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With the coming of the era of intelligence, machine vision and machine learning has become a research hotspot in recent years[1]. As an advanced manufacturing technology at present, 3D printing has been maturely applied in aerospace, bio-medicine and other fields[2]. However, a defect such as extruder head blockage, filament break, height error, warping and cracking occurred during the 3D printing process directly affects the printing quality and even the printing success rate. It is an inevitable trend to develop on-line monitoring on the health status of 3D printing devices to achieve unmanned operation of 3D printing. Therefore, this paper proposes a research on the on-line monitoring and compensation algorithm of 3D printing based on machine vision, which is significant to promote the development of 3D printing technology. The printing process usually takes certain time so it couldn’t be tracked and recognized by human eyes. Therefore, based on printing experiments and printing defect analysis, this paper comprehensively analyzes the monitoring mechanism, puts forward four monitoring elements and carries out certain theoretical analysis, aiming to realize realtime monitoring and improve printing success rate. Meanwhile, this paper analyzes the on-line monitoring by machine vision and compensation algorithm in theory, in order to guide the establishment of related experimental platform. Keywords-3D Printing; Defect Analysis; On-Line Monitoring; Theoretical Analysis

Experimental Research on On-line Monitoring and Compensation Algorithm of 3D Printing Based on Machine Vision