Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers

Abstract

Statistical process monitoring (SPM) methods have been adopted and studied to detect variations in the fused deposition modeling (FDM) process in recent years. The FDM process that builds parts layer-by-layer is accomplished in a specified manufacturing period (number of layers) without interruption or suspension. Thus, traditional SPM methods, where the average run length is used for the calculation of the control limits and the measurement of the performance, are no longer applicable to the FDM process. In this paper, an SPM method is proposed based on the surface image data of FDM parts with consistent layers and a specified period. The probability of alarm in a specified period (PASP) and the cumulative PASP are introduced to determine the control limits and evaluate the monitoring performance. Regions of interest are determined in a fixed way to cover the sizes and locations of different defects. The statistics are calculated based on the generalized likelihood ratio. The control limit is determined based on the specified period and the nominal in-control PASP. A simulation study for different locations, sizes and magnitudes of the mean shift of defects is presented. In the case study, the proposed SPM method is applied to monitor the FDM process of a cuboid, which verifies the effectiveness of the proposed method.