Abstract
Emerging technological advances are reshaping the casting sector in latest decades. Casting technology is evolving towards intelligent casting paradigm that involves automation, greenization and intelligentization, which attracts more and more attention from the academic and industry communities. In this paper, the main features of casting technology were briefly summarized and forecasted, and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed. Moreover, the technical visions of intelligent casting process were also put forward. The key technologies for intelligent casting process comprise 3D printing technologies, intelligent mold technologies and intelligent process control technologies. In future, the intelligent mold that derived from mold with sensors, control devices and actuators will probably incorporate the Internet of Things, online inspection, embedded simulation, decision-making and control system, and other technologies to form intelligent cyber-physical casting system, which may pave the way to realize intelligent casting. It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control, with the defects, microstructure, performance, and service life of the fabricated castings can be accurately predicted and tailored.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Lessiter M J, Kotzin E L. Timeline of casting technology. Modern Casting, 2002, 92(11): 43–49.
Stefanescu D M. A succinct history of metalcasting knowledge. International Journal of Metalcasting, 2023, 17(4): 2373–2388.
Jie W Q, Jian Z Y, Liu L, et al. Casting technology. Beijing: Higher Education Press, 2013. (In Chinese)
Luo A A. Advanced metal casting. In: Caballero F G. Encyclopedia of Materials: Metals and Alloys, Netherlands: Elsevier, 2022, 3: 13–26.
Metal casting technology roadmaps. Foundry Institution of Chinese Mechanical Engineering Society. Beijing: China Science and Technology Press, 2016. (In Chinese)
Lehmhus D. Advances in metal casting technology: A review of state of the art, challenges and trends-Part I: Changing markets, changing products. Metals, 2022, 12(11): 1959.
Chevrolet. Engine parts: LT/LS/LSX Production cylinder blocks Performance. https://www.chevrolet.com/performance-parts/components/engine/ls-lt-lsx-series-blocks/production-cylinder-blocks/, 2024, accessed 10 March 2024.
Chevrolet. Engine parts: LS/LT/LSX Cylinder heads ∣ Performance. https://www.chevrolet.com/performance-parts/components/engine/ls-lt-lsx-series-blocks/cylinder-heads/, 2024, accessed 10 March 2024.
Campbell J. Complete casting handbook. 2nd. ed. Oxford: Butterworth-Heinemann. Chapter 16, Casting, 2015: 821–882.
Madan J, Singh P P. Sustainability in foundry and metal casting industry. In: Ganesh Narayanan R, Gunasekera J S (eds.) Sustainable Manufacturing Processes, Netherlands: Academic Press, 2023: 29–52.
Khan M A A, Sheikh A K, Al-Shaer B S. Evolution of metal casting technologies-A historical perspective. 1st ed., Switzerland: Springer Cham, Springer Briefs in Applied Sciences and Technology, 2017, 1–42.
Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing. Engineering, 2018, 4(1): 11–20.
Liu X L. Innovation, intelligence and green development of foundry equipment in China. China Foundry Machinery & Technology, 2020, 55(2): 5–9. (In Chinese)
Xu Q Y. Metal casting technology roadmaps: digital, networked, intelligent casting. Foundry, 2017, 66(12): 1243–1250. (In Chinese)
Sachs E M, Haggerty S, Michael J, et al. Three-dimensional printing techniques. CA, US5340656, 1994.
Sachs E, Cima M, Cornie J, et al. Three-dimensional printing: The physics and implications of additive manufacturing. CIRP Annals, 1993, 42(1): 257–260.
Kang J, Ma Q. The role and impact of 3D printing technologies in casting. China Foundry, 2017, 14(3): 157–168.
Voxeljet A G. Print sand casting cores and molds from your CAD files. https://www.voxeljet.com/3d-printing-solution/sand-casting/, 2024, accessed 5 March 2024.
Almaghariz E S, Conner B P, Lenner L, et al. Quantifying the role of part design complexity in using 3D sand printing for molds and cores. International Journal of Metalcasting, 2016, 10(3): 240–252.
Pham D T, Dimov S S. Rapid prototyping: A time compression tool. Ingenia, 2003, 17: 43–48.
Yang J, Shi Y, Shen Q, et al. Selective laser sintering of HIPS and investment casting technology. Journal of Materials Processing Technology, 2009, 209(4): 1901–1908.
3D insider. What is FDM 3D printing? https://3dinsider.com/what-is-fdm/, 2024, accessed 6 March 2024.
Pal D K, Ravi B. Rapid tooling route selection and evaluation for sand and investment casting. Virtual and Physical Prototyping Journal, 2007, 2(4): 197–207.
Meta Cast Sdn Bhd. Mold and Core - Meta Cast Sdn Bhd, https://www.metacast.com.my/mold_n_core/, 2024, accessed 6 March 2024.
Wang D, Dong A, Zhu G, et al. Rapid casting of complex impeller based on 3D printing wax pattern and simulation optimization. International Journal of Advanced Manufacturing Technology, 2019, 100(9–12): 2629–2635.
Tang S Y, Yang L, Fan Z T, et al. A review of additive manufacturing technology and its application to foundry in China. China Foundry, 2021, 18(4): 249–264.
Javelin. Fused deposition modeling/FDM technology from Stratasys. https://www.javelin-tech.com/3d/manufacture/fdmtechnology/, 2024, accessed 6 March 2024.
Shi Y, Zhang J, Wen S, et al. Additive manufacturing and foundry innovation. China Foundry, 2021, 18(4): 286–295.
Almaghariz E S, Conner B P, Lenner L, et al. Quantifying the role of part design complexity in using 3D sand printing for molds and cores. International Journal of Metalcasting, 2016, 10(3): 240–252.
Almaghariz E S. Determining when to use 3D sand printing: Quantifying the role of complexity. Master’s Dissertation, USA OH: Youngstown State University, 2015: 1–63.
Conner B P, Manogharan G P, Martof A N, et al. Making sense of 3-D printing: Creating a map of additive manufacturing products and services. Additive Manufacturing, 2014, 1–4: 64–76.
Wang J, Sama S R, Manogharan G. Re-thinking design methodology for castings: 3D sand-printing and topology optimization. International Journal of Metalcasting, 2019, 13: 2–17.
Sama S R, Badamo T, Manogharan G. Case studies on integrating 3D sand-printing technology into the production portfolio of a sand-casting foundry. International Journal of Metalcasting, 2020, 14: 12–24.
Snelling D, Li Q, Meisel N, et al. Lightweight metal cellular structures fabricated via 3D printing of sand cast molds. Advanced Engineering Materials, 2015, 17(7): 923–932.
Deng C, Kang J, Shangguan H, et al. Insulation effect of air cavity in sand mold using 3D printing technology. China Foundry, 2018, 15(1): 37–43.
Walker J, Harris E, Lynagh C, et al. 3D printed smart molds for sand casting. International Journal of Metalcasting, 2018, 12(4): 785–796.
Kang J, Shangguan H, Deng C, et al. Additive manufacturing-driven mold design for castings. Additive Manufacturing, 2018, 22: 472–478.
Shangguan H, Kang J, Deng C Y, et al. 3D-printed shell-truss sand mold for aluminum castings. Journal of Materials Processing Technology, 2017, 250: 247–253.
Deng C, Kang J, Shangguan H, et al. Effects of hollow structures in sand mold manufactured using 3D printing technology. Journal of Materials Processing Technology, 2018, 255: 516–523.
Shangguan H, Kang J, Yi J, et al. The design of the 3D printed lattice reinforced thickness-varying shell mold for casting. Materials, 2018, 11(4): 535.
Shangguan H, Kang J, Deng C, et al. 3D-printed rib-enforced shell sand mold for aluminum castings. The International Journal of Advanced Manufacturing Technology, 2018, 96(5–8): 2175–2182.
Wang J, Zheng L, Kang J, et al. Study on the directional solidification process of an aluminum alloy bar in multi-shell mold being gradually immersed in water. Materials, 2020, 13(9): 2197.
Jahanbakhshi M, Nourouzi S, Naseri R, et al. Investigation of simultaneous effects of cooling slope casting and mold vibration on mechanical and microstructural properties of A356 aluminum alloy. Metals and Materials International, 2022, 28(6): 1508–1516.
Abu-Dheir N, Khraisheh M, Saito K, et al. Silicon morphology modification in the eutectic Al-Si alloy using mechanical mold vibration. Materials Science and Engineering: A, 2005, 393(1–2): 109–117.
Olufemi A F, Ademola I S. Effects of melt vibration during solidification on the mechanical property of Mg-Al-Zn Alloy. International Journal of Metallurgical Engineering, 2012, 1(3): 40–43.
Kudryashova O, Khmeleva M, Danilov P, et al. Optimizing the conditions of metal solidification with vibration. Metals, 2019, 9(3): 366.
Kund N K. Effect of tilted plate vibration on solidification and microstructural and mechanical properties of semisolid cast and heat-treated A356 Al alloy. The International Journal of Advanced Manufacturing Technology, 2018, 97: 1617–1626.
Chaturvedi V, Talapaneni T. An overview on the microstructure and mechanical properties of vibrated magnesium alloy during solidification. In: Kolhe M L, Jaju S B, Diagavane P M (eds.), Smart Technologies for Energy, Environment and Sustainable Development, ICSTEESD 2020; Springer Proceedings in Energy. Springer, Singapore, 2022, 2: 423–431.
Chaturvedi V, Talapaneni T. Effect of mechanical vibration and grain refiner on microstructure and mechanical properties of AZ91Mg alloy during solidification. Journal of Materials Engineering and Performance, 2021, 30: 3187–3202.
Li M, Tamura T, Omura N, et al. Microstructure formation and grain refinement of Mg-based alloys by electromagnetic vibration technique. Transactions of Nonferrous Metals Society of China, 2010, 20(7): 1192–1198.
Singh S, Gupta D, Jain V. Novel electromagnetic composite casting process: Theory, feasibility and characterization. Materials & Design, 2016, 111: 51–59.
Mishra R R, Sharma A K. On melting characteristics of bulk Al-7039 alloy during in-situ microwave casting. Applied Thermal Engineering, 2017, 111: 660–675.
Maurya A, Kumar R, Jha P. Simulation of electromagnetic field and its effect during electromagnetic stirring in continuous casting mold. Journal of Manufacturing Processes, 2020, 60: 596–607.
Cho S M, Thomas B G. Electromagnetic effects on solidification defect formation in continuous steel casting. JOM, 2020, 72(10): 3610–3627.
Gajmal S, Raut D N. A review of opportunities and challenges in electromagnetic assisted casting. Recent Trends in Production Engineering, 2019, 2(1): 1–17.
Samyal R, Bagha A K, Bedi R. The casting of materials using electromagnetic energy: A review. Materials Today: Proceedings, 2020, 26(Part 2): 1279–1283.
Singh S, Singh P, Gupta D, et al. Development and characterization of electromagnetic processed cast iron joint. Engineering Science and Technology, an International Journal, 2019, 22(2): 569–577.
Raj A, Kishore S R, Jose L, et al. A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues. The European Physical Journal Plus, 2021, 136: 704.
Jian X, Xu H, Meek T, et al. Effect of power ultrasound on solidification of aluminium A356 alloy. Materials Letters, 2005, 59(2–3): 190–193.
Yao L, Hao H, Ji S H, et al. Effects of ultrasonic vibration on solidification structure and properties of Mg-8Li-3Al alloy. Transactions of Nonferrous Metals Society of China, 2011, 21(6): 1241–1246.
Barbosa J, Puga H. Ultrasonic melt treatment of light alloys. International Journal of Metalcasting, 2019, 13: 180–189.
Proni C T W, Brollo G L, Zoqui E J. A comparison of the use of ultrasonic melt treatment and mechanical vibration in the manufacture of AI5Si5Zn alloy feedstock for thixoforming. Metallurgical and Materials Transactions: B, 2020, 51: 306–317.
Tonry C E H, Bojarevics V, Djambazov G, et al. Contactless ultrasonic treatment in direct chill casting. JOM, 2020, 72(11): 4082–4091.
Zhang L, Li X Q, Liu Z L, et al. Scalable ultrasonic casting of large-scale 2219AA Al alloys: Experiment and simulation. Materials Today Communications, 2021, 27: 102329.
Emadi P, Ravindran C. The influence of high temperature ultrasonic processing time on the microstructure and mechanical properties AZ91E magnesium alloy. Journal of Materials Engineering and Performance, 2021, 30: 1188–1199.
Kottana N, Vishwanatha H, Sengupta S, et al. Investigation on synergetic effect of non-contact ultrasonic casting and mushy state rolling on microstructure and hardness of Al-Si-Al2O3 nanocomposites. International Journal on Interactive Design and Manufacturing, 2023, 17(5): 2299–2308.
He W. Intelligent control system for automobile brake disc investment casting based on PLC. Hot Working Technology, 2020, 49(3): 84–88. (In Chinese)
Kang J, Long H, Li Y, et al. Observation of the mold-filling process of a large hydro-turbine guide vane casting. Metallurgical and Materials Transactions: B, 2015, 46(1): 337–344.
Pekguleryuz M O, Li X, Aliravci C A. In-situ investigation of hot tearing in aluminum alloy AA1050 via acoustic emission and cooling curve analysis. Metallurgical and Materials Transactions: A, 2009, 40: 1436–1456.
Bian Q, Bauer C, Stadler A, et al. Monitoring strain evolution and distribution during the casting process of AlSi9Cu3 alloy with optical fiber sensors. Journal of Alloys and Compounds, 2023, 935(Part 2): 168146.
Chu Q, Liang J D. Design of ultrasonic high pressure casting intelligent control system based on PLC. Hot Working Technology, 2019, 48(23): 89–92. (In Chinese)
Shi G F. Research on intelligent control of aluminum alloy wheel hub low pressure casting based on PLC. The Chinese Journal of Nonferrous Metals, 2019, 11: 279–280. (In Chinese)
Vuksanovich B, Herberger C, Jaric D, et al. Wireless ventilation measurement in 3D printed sand molds. International Journal of Metalcasting, 2022, 16(1): 80–92.
Walker J M, Prokop A, Lynagh C, et al. Real-time process monitoring of core shifts during metal casting with wireless sensing and 3D sand printing. Additive Manufacturing, 2019, 27: 54–60.
Teskeredžić A, Demirdžić I, Muzaferija S. Numerical method for calculation of complete casting processes-Part I: Theory. Numerical Heat Transfer, Part B: Fundamentals, 2015, 68(4): 295–316.
Chen Z, Li Y, Zhao F, et al. Progress in numerical simulation of casting process. Measurement and Control, 2022, 55(5–6): 257–264.
Kang J, Wang J, Shangguan H, et al. Modeling and simulation of the casting process with skeletal sand mold. Materials, 2020, 13(7): 1596.
Xu J, Kang J, Zheng L, et al. Numerical simulation of the directional solidification process with multi-shell mold being gradually immersed in water. Journal of Materials Research and Technology, 2022, 19: 2705–2716.
Hu H, Chen F, Chen X, et al. Effect of cooling water flow rates on local temperatures and heat transfer of casting dies. Journal of Materials Processing Technology, 2004, 148(1): 57–67.
Yang T, Hu H, Chen X, et al. Thermal analysis of casting dies with local temperature controller. International Journal of Advanced Manufacturing Technology, 2007, 33(3–4): 277–284.
Shin S, Lee S, Kim D, et al. Enhanced cooling channel efficiency of high-pressure die-casting molds with pure copper linings in cooling channels via explosive bonding. Journal of Materials Processing Technology, 2021, 297: 117235.
Stets W, Petzschmann U. Active cooling of resin bonded moulds to reduce the cooling time of heavy-section castings without loss of casting quality. In: Proc. 71st World Foundry Congress, Bilbao Spain, 2014.
Zhang L Y, Ma Z, Shan S F, et al. Effect of cooling rate on solidified microstructure and mechanical properties of aluminium-A356 alloy. Journal of Materials Processing Technology, 2008, 207: 107–111.
Szalva P, Orbulov IN. The effect of vacuum on the mechanical properties of die cast aluminum AlSi9Cu3(Fe) alloy. International Journal of Metalcasting, 2019, 13: 853–864.
Kurtulus K, Bolatturk A, Coskun A, et al. An experimental investigation of the cooling and heating performance of a gravity die casting mold with conformal cooling channels. Applied Thermal Engineering, 2021, 194: 117105.
Kang J, Hao X, Nie G, et al. Intensive riser cooling of castings after solidification. Journal of Materials Processing Technology, 2015, 215: 278–286.
Grassi J, Campbell J, Hartieb M, et al. The ablation casting process. Materials Science Forum, 2009, 618–619: 591–594.
Weiss D, Grassi J, Schultz B, et al. Testing the limits of ablation. Modern Casting, 2011, 101(12): 26–29.
Taghipourian M, Mohammada M, Boutorabi SM, et al. The effect of waterjet beginning time on the microstructure and mechanical properties of A356 aluminum alloy during the ablation casting process. Joumal of Materials Processing Technology, 2016, 238: 89–95.
Boutorabi S M A, Torkaman P, Campell J, et al. Structure and properties of carbon steel cast by the ablation process. International Journal of Metalcasting, 2021, 15: 306–318.
Acar S, Guler K A. A preliminary study upon the application of the direct water cooling with the lost foam casting process. International Journal of Metalcasting, 2020, 15: 88–97.
Wu J, Sui D, Han Q. High quality plate-shaped A356 alloy casting by a combined ablation cooling and mold heating method. Journal of Materials Processing Technology, 2022, 303: 117536.
Kang J, Shangguan H, Peng F, et al. Cooling control for castings by adopting skeletal sand mold design. China Foundry, 2021, 18(1): 18–28.
Han X. Research on temperature field prediction method of sand mold casting solidification process based on U-NET. Master’s Dissertation, Beijing: Beijing Jiaotong University, 2022: 66. (In Chinese)
Ferguson M, Ak R, Lee Y T T, et al. Automatic localization of casting defects with convolutional neural networks. In: Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, USA, 2017: 1726–1735.
Gellrich S, Filz M A, Wilde A S, et al. Deep transfer learning for improved product quality prediction: A case study of aluminum gravity die casting. Procedia CIRP, 2021, 104: 912–917.
Chen S K, Kaufmann T. Development of data-driven machine learning models for the prediction of casting surface defects. Metals, 2022, 12(1): 1.
Tavakoli R, Davami P. Optimal riser design in sand casting process with evolutionary topology optimization. Structural and Multidisciplinary Optimization, 2009, 38: 205–214.
Dong C C, Shen X, Zhou J X, et al. Optimal design of feeding system in steel casting by constrained optimization algorithms based on InteCAST. China Foundry, 2016, 13(6): 375–382.
Chen H, Gao Q J, Wang Z H, et al. Optimization of casting system structure based on genetic algorithm for A356 casting quality prediction. International Journal of Metalcasting, 2023, 17: 1948–1969.
Nouri M, Artozoul J, Caillaud A, et al. Shrinkage porosity prediction empowered by physics-based and data-driven hybrid models. International Journal of Material Forming, 2022, 15(3): 25.
Bak C, Roy A G, Son H. Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique. CIRP Journal of Manufacturing Science and Technology, 2021, 33: 327–338.
Lee J H, Noh S D, Kim H J, et al. Implementation of cyber-physical production systems for quality prediction and operation control in metal casting. Sensors, 2018, 18(5): 1428.
Acknowledgments
This research was funded by the Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund (L212002), the Tsinghua-Toyota Joint Research Fund (20223930096), and the Guangdong Provincial Key Area Research and Development Program (2022B0909070001).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Jin-wu Kang Ph. D, Associate Professor. His research interests mainly focus on the modeling and simulation of casting, solidification and additive manufacturing, intelligent casting. His professional expertise includes computer-aided engineering (CAE) for casting process, advanced hollow mold technology, AI enabled simulation, etc. To date, he has published about 200 papers and received four academic awards from the Beijing City, Ministry of Education of China, etc. for his distinguished research.
E-mail: kangjw@tsinghua.edu.cn
Rights and permissions
About this article
Cite this article
Kang, Jw., Liu, Bl., Jing, T. et al. Intelligent casting: Empowering the future foundry industry. China Foundry (2024). https://doi.org/10.1007/s41230-024-4056-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41230-024-4056-z
Keywords
- intelligent casting
- 3D printing
- intelligent mold
- process control
- cyber-physical casting system
- embedded simulation