A layer-by-layer quality monitoring framework for 3D printing

Abstract

Technology development in additive manufacturing is accelerating transition from mass production to mass customization. In this transition, automation in all stages of production including quality control is a key. In this study, a layer-wise framework is proposed to monitor quality of 3D printing parts based on top-view images. The proposed statistical process monitoring method starts with self-start control charts that require only two successful initial prints. Answering the challenges of image processing due to lighting, a Machine Learning (ML) method is adopted to separate each layer from the printing bed. A sample image is compared to the standard image from a good part at each layer. The number of pixels in the difference images is fed into the proposed control charts to monitor printing process at each layer. An Exponentially Weighted Moving Average (EWMA) chart based on the number of pixels is used for process monitoring at each layer. Once enough parts have been printed, homogeneous layers are clustered to reduce the number of control charts needed for process monitoring. Experimental results based on a 3-inch diameter basket part show that the proposed framework based on continuously monitoring of layer-by-layer images is able of detecting small changes in printing process.