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：我们提出一种新颖的基于神经网络的计算流程，作为面向多轴3D打印的、与表示方法无关的切片器。这一先进切片器能够处理具有多样化几何表示和复杂拓扑结构的模型。该方法通过神经网络建立变形映射，在输入模型周围的空间中定义标量场。随后从该场中提取等值面，以生成用于3D打印的曲面层。通过构建可微分计算流程，我们能够直接基于场梯度（即局部打印方向）定义的损失函数对映射进行优化。为满足无支撑和强度增强的制造目标，我们引入了新的损失函数。新的计算流程对场的初始值依赖更小，并能生成性能显著提升的切片结果。