C. Multi-objective optimization of injection molding process parameters based on BO-RFR and NSGAII methods. Int. Polym. Process. 2023, 38, 8–18. [Google Scholar] [CrossRef]
- Feng, Q.; Liu, L.; Zhou, X. Automated multi-objective optimization for thin-walled plastic products using Taguchi, ANOVA, and hybrid ANN-MOGA. Int. J. Adv. Manuf. Technol. 2020, 106, 559–575. [Google Scholar] [CrossRef]
- Peng, Y.; Li, M.; Yang, X. Void formation and suppression in CFRP laminate using newly designed ultrasonic vibration assisted RTM technique. Compos. Struct. 2024, 329, 117796. [Google Scholar] [CrossRef]
- Tzeng, C.J.; Lin, Y.H.; Yang, Y.K.; Jeng, M.C. Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. J. Mater. Process. Technol. 2009, 209, 2753–2759. [Google Scholar] [CrossRef]
- Peng, Y.; Li, M.; Yang, X.; Li, J.; Xiong, C. Quasi-static crush response of lightweight sandwich composite tubes fabricated via newly designed internal thermal expansion technique. Compos. Sci. Technol. 2024, 251, 110574. [Google Scholar] [CrossRef]
- Tosun, N.; Pihtili, H. The effect of cutting parameters on wire crater sizes in wire EDM. Int. J. Adv. Manuf. Technol. 2003, 21, 857–865. [Google Scholar] [CrossRef]
Type | Number | Side Length | Aspect Ratio | Matching |
---|---|---|---|---|
Triangle | 49,966 | 0.4 mm | Max: 19.13 Min: 1.16 Average: 1.61 | Matching: 92.8% Mutual: 95.3 |
Material Data | Value |
---|---|
Melt/Solid Density [cm3] | 1.34 g/1.16 |
Poisson’s ratio v12/v23 | 0.384/0.52 |
Shear modulus [MPa] | 1947 |
Average modulus of elasticity [MPa] | 5877.29 |
Tensile modulus [MPa] | 7900 |
Recommended melt temperature [°C] | 265–290 |
Recommended mold temperature [°C] | 60–80 |
Levels | A Melt_T | B Mold_T | C Filling_t | D Holding_P | E Holding_t | F Cooling_t |
---|---|---|---|---|---|---|
1 | 268 °C | 60 °C | 0.2 s | 60 Mpa | 0.5 s | 30 s |
2 | 278 °C | 70 °C | 0.4 s | 70 Mpa | 1 s | 40 s |
3 | 288 °C | 80 °C | 0.6 s | 80 Mpa | 1.5 s | 50 s |
Runs | Factors | Runs | Factors | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | A | B | C | D | E | F | ||
1 | 1 | 1 | 1 | 1 | 1 | 1 | 15 | 2 | 2 | 3 | 1 | 3 | 1 |
2 | 1 | 1 | 1 | 1 | 2 | 2 | 16 | 2 | 3 | 1 | 2 | 1 | 2 |
3 | 1 | 1 | 1 | 1 | 3 | 3 | 17 | 2 | 3 | 1 | 2 | 2 | 3 |
4 | 1 | 2 | 2 | 2 | 1 | 1 | 18 | 2 | 3 | 1 | 2 | 3 | 1 |
5 | 1 | 2 | 2 | 2 | 2 | 2 | 19 | 3 | 1 | 3 | 2 | 1 | 3 |
6 | 1 | 2 | 2 | 2 | 3 | 3 | 20 | 3 | 1 | 3 | 2 | 2 | 1 |
7 | 1 | 3 | 3 | 3 | 1 | 1 | 21 | 3 | 1 | 3 | 2 | 3 | 2 |
8 | 1 | 3 | 3 | 3 | 2 | 2 | 22 | 3 | 2 | 1 | 3 | 1 | 3 |
9 | 1 | 3 | 3 | 3 | 3 | 3 | 23 | 3 | 2 | 1 | 3 | 2 | 1 |
10 | 2 | 1 | 2 | 3 | 1 | 2 | 24 | 3 | 2 | 1 | 3 | 3 | 2 |
11 | 2 | 1 | 2 | 3 | 2 | 3 | 25 | 3 | 3 | 2 | 1 | 1 | 3 |
12 | 2 | 1 | 2 | 3 | 3 | 1 | 26 | 3 | 3 | 2 | 1 | 2 | 1 |
13 | 2 | 2 | 3 | 1 | 1 | 2 | 27 | 3 | 3 | 2 | 1 | 3 | 2 |
14 | 2 | 2 | 3 | 1 | 2 | 3 | - | - | - | - | - | - | - |
Source | f | Seq SS | Adj SS | Adj MS | F | p | Contribution/% |
---|---|---|---|---|---|---|---|
A | 2 | 0.0941 | 0.0941 | 0.0471 | 1.17 | 0.339 | 0.85 |
B | 2 | 0.5261 | 0.5261 | 0.2631 | 6.54 | 0.01 | 4.74 |
C | 2 | 2.2688 | 2.2688 | 1.1344 | 28.21 | 0 | 20.43 |
D | 2 | 0.1114 | 0.1114 | 0.0557 | 1.39 | 0.282 | 1.00 |
E | 2 | 7.3650 | 7.3650 | 3.6825 | 91.59 | 0 | 66.32 |
F | 2 | 0.1763 | 0.1763 | 0.0882 | 2.19 | 0.148 | 1.59 |
Error | 14 | 0.5629 | 0.5629 | 0.0402 | 5.07 | ||
Total | 26 | 11.1047 | 100.00 |
Source | f | Seq SS | Adj SS | Adj MS | F | p | Contribution/% |
---|---|---|---|---|---|---|---|
A | 2 | 0.0003010 | 0.0003010 | 0.0001505 | 264.91 | 0 | 17.56 |
B | 2 | 0.0001985 | 0.0001985 | 0.0000992 | 174.68 | 0 | 11.58 |
C | 2 | 0.0007838 | 0.0007838 | 0.0003919 | 689.82 | 0 | 45.73 |
D | 2 | 0.0004210 | 0.0004210 | 0.0002105 | 370.51 | 0 | 24.56 |
E | 2 | 0.0000015 | 0.0000015 | 0.0000007 | 1.29 | 0.306 | 0.09 |
F | 2 | 0.0000003 | 0.0000003 | 0.0000001 | 0.26 | 0.773 | 0.02 |
Error | 14 | 0.0000080 | 0.0000080 | 0.0000006 | 0.47 | ||
Total | 26 | 0.0017141 | 100.00 |
Runs | GRC | GRG | Runs | GRC | GRG | ||
---|---|---|---|---|---|---|---|
Warpage | Shrinkage | Warpage | Shrinkage | ||||
1 | 0.44 | 0.46 | 0.45 | 15 | 0.53 | 0.77 | 0.65 |
2 | 0.45 | 0.56 | 0.51 | 16 | 0.53 | 0.77 | 0.65 |
3 | 0.45 | 0.53 | 0.49 | 17 | 0.37 | 0.34 | 0.35 |
4 | 0.52 | 0.44 | 0.48 | 18 | 0.35 | 0.54 | 0.45 |
5 | 0.51 | 0.64 | 0.58 | 19 | 0.35 | 0.55 | 0.45 |
6 | 0.51 | 0.64 | 0.58 | 20 | 0.62 | 0.47 | 0.55 |
7 | 1.00 | 0.48 | 0.74 | 21 | 0.60 | 1.00 | 0.80 |
8 | 0.94 | 0.86 | 0.90 | 22 | 0.60 | 1.00 | 0.80 |
9 | 0.94 | 0.86 | 0.90 | 23 | 0.39 | 0.33 | 0.36 |
10 | 0.68 | 0.43 | 0.56 | 24 | 0.43 | 0.53 | 0.48 |
11 | 0.69 | 0.84 | 0.77 | 25 | 0.40 | 0.65 | 0.53 |
12 | 0.69 | 0.84 | 0.76 | 26 | 0.34 | 0.33 | 0.34 |
13 | 0.55 | 0.48 | 0.51 | 27 | 0.33 | 0.61 | 0.47 |
14 | 0.44 | 0.46 | 0.45 | - | - | - | - |
Source | f | Seq SS | Adj SS | Adj MS | F | p | Contribution % |
---|---|---|---|---|---|---|---|
A | 2 | 0.0326 | 0.0326 | 0.01631 | 10.18 | 0.002 | 4.75 |
B | 2 | 0.0457 | 0.0457 | 0.02285 | 14.25 | 0 | 6.65 |
C | 2 | 0.3308 | 0.3308 | 0.16539 | 103.18 | 0 | 48.17 |
D | 2 | 0.1151 | 0.1151 | 0.05755 | 35.9 | 0 | 16.76 |
E | 2 | 0.1376 | 0.1376 | 0.06879 | 42.92 | 0 | 20.03 |
F | 2 | 0.0025 | 0.0025 | 0.00125 | 0.78 | 0.477 | 0.36 |
Error | 14 | 0.0224 | 0.0224 | 0.00160 | - | - | 3.27 |
Total | 26 | 0.6867 | - | - | - | - | 100.00 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tan, M.; Peng, S.; Huo, Y.; Li, M. Multi-Objective Optimization of Injection Molding Parameters for Manufacturing Thin-Walled Composite Connector Terminals. Materials 2024, 17, 3949. https://doi.org/10.3390/ma17163949
Tan M, Peng S, Huo Y, Li M. Multi-Objective Optimization of Injection Molding Parameters for Manufacturing Thin-Walled Composite Connector Terminals. Materials. 2024; 17(16):3949. https://doi.org/10.3390/ma17163949
Chicago/Turabian StyleTan, Mingbo, Size Peng, Yingfei Huo, and Maojun Li. 2024. "Multi-Objective Optimization of Injection Molding Parameters for Manufacturing Thin-Walled Composite Connector Terminals" Materials 17, no. 16: 3949. https://doi.org/10.3390/ma17163949