Tan Rongkai, Madathil Abhilash Puthanveettil, Liu Qi, Cheng Jian, Lin Fengtao
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Centre for Precision Manufacturing, DMEM, University of Strathclyde, Glasgow G1 1XJ, UK.
Micromachines (Basel). 2025 Apr 14;16(4):464. doi: 10.3390/mi16040464.
Micro-milling is increasingly recognized as a crucial technique for machining intricate and miniature 3D aerospace components, particularly those fabricated from difficult-to-cut Ti-6Al-4V alloys. However, its practical applications are hindered by significant challenges, particularly the unavoidable generation of burrs, which complicate subsequent finishing processes and adversely affect overall part quality. To optimize the burr formation in the micro-milling of Ti-6Al-4V alloys, this study proposes a novel hybrid-ranking optimization algorithm that integrates Grey Relational Analysis (GRA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This approach innovatively combines GRA and TOPSIS with a random forest regression (RFR) model, facilitating the exploration of nonlinear and complex relationships between input parameters and machining outcomes. Specifically, the effects of spindle speed, depth of cut, and feed rate per tooth on surface roughness and burr width generated during both down-milling and up-milling processes were systematically investigated using the proposed methodology. The results reveal that the depth of cut is the most influential factor affecting surface roughness, while feed rate per tooth plays a critical role in controlling burr formation. Moreover, the GRA-TOPSIS-RFR method significantly outperforms existing optimization and prediction models, with the integration of the RFR model enhancing prediction accuracy by 42.6% compared to traditional linear regression approaches. The validation experimental results agree well with the GRA-TOPSIS-RFR-optimized outcomes. This research provides valuable insights into optimizing the micro-milling process of titanium components, ultimately contributing to improved quality, performance, and service life across various aerospace applications.
微铣削日益被视为加工复杂和微型三维航空航天部件的关键技术，特别是那些由难切削的Ti-6Al-4V合金制造的部件。然而，其实际应用受到重大挑战的阻碍，尤其是不可避免地产生毛刺，这使后续的精加工过程复杂化，并对整体零件质量产生不利影响。为了优化Ti-6Al-4V合金微铣削中的毛刺形成，本研究提出了一种新颖的混合排序优化算法，该算法将灰色关联分析（GRA）与逼近理想解排序法（TOPSIS）相结合。这种方法创新性地将GRA和TOPSIS与随机森林回归（RFR）模型相结合，有助于探索输入参数与加工结果之间的非线性和复杂关系。具体而言，使用所提出的方法系统地研究了主轴转速、切削深度和每齿进给率对顺铣和逆铣过程中产生的表面粗糙度和毛刺宽度的影响。结果表明，切削深度是影响表面粗糙度的最主要因素，而每齿进给率在控制毛刺形成方面起着关键作用。此外，GRA-TOPSIS-RFR方法明显优于现有的优化和预测模型，与传统线性回归方法相比，RFR模型的集成使预测精度提高了42.6%。验证实验结果与GRA-TOPSIS-RFR优化结果吻合良好。本研究为优化钛部件的微铣削工艺提供了有价值的见解，最终有助于提高各种航空航天应用的质量、性能和使用寿命。
