Abstract: A generalized additive modeling approach to separate global geometric shape deformation from surface roughness is provided. Under this statistical framework, tensor product basis expansion is adopted to learn both the low-order shape deformation and high-order roughness patterns. The established predictive model enables the optimal geometric compensation for product redesign to reduce shape deformation from the target geometry without altering process parameters. Experimental validation on WAAM manufactured cylindrical walls of various radi shows the effectiveness of the proposed framework.

Abstract: Methods and systems for predicting deformation error and compensating for shape deviation in Additive Manufacturing (AM) techniques include, in one aspect, a method including: obtaining a deformation model for an AM machine; predicting deformation for the object using the deformation model applied to the 3D model; selecting an amount of deformation compensation to effect by minimizing deviation for the predicted deformation; and providing the selected amount of deformation compensation to modify the 3D model to compensate for deformation during creation by the AM machine.

Abstract: Methods and systems for predicting deformation error and compensating for shape deviation in Additive Manufacturing (AM) techniques include, in one aspect, a method including: obtaining a deformation model for an AM machine; predicting deformation for the object using the deformation model applied to the 3D model; selecting an amount of deformation compensation to effect by minimizing deviation for the predicted deformation; and providing the selected amount of deformation compensation to modify the 3D model to compensate for deformation during creation by the AM machine.