Computer vision-based evaluation of dimensional accuracy for MEAM in new product development

Evaluation of dimensional accuracy for parts is a key component of quality assurance for material extrusion based additive manufacturing (MEAM) in new product development. Computer vision-based frameworks are currently the most suitable methods for MEAM dimensional accuracy evaluation due to their non-contact measurement characteristic, rapidity and high accuracy. However, the previous computer vision-based frameworks in MEAM had the limitations of high camera cost and strict installation requirements. To address this issue, a cheap and accurate computer vision-based framework is proposed for MEAM based on a single mobile phone camera without strict installation requirements. First, camera is calibrated to reduce the influence of lens distortion. Then, dimensions are measured through projective transformation and scaling. To find the best calibration pattern, three calibration pattern named checkerboard, symmetric circle and asymmetric circle are investigated. The proposed framework is validated experimentally through printing tests of a cube and a cylinder fabricated by a fused deposition modeling (FDM) 3D printer. The result shows that camera calibration using asymmetric circular grid pattern has the highest accuracy and the proposed computer vision framework is validated as the maximum relative error is less than 1%.