The objective of this award is to establish methodologies for error prediction and control of geometric shapes associated with a wide range of additive manufacturing (AM) processes. The ultimate goal is to overcome a major barrier of direct digital manufacturing: inadequate dimensional accuracy in current AM processes. The proposed research strategy is to establish a methodological framework for system-level, smart, and high precision shape error compensation and control. The research plan consists of four research tasks: (1) modeling and prediction of profile deviations for complex shapes; (2) efficient experimental designs and analysis strategies for modeling profile deviations; (3) optimal compensation and smart shape-to-shape control of profile deviations, and (4) experimental investigation and validation.
Successful completion of the project is expected to significantly improve the geometric accuracy of AM-built products. The research will produce new knowledge regarding system-level or universal geometric shape accuracy control methodologies to reduce duplicated efforts for wider and quicker adoption of AM technologies, and high-precision and smart shape-to-shape compensation methodologies to avoid post-processing and extensive process calibration efforts prior to production. The research outcomes including patents will facilitate wider and quicker adoption of AM technology, spurring manufacturing innovations and job creation. The research project will provide new curriculum on AM technology and manufacturing education. Inter-university workshops and video conferences involving faculty and graduate students have been and will continue to be organized for synergistic collaborations.
