Additive manufacturing, or 3-D printing, is an emerging technology that enables the direct fabrication of complex shapes. The material solidification in 3-D printing, however, may yield geometric shape deviation in the 3-D-printed parts, which adversely affects product quality and performance in engineering applications. The advancement in sensing technology enables us to collect measurement data to facilitate automated quality inspection for 3-D printing. The objective of this study is to develop a data fusion method that can effectively fuse the different sources of sensor data and process data to evaluate the quality of 3-D printed parts. Specifically, this study fuses the information extracted from three data sources with different accuracy and measurement efficiency to evaluate the geometric shape deviation of the printed parts. Significant features about part quality are extracted from the 3-D surface measurement data using principal component analysis (PCA) and uncorrelated multilinear PCA. Selected features are then fed into classification and regression to infer the quality of the part. The proposed prediction model starts with a preliminary classifier for the initial inspection using only low-resolution 3-D surface measurement, followed by a more refined classifier trained by high-resolution 3-D surface measurement to update the quality class labels. Such a two-level classification model is able to guarantee time efficiency while maintaining high accuracy. Results show that the proposed fusion method is promising for efficient and automated quality inspection.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- 3-D printing
- Feature extraction
- multimodal data fusion
- quality prediction
- surface contour