Smart Calibration Through Deep Learning for High-Confidence and Interoperable Cyber-Physical Additive Manufacturing Systems

As an indispensable link of the life-cycle of AM, end-part quality control in Cyber-Physical Additive Manufacturing Systems (CPAMS) is made difficult by enormous differences in product designs/varieties. Statistical monitoring of additive manufacturing (AM) processes faces major challenge due to the nature of one-of-a-kind manufacturing. This posters puts forth a prescriptive SPC scheme to monitor shape deformation from shape to shape. Only a limited number of test shapes are required to establish control limits. The strategy is extension of our previous work on the prescriptive modeling and compensation of shape deformation to the field of SPC. Experimental investigation using stereolithography process validates the proposed deformation monitoring approach.