In recent years, additive manufacturing has been developing rapidly mainly due to the ease of fabricating complex components. However, complex structures with overhangs inevitably require support materials to prevent collapse and reduce warping of the part. In this paper, the effects of process parameters on printable bridge length (PBL) are investigated. An optimisation is conducted to maximise the distance between support points, thus minimising the support usage. The orthogonal design method is employed for designing the experiments. The samples are then used to train a neural network for predicting the nonlinear relationships between PBL and process parameters. The results show that the established neural network can correctly predict the longest PBL which can be integrated into support generation process in additive manufacturing for maximising the distance between support points, thus reducing support usage. A framework for integrating the findings of this paper into support generation process is proposed.

近年来，增材制造得到了快速发展，这主要是由于易于制造复杂的组件。但是，带有悬挑的复杂结构不可避免地需要支撑材料来防止塌陷并减少零件的翘曲。本文研究了工艺参数对可印刷桥长（PBL）的影响。进行优化以最大化支撑点之间的距离，从而最小化支撑物的使用。采用正交设计法进行实验设计。然后将样本用于训练神经网络，以预测PBL和过程参数之间的非线性关系。结果表明，所建立的神经网络可以正确预测最长的PBL，该最长的PBL可以集成到增材制造的支撑生成过程中，以最大化支撑点之间的距离，从而减少支撑的使用。提出了一个将本文的发现整合到支持产生过程中的框架。