镍基高温合金因其优异的耐高温和耐腐蚀性能，在航空航天等高端制造领域得到了广泛应用。然而，传统加工方式存在工艺复杂、效率低下及加工成本高等问题，制约了其大规模应用。相比之下，增材制造技术具备较高的设计自由度和缩短加工周期的优势，在提升制造效率方面展现出巨大潜力。但由于增材制造过程中的打印缺陷，其内流道表面质量普遍较差，难以满足实际工程应用需求。因此，实现高温合金增材构件内流道的高质量精整加工成为当前研究的重点。本文分别对磨料水射流抛光与电解质等离子抛光工艺进行了深入分析，探讨了两种抛光方法的材料去除机制，在此基础上，提出将磨料水射流抛光作为预处理、电解质等离子抛光作为精加工工序的复合抛光工艺方案，从而显著提升增材制造高温合金内流道的表面质量。

在磨料水射流抛光工艺研究中，分析了单颗磨粒的冲蚀机理及材料微观去除机理，并通过实验研究不同入口压力和抛光时间对抛光效果的影响规律，确定了兼顾表面质量和尺寸精度的抛光参数。

在电解质等离子抛光工艺研究中，采用内置电极式抛光方法，基于放电理论与阳极溶解理论，阐明其材料去除机理。通过伏安特性分析，明确了适用于内流道抛光的电压范围。在此基础上，开展了单因素实验，研究了抛光电压、电解液温度、阴极丝移动速度和蠕动泵转速对表面粗糙度的影响规律，并通过正交试验优化工艺参数，最终获得最优参数组合为：电压40 V、抛光液温度80℃、阴极丝移动速度3 mm/min、蠕动泵转速100 rpm。实验结果表明，该方法可显著改善增材制造高温合金内流道工件的表面质量，粗糙度由初始的11.125 μm降低至3.249 μm，尽管如此，仍未满足Ra ≤ 1 μm的应用要求。

针对增材制造内流道工件初始表面粗糙度较高的问题，单一抛光工艺难以满足精密加工的表面质量要求，本文提出磨料水射流-电解质等离子复合抛光方法以降低高温合金内流道的表面粗糙度。实验结果显示，抛光后最终表面粗糙度为0.875 μm，优于单一抛光工艺的处理效果，充分验证了该复合抛光方法在提升内流道表面质量方面的可行性与有效性。此外，构建了基于BP神经网络的粗糙度预测模型，并与多元线性回归模型进行了对比分析。结果显示，BP神经网络模型具有更高的预测精度和更小的相对误差，在高温合金内流道表面粗糙度预测中表现出更强的适应性与可靠性。

Nickel-based superalloys are widely used in high-end manufacturing fields such as aerospace due to their excellent high-temperature resistance and corrosion resistance. However, traditional machining methods suffer from complex processes, low efficiency, and high costs, limiting their large-scale application. In contrast, additive manufacturing (AM) offers greater design flexibility and shorter production cycles, showing great potential in improving manufacturing efficiency. Nevertheless, due to printing defects during the AM process, the surface quality of internal flow channels is generally poor, failing to meet the requirements of practical engineering applications. Therefore, achieving high-quality finishing of internal channels in AM-fabricated superalloy components has become a key research focus.

An in-depth analysis of abrasive water jet polishing (AWJP) and electrolyte plasma polishing (EPP) is conducted, with a focus on exploring the material removal mechanisms of both processes. On this basis, a composite polishing process is proposed, where AWJP is used as a pretreatment and EPP as the final finishing step, aiming to significantly enhance the surface quality of internal flow channels in additively manufactured nickel-based superalloys.

In the AWJP process study, the erosion mechanism of single abrasive particles and the microscopic material removal mechanism are analyzed. Experimental studies are conducted to investigate the effects of inlet pressure and polishing time on polishing performance, determining optimal parameters that balance surface quality and dimensional accuracy.

For the EPP process, an internal electrode polishing method is adopted. Based on discharge and anodic dissolution theories, the material removal mechanism is clarified. Through voltammetric analysis, the appropriate voltage range for polishing internal channels is determined. Subsequently, single-factor experiments are conducted to study the influence of polishing voltage, electrolyte temperature, cathode wire feed speed, and peristaltic pump speed on surface roughness. An orthogonal experiment is carried out to optimize the process parameters, resulting in the optimal combination: voltage of 40 V, electrolyte temperature of 80 °C, cathode feed speed of 3 mm/min, and peristaltic pump speed of 100 rpm. Experimental results show that the proposed method significantly improves surface quality, reducing roughness from 11.125 μm to 3.249 μm. However, the result still does not meet the application requirement of Ra ≤ 1 μm.

To address the high initial roughness of internal channels in AM components and the limitations of single polishing methods, a composite AWJP-EPP polishing approach is proposed to further reduce surface roughness. Experimental results show that the final surface roughness reaches 0.875 μm, outperforming either single method, thereby validating the feasibility and effectiveness of the proposed composite process in improving surface quality. Furthermore, a surface roughness prediction model based on a BP neural network is established and compared with a multiple linear regression model. The results demonstrate that the BP neural network model offers higher prediction accuracy and lower relative error, showing greater adaptability and reliability in predicting the surface roughness of internal flow channels in nickel-based superalloys.

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