Existing curved-layer-based process planning methods for multi-axis manufacturing address collisions only indirectly and generate toolpaths in a post-processing step, leaving toolpath geometry uncontrolled during optimization. We present an implicit neural field-based framework for multi-axis process planning that overcomes these limitations by embedding both layer generation and toolpath design within a single differentiable pipeline. Using sinusoidally activated neural networks to represent layers and toolpaths as implicit fields, our method enables direct evaluation of field values and derivatives at any spatial point, thereby allowing explicit collision avoidance and joint optimization of manufacturing layers and toolpaths. We further investigate how network hyperparameters and objective definitions influence singularity behavior and topology transitions, offering built-in mechanisms for regularization and stability control. The proposed approach is demonstrated on examples in both additive and subtractive manufacturing, validating its generality and effectiveness.

The new funding will enable Ubie to accelerate its growth and strengthen its presence in the U.S., following strong interest and traction in that market. To date, Ubie has raised $76 million in total. It will also focus on expanding its business to the U.S. in order to apply the technology it has developed in Japan, one of the leading countries in the medical field. This is the second overseas corporation following Singapore. The decision follows the fact that the number of users has been increasing steadily since the release of the AI symptom checker in April 2022 in the U.S. The establishment of the local subsidiary will further strengthen the partnership with U.S. pharmaceutical companies, as well as the products, including hiring local people in the U.S. Ubie is a Japanese health-tech startup founded by a medical doctor and an engineer in 2017.