A cost-effective and reliable measurement strategy for 3D printed parts by integrating low- and high-resolution measurement systems
提出一种联合使用低分辨率和高分辨率测量系统的策略，通过贝叶斯模型整合数据，在降低成本的同时提高3D打印零件的测量精度，适用于制造业质量控制。
Metrology data are crucial to quality control of three-dimensional (3D) printed parts. Low-cost measurement systems are often unreliable due to their low resolutions, whereas high-resolution measurement systems usually induce high measurement costs. To balance the measurement cost and accuracy, a new cost-effective and reliable measurement strategy is proposed in this article, which jointly uses two-resolution measurement systems. Specifically, only a small sample of base parts are measured by both the low- and high-resolution measurement systems in order to save costs. The measurement accuracy of most parts with only low-resolution metrology data is improved by effectively integrating high-resolution metrology data of the base parts. A Bayesian generative model parameterizes a part-independent bias and variance pattern of the low-resolution metrology data and facilitates a between-part data integration via an efficient Markov chain Monte Carlo sampling algorithm. This multi-part two-resolution metrology data integration highlights the novelty and contribution of this article compared with the existing one-part data integration methods in the literature. Finally, an intensive experimental study involving a laser scanner and a machine visual system has validated the effectiveness of our measurement strategy in acquisition of reliable metrology data of 3D printed parts.