| 龚灯,赵伦武,倪帅,韩刚.复杂内孔结构磨粒流加工的检测评价与优化[J].电子测量与仪器学报,2024,38(3):187-194 |
| 复杂内孔结构磨粒流加工的检测评价与优化 |
| Evaluation and optimization of complex inner hole structurein abrasive flow machining |
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| DOI： |
| 中文关键词: 磨粒流 去毛刺工艺 正交试验 加工质量 多目标优化 |
| 英文关键词:abrasive flow machining deburring process orthogonal test machining quality multi-objective optimization |
| 基金项目:安徽省教育厅自然科学研究重点项目（KJ2021A1493）、国家自然科学基金（51675155）、2023校级质量工程项目（gf2023tszy02）资助 |
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| Author | Institution |
| Gong Deng | School of Mechanical Technology,Anhui Vocational College of Defense Technology, Lu′an 237005, China |
| Zhao Lunwu | School of Mechanical Engineering,Hefei University of Technology, Hefei 230009, China |
| Ni Shuai | School of Mechanical Technology,Anhui Vocational College of Defense Technology, Lu′an 237005, China |
| Han Gang | School of Mechanical and Vehicle Engineering,West Anhui University, Lu′an 237012, China |
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| 摘要点击次数: 416 |
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| 中文摘要: |
| 为提高军工零件的内孔加工的可靠性和加工质量，通过正交试验法，分析磨粒流加工中，磨料粒度、磨料质量分数、磨粒流工作压力和循环加工次数对阶梯内孔倒圆角半径、材料去除率、内孔表面粗糙度的影响规律，磨粒流工作压力是内孔倒圆角半径的首要影响因素，较为显著，材料去除率的首要影响因素是磨料质量分数，而内孔表面粗糙度的首要影响因素是循环加工次数，通过非线性回归分析建立了复杂内孔磨粒流加工效果评价的模型。以改进的带精英策略的快速非支配排序遗传算法(NSGA-II)进行多目标工艺参数寻优，得到了综合平衡权重下的最佳工艺方案（磨料粒度1 000#，磨料质量分数为50%，工作压力为7.5 MPa，循环加工次数为30次），验证试验表明优化结果和实际加工效果具有较好的一致性。 |
| 英文摘要: |
| To improve the reliability and machining quality of deburring process for inner holes of projectile components, an evaluation index for the quality of inner hole abrasive flow machining (AFM) is established based on the inverted fillet radius, material removal rate, and surface roughness. An orthogonal test was designed and completed, with abrasive particle size, abrasive mass fraction, abrasive flow working pressure, and cycle numbers as the control factors. Results indicate that all process parameters can ensure good deburring effect. The working pressure of AFM is the primary and significant influencing factor on the inverted filler radius. The primary influencing factor on the material removal rate is the abrasive mass fraction. And for surface roughness, it is cycle numbers. Furthermore, A prediction model for process parameters and machining quality evaluation indexes was established through nonlinear regression analysis. Multi-objective optimization was performed using the fast elitist non-dominated sorting genetic algorithm (NSGA-II). The optimized parameters (The abrasive grain size is 1 000#, the mass fraction of abrasive is 40%, the pressure is 7.5 MPa, and cycles times is 30) were verified and the optimized results are consistent with the actual machining. |
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