Developing Jewelry Manufacturing Using Software Simulation, A New Solution for Developing Design and Manufacturing
a new solution for developing design and manufacturing
DOI:
https://doi.org/10.71350/jmis.4Keywords:
Gold Manufacturing, Jewelry Manufacturing, Software Simulation, Click 2CastAbstract
This study investigates the application of software-based casting simulation for improving the design and manufacturing performance of gold jewelry casting. A cast gold ring was analyzed to evaluate molten metal flow behavior, contour fill pressure, cold shut formation, macro porosity, and Niyama factor distribution during solidification. The simulation results showed that contour fill pressure ranged from 0.138 to 1.26 MPa, indicating non-uniform mold filling behavior caused by differences in geometry, cross-sectional thickness, and thermal conditions. Thin and intricate regions exhibited lower filling pressures and rapid heat loss, increasing the probability of cold shut formation and incomplete filling defects. The Niyama factor values ranged from 0.0064 to 0.32 (°C·s)0.5·mm-1, revealing non-uniform solidification behavior throughout the casting. Low Niyama values were associated with regions susceptible to shrinkage porosity because of insufficient feeding and unfavorable thermal gradients. The study also compared the casting behavior of 18 karat and 22 karat gold alloys. The results showed that the 22 karat alloy exhibited slightly lower cold shut values due to its improved fluidity and lower flow resistance. Furthermore, increasing the casting temperature of the 22 karat alloy from 1022 °C to 1050 °C reduced cold shut formation from 0.1429 to 0.1040 and decreased macro porosity volume by 4.4%, owing to enhanced molten metal flow and improved feeding behavior during solidification. Higher temperatures also increased the Niyama factor, indicating more favorable solidification conditions and reduced shrinkage defect tendency. Overall, the findings demonstrate that casting simulation is an effective engineering tool for predicting manufacturing defects and optimizing process parameters before production. The integration of simulation into jewelry manufacturing can improve product quality, reduce material waste and production costs, and support more efficient and sustainable manufacturing systems.
References
[1] Dauriz, L., Remy, N., & Tochtermann, T. (2014). A multifaceted future: The jewelry industry in 2020. McKinsey & Company. http://www.mckinsey.com/industries/retail/our-insights/amultifaceted-future-the-jewelry-industry-in-2020
[2] Karo, M. (1968). The US jewelry industry. Financial Analysts Journal, 24(2), 49–56. https://doi.org/10.2307/4470291
[3] Tofail, S. A., Koumoulos, E. P., Bandyopadhyay, A., Bose, S., O’Donoghue, L., & Charitidis, C. (2018). Additive manufacturing: Scientific and technological challenges, market uptake and opportunities. Materials Today, 21(1), 22–37. https://doi.org/10.1016/j.mattod.2017.07.001
[4] Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M. K., Gaur, V., Krolczyk, G. M., & Wu, C. (2023). Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control. Journal of Intelligent Manufacturing, 34(1), 21–55. https://doi.org/10.1007/s10845-022-02032-1
[5] Ashby, M. F., & Johnson, K. (2013). Materials and design: The art and science of material selection in product design (3rd ed.). Butterworth-Heinemann
[6] Tan, Q., & Li, H. (2024). Application of computer aided design in product innovation and development: Practical examination on taking the industrial design process. IEEE Access, 12, 85622–85634. https://doi.org/10.1109/ACCESS.2024.3444007
[7] Bi, Z., & Wang, X. (2020). Computer aided design and manufacturing. John Wiley & Sons
[8] Supsomboon, S. (2019). Simulation for jewelry production process improvement using line balancing: A case study. Management Systems in Production Engineering, 27(3), 127–137. https://doi.org/10.1515/mspe-2019-0021
[9] Fischer-Bühner, J. (2006, September). Computer simulation of jewelry investment casting: What can we expect? [Conference presentation]. The Santa Fe Symposium on Jewelry Manufacturing Technology 2006, Santa Fe, NM, United States
[10] Abdel-Rahman, A. A. R., Sabry, A. M., & Abd al-Nabi, S. M. (2025). Simulation of jewelry forming processes using smart materials. International Design Journal, 15(1), 47–57. https://doi.org/10.21608/idj.2025.334455
[11] Seth, A., Vance, J. M., & Oliver, J. H. (2011). Virtual reality for assembly methods prototyping: A review. Virtual Reality, 15(1), 5–20. https://doi.org/10.1007/s10055-009-0143-y
[12] Jain, N., Carlson, K. D., & Beckermann, C. (2007). Round robin study to assess variations in casting simulation Niyama criterion predictions [Paper presentation]. 61st SFSA Technical and Operating Conference, Chicago, IL, United States
[13] Khaled, I. (2013). Prediction of shrinkage porosity in Ti-46Al-8Nb tilt-casting using the Niyama criterion function. International Journal of Metalcasting, 7(4), 35–42. https://doi.org/10.1007/BF03355562
[14] Ashkani, O., Kamali, A. H., Sadeq, A. M., Neyestanaki, S. T., & Jordovic, B. (2025). Casting of valve housing with aluminum alloy, competition between simulation and actual prototype manufacturing. Transactions of the Indian Institute of Metals, 78(12), 270–279. https://doi.org/10.1007/s12666-024-03456-
[15] Kunter, R. (2001). Gold: Alloying, properties, and applications. In K. H. J. Buschow, R. W. Cahn, M. C. Flemings, B. Ilschner, E. J. Kramer, S. Mahajan, & P. Veyssière (Eds.), Encyclopedia of materials: Science and technology (pp. 3593–3596). Elsevier
[16] Valencia, J. J., & Quested, P. N. (2008). Thermophysical properties. In ASM Handbook: Casting (Vol. 15, pp. 468–481). ASM International
[17] Tijana, A., Valentina, V., Nataša, T., Miloš, H. M., Suzana, G. A., Milica, B., ... & Rebeka, R. (2021). Mechanical properties of new denture base material modified with gold nanoparticles. Journal of Prosthodontic Research, 65(2), 155–161. https://doi.org/10.2186/jpr.JPOR_2019_40
[18] Lifeng, D., Chukun, Q., Linhua, Z., Qiang, L., Peiyu, G., Jinhao, N., & Hongmei, D. (2025). Simulation of electroplating process and influence mechanism for repairing inner and outer surfaces of a gold cup based on COMSOL multiphysics. Results in Engineering, 25, Article 106691. https://doi.org/10.1016/j.rineng.2024.106691
[19] Pei, X., Hou, H., & Zhao, Y. (2025). A review of intelligent design and optimization of metal casting processes. Acta Metallurgica Sinica (English Letters), 38(8), 1293–1311. https://doi.org/10.1007/s40195-024-01712-w
[20] Yang, J., Liu, B., Shu, D., Yang, Q., & Hu, T. (2025). Local stress/strain field analysis of die-casting Al alloys via 3D model simulation with realistic defect distribution and RVE modelling. Engineering Failure Analysis, 168, Article 109104. https://doi.org/10.1016/j.engfailanal.2024.109104
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of Manufacturing and Intelligent Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
