An error identification and compensation method for Cartesian 3D printer based on specially designed test artifact

Abstract

The printing accuracy is one of the most important metrics to evaluate the additive manufacturing (AM) machine. In this paper, an error identification and compensation method for Cartesian 3D printer is presented based on a specially-designed test artifact to improve printing accuracy. The relationship between the geometric errors of the printed object and the kinematic errors of the printer axes is established based on the theory of the multi-body system. A series of formulas are derived to separate the kinematic errors of each axis from the geometric errors. To extract the geometric errors required for the mathematical calculations, an artifact with the special features is proposed and printed. The geometric errors of the characteristic points on the artifact is measured by a coordinate measuring machine (CMM). From the measured geometric errors, kinematic errors of the printer can be identified, and can be further compensated by adjusting the CAD model of the object. Two compensated algorithms are established; one uses the fitted curves of the kinematic errors, and the other uses the average kinematic error values. Printing tests and case studies are performed to verify the effectiveness of the proposed method. The results show that the proposed method can improve printing accuracy of the Cartesian 3D printer.

Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System

“…They developed a fuzzy logic-based approach for designfor-AM to manage uncertainties in material properties while meeting the quality standards of 3D printed objects. Holzmond and Li [20] developed a system that detects two common 3D printing errors: filament blockages and low flow. They use a digital image correlation system to compare the point cloud captured from the printer-head movement program (g-code) and the point cloud of a printed surface in real-time.…”

Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System

“…They developed a fuzzy-logic-based approach for design for AM to manage uncertainties in material properties while meeting the quality standards of 3D-printed objects. Holzmond and Li [20] developed a system that detects two common 3D printing errors: filament blockages and low flow. They used a digital image correlation system to compare the point cloud captured from the printer-head movement program (g-code) and the point cloud of a printed surface in real time.…”