We introduce a novel neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. This advanced slicer can work on models with diverse representations and intricate topology. The approach involves employing neural networks to establish a deformation mapping, defining a scalar field in the space surrounding an input model. Isosurfaces are subsequently extracted from this field to generate curved layers for 3D printing. Creating a differentiable pipeline enables us to optimize the mapping through loss functions directly defined on the field gradients as the local printing directions. New loss functions have been introduced to meet the manufacturing objectives of support-free and strength reinforcement. Our new computation pipeline relies less on the initial values of the field and can generate slicing results with significantly improved performance.

翻译：我们提出了一种基于神经网络的新型计算流程，作为多轴3D打印中与表征无关的切片器。该先进切片器可处理具有不同表征形式和复杂拓扑结构的模型。该方法通过部署神经网络建立形变映射，在输入模型周边空间中定义标量场，随后从该场提取等值面以生成用于3D打印的曲形层。通过构建可微分计算流程，我们能够基于直接定义在场梯度上的损失函数（以局部打印方向为优化目标）对映射进行优化。针对无支撑打印和强度增强这两项制造目标，我们提出了新的损失函数。本新型计算流程降低了对场初始值的依赖，能够生成性能显著提升的切片结果。