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Estimation of Casting Mold Interfacial Heat Transfer Coefficient in Pressure Die Casting Process by Artificial Intelligence Methods

  • Research Article-Mechanical Engineering
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Abstract

Pressure casting process, which is based on the principle of filling and solidifying the liquid metal into the mold cavity with the effect of speed and pressure, enables to obtain a serial product. The pressure casting process usually involves a thermal process. Starting with the casting process, the thermal resistances, especially formed at the casting mold interface, and the resultant interfacial heat transfer coefficient (IHTC) are among the most important factors determining the mechanical and physical properties of the produced part. The IHTC depends on the mold temperature, casting temperature, injection pressure, injection rate, vacuum application and many other incalculable parameters. In this study, it was aimed to determine the heat transfer coefficient and heat flux of the casting mold interface which has a significant effect on the quality of parts in the pressure casting of cylindrical mold geometry of AlSi8Cu3Fe aluminum alloy. The study was carried out depending on different casting temperatures, injection pressure, injection speed and vacuum application to the mold cavity. Temperatures were measured with thermocouples placed in the mold and casting material, IHTC and heat flux were calculated with finite difference method by using experimentally measured temperatures. In the application of artificial intelligence methods, casting temperature, injection speed, injection pressure and vacuum conditions are given as input parameters and interfacial flow coefficient and heat flux are accepted as output parameters. With the help of these parameters, DTR, MLR and ANNR deep learning algorithms were used to estimate the interfacial heat transfer coefficient. Among these algorithms, ANNR algorithm was found to be the most accurate estimating model at the rate of 99.9%. For the obtained model, a computer program was prepared for the users to be able to see and follow the experimental results and the results obtained from the model at the same time.

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References

  1. Doehler, H.H.; Basınçlı Döküm, Ç.; Bayvas, M.Ş.: Mesleki ve Teknik Öğretim Kitapları, Etüd Ve Programlama Dairesi Yayınları No:80, Erkek Teknik Yüksek Öğretmen Okulu Matbaası, 514s, Ankara (1974)

  2. Flemings, C.M.; Döküm Ve Katılaştırma, T.; Çeviren, B.M.: İstanbul Teknik Üniversitesi Matbaası, Gümüşsuyu, 415s, İstanbul (1976)

  3. Vinarcık, J.E.: High Integrity Die Casting Processes. Wiley, New York (2003)

    Google Scholar 

  4. Anderesen, B.: Die Casting Engineering a Hydraulic, Thermal and Mechanical Process. Marcel Dekker, New York (2005)

    Google Scholar 

  5. Koru, M.: Basınçlı Döküm Yönteminde Al-Si (A413) Alaşımının Termal ve Dinamik Parametrelere Bağlı Olarak Ara Yüzey Isı Transfer Katsayısının Deneysel ve Teorik İncelenmesi. S.D.Ü. Fen Bilimleri Enstitüsü, Doktora Tezi, 157s., Isparta (2009)

  6. İpek, O.; Koru, M.: Yüksek Basınçlı Döküm Prosesinde Kalıp Sıcaklığına Bağlı Olarak Döküm-Kalıp Ara Yüzeyinde Oluşan Termal Temas Direncinin Belirlenmesi. Isı Bilimi ve Tekniği Dergisi 31, 45–57 (2011)

    Google Scholar 

  7. Papai, J.P.: Contact heat transfer coefficient in aluminum alloy die casting: an experimental and numerical investigation. Ph.D. thesis, The Ohio State University (1994)

  8. Assar, A.M.: Mould surface roughness and interfacial heat transfer using heat flow model. Mater. Sci. Technol. 13(8), 702 (1997)

    Google Scholar 

  9. Chen, Z.W.: Skin solidification during high pressure die casting of Al-11Si-2Cu-1Fe. Alloy Mater. Sci. Eng. 348, 145–153 (2003)

    Google Scholar 

  10. Dour, G.; Dargusch, M.; Davidson, C.: Recommendations and guidelines for the performance of accurate heat transfer measurements in rapid forming processes. Int. J. Heat Mass Transf. 49, 1773–1789 (2006)

    Google Scholar 

  11. Dour, G.; Dargusch, M.; Davidson, C.; Nef, A.: Development of a non-intrusive heat transfer coefficient gauge and its application to high pressure die casting effect of the process parameters. J. Mater. Process. Technol. 169, 223–233 (2005)

    Google Scholar 

  12. Şahin, H.M.; Kocatepe, K.; Kayıkçı, R.; Akar, N.: Ötektik Al-Si alaşımında soğutucu yüzey pürüzlülüğünün ara yüzey ısı transfer katsayısına etkisi. Gazi Üniv. Müh. Mim. Fak. Der. Cilt. 21(3), 473–481 (2006)

    Google Scholar 

  13. Akar, N.: Katılaşma sırasında döküm-Kalıp ara yüzeyinde ısı transfer katsayısının incelenmesi. Gazi Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi, 136 S., Ankara (2006)

  14. Griffiths, W.D.: Modelled heat transfer coefficient for al-7 wt-%si alloy casting unidirectionally solidified horizontally and vertically downwards. Mater. Sci. Technol. 16, 255–260 (2000)

    Google Scholar 

  15. Akar, N.; Şahin, H.M.; Yalçın, N.; Kocatepe, K.: Experimental study on the effect of liquid metal superheat and casting height on interfacial heat transfer coefficient. Exp. Heat Transf. 21, 83–98 (2008)

    Google Scholar 

  16. Akar, N.; Boran, K.; Hozikligil, B.: Effect of mold temperature on heat transfer coefficient at casting-mold interface. J. Fac. Eng. Archit. Gazi Univ. 28(2), 275–282 (2013)

    Google Scholar 

  17. Campbell, J.: Casting. Butterworth Heinemann, Oxford (2002)

    Google Scholar 

  18. Incropera, F.P.; Dewitt, D.P.: Fundamentals of Heat and Mass Transfer, 5th edn. Wiley, New York (2001)

    Google Scholar 

  19. Özışık, M.N.: Finite difference methods in heat transfer. Mechanical and Aerospace Engineering Department, North Carolina State University, p 412. CRC, Florida, USA (1994)

  20. Lau, F.; Lee, W.B.; Xiong, S.M.; Liu, B.C.: A study of the interfacial heat transfer between an iron casting and a metallic mould. J. Mater. Process. Technol. 79, 25–29 (1998)

    Google Scholar 

  21. Zhi-peng, G.; Shou-mei, X.; Bai-cheng, L.; Li, M.; Allison, J.: Determination of the heat transfer coefficient at metal–die interface of high pressure die casting process of AM50 alloy. Int. J. Heat Mass Transf. 51(25), 6032–6038 (2008)

    Google Scholar 

  22. Hamasaiid, A.; Dour, G.; Dargusch, M.S.; Loulou, T.; Davidson, C.; Savage, G.: Heat-transfer coefficient and in-cavity pressure at the casting-die interface during high-pressure die casting of the magnesium alloy AZ91D. Metall. Mater. Trans. A 39a, 853 (2008)

    Google Scholar 

  23. Krimpenis, A.; Benardos, P.G.; Vosniakos, G.C.; Koukouvitaki, A.: Simulation-Based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms. Int. J. Adv. Manuf. Technol. 27, 509–517 (2006)

    Google Scholar 

  24. Loulou, T.; Artyukhin, E.A.; Bardon, J.P.: Estimation of thermal contact resistance during the first stage of metal solidification process: I-experiment principle and modelisation. Int. J. Heat Mass Transf. 42, 2119–2127 (1999)

    Google Scholar 

  25. Taha, M.A.; El-Mahallawy, N.A.; El-Mestekawi, M.T.; Hassan, A.A.: Estimation of air gap and heat transfer coefficient at different faces of Al and Al-Si casting solidifying in permanent mould. Mater. Sci. Technol. 17(9), 1093 (2001)

    Google Scholar 

  26. Santos, C.A.; Quaresma, J.M.V.; Garcia, A.: Determination of transient interfacial heat transfer coefficients in chill mold castings. J. Alloys Compd. 319, 174–186 (2001)

    Google Scholar 

  27. Gafur, M.A.; Haque, M.N.; Prabhu, K.N.: Effect of chill thickness and superheat on casting/chill interfacial heat transfer during solidification of commercially pure aluminum. J. Mater. Process. Technol. 133, 257–265 (2003)

    Google Scholar 

  28. Hallam, C.P.; Griffiths, W.D.: A model of the interfacial heat transfer coefficient for the aluminum gravity die casting process. Metall. Mater. Trans. 35(4), 721 (2004)

    Google Scholar 

  29. Srinivasan, M.N.: Heat transfer coefficients at the casting-mould interface during solidification of flake graphite cast iron in metallic mould. Indian J. Technol. 20(4), 123–129 (1982)

    Google Scholar 

  30. Broucaret, S.; Michrafy, A.; Dour, G.: Heat transfer and thermo-mechanical stresses in a gravity casting die influence of process parameters. J. Mater. Process. Technol. 110, 211–217 (2001)

    Google Scholar 

  31. Yaqoob, I.; Hashem, I.A.T.; Gani, A.; Mokhtar, S.; Ahmed, E.; Anuar, N.B.; Vasilakos, A.V.: Big data: from beginning to future. Int. J. Inf. Manag. 36(6), 1231–1247 (2016)

    Google Scholar 

  32. Zhang, D.: Big data security and privacy protection. In: 8th International Conference on Management and Computer Science (ICMCS 2018). Atlantis Press (2018)

  33. Fan, S.K.S.; Su, C.J.; Nien, H.T.; Tsai, P.F.; Cheng, C.Y.: Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Comput. 22(17), 5707–5718 (2018)

    Google Scholar 

  34. Plageras, A.P.; Psannis, K.E.; Stergiou, C.; Wang, H.; Gupta, B.B.: Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Gener. Comput. Syst. 82, 349–357 (2018)

    Google Scholar 

  35. Jing, X.; Yan, Z.; Pedrycz, W.: Security data collection and data analytics in the internet: a survey. IEEE Commun. Surv. Tutor. 21(1), 586–618 (2018)

    Google Scholar 

  36. Stieglitz, S.; Mirbabaie, M.; Ross, B.; Neuberger, C.: Social media analytics—challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manag. 39, 156–168 (2018)

    Google Scholar 

  37. Parmar, C.; Barry, J.D.; Hosny, A.; Quackenbush, J.; Aerts, H.J.: Data analysis strategies in medical imaging. Clin. Cancer Res. 24(15), 3492–3499 (2018)

    Google Scholar 

  38. Zhang, L.; Wang, H.; Li, Q.; Zhao, M.H.; Zhan, Q.M.: Big data and medical research in China. BMJ 360, j5910 (2018)

    Google Scholar 

  39. Lee, S.; Huh, J.H.: An effective security measures for nuclear power plant using big data analysis approach. J. Supercomput. 75, 1–28 (2018)

    Google Scholar 

  40. Mariani, M.; Baggio, R.; Fuchs, M.; Höepken, W.: Business intelligence and big data in hospitality and tourism: a systematic literature review. Int. J. Contemp. Hosp. Manag. 30(12), 3514–3554 (2018)

    Google Scholar 

  41. Ren, S.; Zhang, Y.; Liu, Y.; Sakao, T.; Huisingh, D.; Almeida, C.M.: A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. J. Clean. Prod. 210, 1343–1365 (2019)

    Google Scholar 

  42. Kuo, Y.H.; Kusiak, A.: From data to big data in production research: the past and future trends. Int. J. Prod. Res. 75, 1–26 (2018)

    Google Scholar 

  43. Russom, P.: Big data analytics. TDWI Best Pract. Rep. Fourth Quart. 19(4), 1–34 (2011)

    Google Scholar 

  44. Varatharajan, R.; Manogaran, G.; Priyan, M.K.: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed. Tools Appl. 77(8), 10195–10215 (2018)

    Google Scholar 

  45. Hamet, P.; Tremblay, J.: Artificial intelligence in medicine. Metab. Clin. Exp. 69, 36–40 (2017). https://doi.org/10.1016/j.metabol.2017.01.011

    Article  Google Scholar 

  46. Lee, J.; Davari, H.; Singh, J.; Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23 (2018)

    Google Scholar 

  47. Li, B.; Chai, X.; Hou, B.; Zhang, L.; Zhou, J.; Liu, Y.: New generation artificial intelligence-driven intelligent manufacturing (NGAIIM). In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1864–1869. IEEE (2018)

  48. Roll, I.; Wylie, R.: Evolution and revolution in artificial intelligence in education. Int. J. Artif. Intell. Educ. 26(2), 582–599 (2016)

    Google Scholar 

  49. Spronck, P.; André, E.; Cook, M.; Preuß, M.: Artificial and computational intelligence in games: AI-driven game design (Dagstuhl Seminar 17471). In: Dagstuhl Reports, vol. 7, no. 11. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2018)

  50. Fok, S.C.; Ong, E.K.: A high school project on artificial intelligence in robotics. Artif. Intell. Eng. 10(1), 61–70 (1996). https://doi.org/10.1016/0954-1810(95)00016-X

    Article  Google Scholar 

  51. Taşar, B.; Üneş, F.; Demirci, M.; Kaya, Y.Z.: Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi 9(1), 543–551 (2018)

    Google Scholar 

  52. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1994)

    MATH  Google Scholar 

  53. Zhu, A.X.: Artificial neural networks. Int. Encycl. Geogr. People Earth Environ. Technol. People Earth Environ. Technol. 15, 1–6 (2016)

    Google Scholar 

  54. Turkson, R.F.; Yan, F.; Ali, M.K.A.; Hu, J.: Artificial neural network applications in the calibration of spark-ignition engines: an overview. Eng. Sci. Technol. Int. J. 19(3), 1346–1359 (2016)

    Google Scholar 

  55. Ata, R.: Artificial neural networks applications in wind energy systems: a review. Renew. Sustain. Energy Rev. 49, 534–562 (2015)

    Google Scholar 

  56. Guresen, E.; Kayakutlu, G.; Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389–10397 (2011)

    Google Scholar 

  57. Mba, L.; Meukam, P.; Kemajou, A.: Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Build. 121, 32–42 (2016)

    Google Scholar 

  58. Murugan, S.; Kumar, B.M.; Amudha, S.:. Classification and prediction of breast cancer using linear regression, decision tree and random forest. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 763–766. IEEE (2017)

  59. Ignatov, D.; Ignatov, A.: Decision stream: cultivating deep decision trees. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 905–912. IEEE (2017)

  60. Electronic Statistics Textbook, in Tulsa, StatSoft, OK, USA (2013)

  61. Swetapadma, A.; Yadav, A.: A novel decision tree regression-based fault distance estimation scheme for transmission lines. IEEE Trans. Power Deliv. 32(1), 234–245 (2016)

    Google Scholar 

  62. Quraishi, M.Z.; Mouazen, A.M.: Development of a methodology for in situ assessment of topsoil dry bulk density. Soil Tillage Res. 126, 229–237 (2013)

    Google Scholar 

  63. Choubin, B.; Khalighi-Sigaroodi, S.; Malekian, A.; Kişi, Ö.: Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrol. Sci. J. 61(6), 1001–1009 (2016)

    Google Scholar 

  64. Gardner, M.W.; Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)

    Google Scholar 

  65. Kaneko, H.: Beware of r2 even for test datasets: using the latest measured y-values (r2LM) in time series data analysis. J. Chemom. 33(2), e3093 (2019)

    Google Scholar 

  66. Susac, F.; Teodor, V.G.; Ganea, D.: Estimation of Heat Transfer Coefficient in Permanent Mold Casting Using Artificial Neural Networks, New Technologies and Products in Machine Manufacturing Technologies (2017)

  67. Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.: Inverse approach using bio-inspired algorithm within Bayesian framework for the estimation of heat transfer coefficients during solidification of casting. J. Heat Transf. 142(1), 012403 (2020)

    Google Scholar 

  68. Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.: Simultaneous estimation of unknown parameters using a priori knowledge for the estimation of interfacial heat transfer coefficient during solidification of Sn–5wt% Pb alloy—an ANN-driven Bayesian approach. Sādhanā 44(4), 100 (2019)

    MathSciNet  Google Scholar 

  69. Rajaraman, R.; Velraj, R.: Comparison of interfacial heat transfer coefficient estimated by two different techniques during solidification of cylindrical aluminum alloy casting. Heat Mass Transf. 44(9), 1025–1034 (2008)

    Google Scholar 

  70. Rao, R.V.; Kalyankar, V.D.; Waghmare, G.: Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm. Appl. Math. Model. 38(23), 5592–5608 (2014)

    Google Scholar 

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  1. Faculty of Technology, Isparta University of Applied Sciences, Çünür, Turkey

    Bekir Aksoy & Murat Koru

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Aksoy, B., Koru, M. Estimation of Casting Mold Interfacial Heat Transfer Coefficient in Pressure Die Casting Process by Artificial Intelligence Methods. Arab J Sci Eng 45, 8969–8980 (2020). https://doi.org/10.1007/s13369-020-04648-7

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