Multi-view online vision detection based on robot fused deposit modeling 3D printing technology

Abstract

Purpose

Fused deposit modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts. This paper presents a solution to the problem of detecting defects on the outer surface in the additive process of FDM.

Design/methodology/approach

A multi-view and all-round vision detection method is introduced where the detection field of view is changing with the vector of the outer surface during the printing process on the six degrees of freedom robot FDM printer.

Findings

After the image is preprocessed, this paper can identify the defects effectively according to its laminate structure, and introduces a mathematical matrix to represent the defects which will be classified into three typical types according to the geometry shape and area distribution.

Research limitations/implications

This research only focuses on the feasibility of the defects detection method. To create the object of high quality, more research is needed to account for the process parameters which significantly cause the defects.

Practical implications

This work will help to detect the defects online, monitor the printing quality of the outer surface, reduce the waste of printed filaments, etc.

Originality/value

This study is among the first to present a multi-view and all-round vision detection method to detect defects on the outer surface in the additive process of FDM; proposes a means of identifying defects according to its laminate structure; and introduces a mathematical matrix to represent the defects which may be used in quality assessment.

Citation

Shen, H., Sun, W. and Fu, J. (2019), "Multi-view online vision detection based on robot fused deposit modeling 3D printing technology", Rapid Prototyping Journal, Vol. 25 No. 2, pp. 343-355. https://doi.org/10.1108/RPJ-03-2018-0052