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.

基于在速度和压力的作用下将液态金属填充并固化到型腔中的原理的压铸工艺能够获得系列产品。压铸过程通常涉及热处理。从铸造工艺开始，尤其是在铸模界面处形成的热阻以及由此产生的界面传热系数（IHTC）是决定所生产零件的机械和物理性能的最重要因素。IHTC取决于模具温度，铸造温度，注射压力，注射速率，真空施加情况和许多其他无法计算的参数。在这个研究中，₈铜₃铁铝合金。根据不同的铸造温度，注射压力，注射速度和对模腔的真空度进行了研究。用放置在模具和铸造材料中的热电偶测量温度，并使用实验测量的温度通过有限差分法计算IHTC和热通量。在人工智能方法的应用中，以铸造温度，注射速度，注射压力和真空条件为输入参数，并接受界面流动系数和热通量为输出参数。在这些参数的帮助下，使用DTR，MLR和ANNR深度学习算法来估计界面传热系数。在这些算法中，发现ANNR算法是最准确的估计模型，其比率为99.9％。对于获得的模型，准备了一个计算机程序，供用户查看和跟踪实验结果以及从模型中同时获得的结果。