Stiffness optimization of 5-axis machine tool for improving surface roughness of 3D printed products
Stiffness optimization of 5-axis machine tool for improving surface roughness of 3D printed products
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DOI：
10.1007/s12206-017-0625-z
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发表时间：
2017-07
影响因子：
1.6
通讯作者：
S. Ko;Donghun Lee
中科院分区：
文献类型：
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作者：
S. Ko;Donghun Lee
针对叠层式生产方式所带来的表面粗糙度差的问题，设计了一种基于3 P2 R结构的五轴精加工机床。一个强有力的重点放在约束刚度优化。在用ABS、PLA等制造的熔融沉积成型（FDM）工件的精切的情况下，切削力应当明显小于在金属切削过程中通常观察到的金属材料的切削力。因此，本研究的主要重点是通过刚度优化来减小用于嵌入小型FDM 3D打印机的5轴机床的尺寸和重量，同时考虑所施加的切割力在末端执行器处的允许最大位移。因此，在找到FDM中精切削过程的5轴机床的最佳刚度之前，测量ABS和PLA精切削过程中的切削力。测量的ABS和PLA的平均切削力分别为2.02 N和3.50 N。此外，还证实了精切削可以改善FDM工艺生产的工件的表面粗糙度。在本研究中，设计了一个空间刚度的数学模型，其中包括使用卡氏第二定理导出的结构刚度和使用虚功定理导出的致动器刚度。然后进行基于遗传算法（GA）的优化，以最大限度地减少由于所施加的力-力矩的位移和最大允许位移60μm的末端执行器之间的差异。优化后，由切割力在末端执行器发生的位移与初始设计相比大大减少，并接近目标位移。
This paper deals with the design of a 3P2R structure based 5-axis machine tool used for finish cutting to improve the inherent poor surface roughness caused by the laminating based production method. A strong focus is placed on the constrained stiffness optimization. In the case of the finish cutting of Fused deposition modeling (FDM) workpieces produced with ABS, PLA, etc., the cutting forces should be significantly smaller than those of metallic materials generally observed in the metal cutting process. Thus, the main focus of this research is the reduced size and weight of the 5-axis machine tool for embedding into a small-sized FDM 3D printer by stiffness optimization while considering the allowable maximum displacement at the end-effector for the applied cutting forces. Thus, before finding the optimal stiffness of the 5-axis machine tool for the finish cutting process in FDM, the cutting forces in the finish cutting process of ABS and PLA are measured. The measured average cutting forces of ABS and PLA are 2.02 N and 3.50 N, respectively. In addition, it is confirmed that the surface roughness of workpieces produced by FDM process can be improved by finish cutting. A mathematical model of the spatial stiffness including both the structural stiffness derived using the Castigliano’s 2ndtheorem and the actuator stiffness derived using the virtual work theorem, is then devised in this research. Genetic algorithm (GA) based optimization is then performed to minimize the difference between the displacement due to the applied force-moment and the maximum allowable displacement of 60μm at the end-effector. After optimization, the displacements occurred by the cutting forces at the end-effector are considerably reduced compared with the initial design and are close to the target displacements.