Smart Calibration Through Deep Learning for High-Confidence and Interoperable Cyber-Physical Additive Manufacturing Systems
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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.
Submitted by Qiang Huang
on
pdf
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.
Submitted by Qiang Huang
on