全流程 3D 打印仿真
金属增材制造仿真平台END-TO-END 3D PRINTING SIMULATION
METAL AM SIMULATION PLATFORM
让金属 3D 打印从试错，变成可预测工程。上传零件、材料与设备参数，云熔智算YunRong在打印前预测变形、裂纹、孔隙、组织与性能，并给出可执行的工艺优化建议。 Turn metal 3D printing from trial-and-error into predictable engineering. Upload your part, material, and machine parameters — 云熔智算YunRong predicts distortion, cracking, porosity, microstructure, and properties before you print, and delivers actionable process optimization.
颗粒尺度的离散元仿真重现真实粉末床 — 摩擦、范德华力、颗粒分级一颗不漏。这是后续所有物理的起点。
Particle-scale discrete-element simulation replicates real powder beds — friction, van der Waals, size segregation, every grain modeled. This is where every later physics step starts.
激光扫过的瞬间发生了什么？热流、马兰戈尼对流、蒸发反冲压、匙孔失稳 — 多物理场耦合实时求解，温度场以颜色直接显示。
What happens the instant the laser passes? Heat flow, Marangoni convection, evaporation recoil, keyhole instability — all multi-physics fields coupled and solved in real time. Color encodes temperature.
熔池凝固时晶粒是怎么长出来的？相场建模逐层模拟形核、长大、粗化 — 不同晶粒方向用不同颜色，让"看不见的合金组织"变成可视可量化的数据。
How do grains grow as the melt pool solidifies? Phase-field modeling traces nucleation, growth and coarsening layer-by-layer — each grain orientation a distinct color, turning invisible microstructure into quantitative data.
层层堆积后，残余应力如何分布？一个 3D 色谱清晰展示从蓝色（拉应力 −130 MPa）到红色（压应力 3.2 GPa）的真实分布 — 直接告诉你哪儿会翘曲、哪儿会裂。
After layers stack up, how is residual stress distributed? A 3D color map shows the real distribution from blue (tension −130 MPa) to red (compression 3.2 GPa) — telling you exactly where warpage and cracking will appear.
从微观尺度的应力分布出发，外推到宏观零件的延展性、疲劳寿命与断裂行为 — 让工程师在打印之前就知道这个零件能不能用、能用多久。
From microscopic stress distributions out to bulk ductility, fatigue life and fracture behavior — engineers know if the part will work, and how long it will last, before it's ever printed.
打印前在虚拟环境中完整模拟过程，预测缺陷与形变。在已验证案例中，仿真显著降低了金属打印常见的 20–30% 失败率，提升首件成功的把握。
Run the full print in a virtual environment first — defects and distortion predicted before you commit material. In validated cases, simulation markedly reduced the 20–30% failure rate typical of metal AM.
在仿真中完成参数迭代，找到最优工艺窗口后再实际打印。在典型工艺开发项目中，目标是把 3–7 次试印压缩到 1–2 次 — 每一次试错都是上万元的粉末与几十个小时。
Iterate in silico, find the optimal process window, then print. In a typical process-development project, the goal is to compress 3–7 trial prints down to 1–2 — every avoided trial saves tens of thousands in powder and dozens of hours.
云熔智算的 AI 助手 SimuAgent 自动设置参数、自动生成报告。原本需要数周的工艺摸索，在 AI 辅助下有望压缩到数天。
The SimuAgent assistant auto-configures parameters and writes the report. Weeks of process-window hunting can shrink to days with AI assistance.
仿真不是 demo — 每一个预测都拿到实验台上逐项比对。钨倒铜复合材料 EBAM 案例中，仿真与实验达到 90% 一致性。
Simulation isn’t a demo — every prediction is matched against experiment, line by line. In the W/Cu EBAM case, sim and experiment agreed at 90%.
以上为典型工艺开发项目的目标区间与已验证案例的结果；实际效果因材料、设备与零件而异。 Figures above reflect targets in typical process-development projects and results from validated cases; actual outcomes vary by material, machine and part.
多物理场耦合仿真引擎，覆盖流场、热场、电磁场，从微观晶粒生长到宏观机械性能逐层重现 3D 打印过程。每一个仿真结果都经过实验验证。 A multi-physics coupled simulation engine spanning flow, thermal and electromagnetic fields — reproducing the entire AM process from grain-scale microstructure up to bulk mechanical properties, every result experimentally validated.
40+ 仿真功能，10+ 独有。从粉末铺设到熔池演化、微观结构生长、残余应力，到最终零件机械性能 — 一个引擎，一套数据。 40+ simulation functions, 10+ proprietary. From powder bed to melt-pool dynamics, microstructure evolution and residual stress, all the way to final part properties — one engine, one data flow.
同等精度下，CPU 与 GPU 各有原生求解器；百万网格算例：RTX 3070 较 20 核 CPU 提速 2.5 倍，A800 提速 15 倍 — 让仿真不再是瓶颈。 At matched accuracy — with dedicated CPU- and GPU-native solvers — a one-million-cell case runs 2.5× faster on an RTX 3070 than a 20-core CPU, and 15× faster on an A800 — simulation stops being the bottleneck.
SimuAgent AI 助手把仿真所需的几十个参数压成一句话需求。自动建模、自动求解、自动出分析报告 — 工程师不必再是 CAE 专家。 SimuAgent collapses dozens of simulation parameters into a single natural-language request. It auto-builds the model, runs the solver, and writes the analysis report — your engineers no longer need to be CAE specialists.
纳入保护气、载气与粉束轨迹耦合求解，定位粉末利用率偏低的真实原因，仿真熔池尺寸与实验一致。
Coupled shielding-gas, carrier-gas and particle trajectories pinned the root cause of low powder efficiency. Predicted melt-pool dimensions matched experiment.
查看完整案例Full case仿真定位失败根因，结合多道次重熔策略，仅用一个月在实验中实现强化颗粒均匀分布。
Simulation identified root cause; a multi-pass remelting strategy delivered uniform reinforcement-particle distribution in one month.
查看完整案例Full case元素蒸发模型预测不同功率下 Mn 损失速率，与实验测量偏差 < 0.1 wt.%，客户据此预补偿粉末成分。
An element-evaporation model predicted Mn loss per process window within 0.1 wt.% of measurement, letting the customer pre-compensate the alloy.
查看完整案例Full case
提交一个简短的需求，我们的应用工程师会在 48 小时内回复你 — 评估可行性、给出仿真路径、安排 Demo。 Send a short brief. Our application engineers reply within 48 hours — feasibility check, simulation plan, Demo slot.
