基于响应面法的ABS-FDM多轴增减材工艺优化
Optimization of ABS-FDM Multi Axial Additive-subtractive Manufacturing Process Based on Response Surface Method
针对熔融沉积成型(FDM)丙烯腈-丁二烯-苯乙烯共聚物(ABS)制件层间结合弱以及各向异性显著的问题,提出一种多轴增减材协同工艺优化方法。基于单因素预实验与响应面法(Box-Behnken设计),系统研究打印层厚(0.10~0.20 mm)、打印温度(210~230 ℃)、填充密度(60%~100%)及侧面减材方式(逐层或整体)对拉伸强度的交互影响机制。结果表明:填充密度为主导因子,在单因素实验中,当填充密度为100%时,制件的拉伸强度达35.7 MPa;在打印层厚为0.20 mm且填充密度为80%的情况下,采用逐层减材相较整体减材,制件的拉伸强度提升18.7%;当打印层厚从0.10 mm增至0.20 mm时,制件的拉伸强度降低7.4 MPa,降幅为20.1%。优化参数组合(打印层厚0.10 mm、打印温度220 ℃、填充密度100%、逐层减材)能够使制件的拉伸强度达42.9 MPa,相较传统FDM工艺提升25.4%,同时制件的表面粗糙度降至6.5 μm,降幅为54.2%。验证试验的结果证实了该优化模型的可靠性。
Aiming at the problems of weak interlayer bonding and significant anisotropy in fused deposition modeling (FDM) acrylonitrile-butadiene-styrene (ABS) parts, a multi-axis additive-subtractive hybrid process optimization method is proposed. Based on single-factor pre-experiments and response surface methodology (Box-Behnken design), the interactive effects of printing layer thickness (0.10~0.20 mm), printing temperature (210~230 ℃), infill density (60%~100%), and side subtractive method (layer-by-layer or whole) on tensile strength were systematically studied. The results show that the infill density is the dominant factor. In single-factor experiments, when the infill density is 100%, the tensile strength of the part reaches 35.7 MPa. When the printing layer thickness is 0.20 mm and the infill density is 80%, the tensile strength of the part increases by 18.7% using the layer-by-layer subtractive method compared with the whole subtractive method. When the printing layer thickness increases from 0.10 mm to 0.20 mm, the tensile strength of the part decreases by 7.4 MPa, with a reduction of 20.1%. The optimized parameter combination (printing layer thickness of 0.10 mm, printing temperature of 220 ℃, infill density of 100%, and layer-by-layer subtractive method) can increase the tensile strength of the part to 42.9 MPa, which is 25.4% higher than that of the traditional FDM process. Meanwhile, the surface roughness of the part is reduced to 6.5 µm, with a reduction of 54.2%. The results of the verification experiment confirmed the reliability of the optimization model.
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广东省普通高校创新团队项目(自然科学)(2023KCXTDO80)
广东省人力资源和社会保障厅广东省技工教育和职业培训省级课题(KT2023032)
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