Additively manufactured pure copper electrodes can hold high electric fields with low breakdown rates, making them a viable candidate for accelerator component manufacturing.
Additive manufacturing, particularly inkjet printing, is revolutionizing pharmaceutical production by enabling the fabrication of personalized, complex, and efficient drug delivery systems with precise control over dosage forms and release profiles.
A deep reinforcement learning-based framework is developed to generate optimized toolpaths that achieve uniformly distributed temperature fields and avoid extreme thermal accumulation during the laser powder bed fusion process.
An automated machine learning-based architecture for accurate and efficient extraction of material parameters, including ink conductivity and dielectric properties, of inkjet printed microwave components from a single set of measurements.
Optimizing extrusion force control in 3D printing through Bayesian optimization and transfer learning.
Force Controlled Printing in material extrusion additive manufacturing offers superior print quality, disturbance rejection, and adaptability to hardware changes.
The development of a scalable PBF thermal history simulation based on CAPL and validated by melt pool images is crucial for understanding and improving LPBF processes.