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Czinger debuts its topology-optimized BrakeNode integrated brake assembly on the 21C Spyder, reducing unsprung mass and simplifying service. The production-ready component is lighter than conventional brakes and available across Czinger models.
This paper proposes a neuro-symbolic closed-loop architecture for laser powder bed fusion, where an in-loop ontology couples symbolic reasoning with statistical learning to control melt pool depth and eliminate overhang dross. Feasibility is demonstrated via a surrogate calibrated to the NIST AM-Bench benchmark.
A free comprehensive course on 3D printing and additive manufacturing, covering core technologies, materials, design, and applications, is now available on freeCodeCamp.
Introduces DiffARFNO, a two-stage framework combining autoregressive Fourier-MIONet with a conditional DDIM corrector for long-horizon droplet evolution prediction in inkjet printing, achieving state-of-the-art performance on ANSYS Fluent datasets.
The Czinger 21C hybrid hypercar uses 3D-printed components and generative design from parent company Divergent Technologies, achieving exceptional performance and lap records.
This paper proposes a data-driven surrogate modeling framework using a hybrid Graph Neural Network-Long Short-Term Memory architecture to predict the static response of additively manufactured short-fiber thermoplastics, achieving high accuracy (R²≈0.98) and two orders of magnitude speedup over finite element simulations.
RocketSmith is an agentic system that uses large language models to automate the design and additive manufacturing of high-powered rockets, achieving successful flight tests with simulation results matching 80% of predicted apogee.
This paper presents a hybrid machine learning approach for real-time melt pool monitoring in laser powder bed fusion additive manufacturing, combining EfficientNetB0 feature extraction with Random Forest classification to achieve high accuracy and sub-millisecond inference time.
This paper proposes a novel architecture integrating multi-head attention with the Soft Actor-Critic algorithm for porosity prediction and process parameter optimization in additive manufacturing, achieving faster convergence and higher rewards than standard RL methods.