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the average run length is used for the calculation of the control limits and the measurement of the performance, are no longer applicable to the FDM process.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.

近年来，已经采用和研究了统计过程监控（SPM）方法来检测熔融沉积建模（FDM）过程中的变化。在指定的制造周期（层数）中完成逐层构建零件的FDM工艺，而不会中断或暂停。因此，传统的SPM方法不再适用于FDM过程，在传统的SPM方法中，平均运行长度用于控制极限的计算和性能的测量。本文提出了一种基于层数一致，周期指定的FDM零件表面图像数据的SPM方法。引入指定时间段内的警报概率（PASP）和累积PASP来确定控制限制并评估监视性能。以固定的方式确定目标区域，以覆盖不同缺陷的大小和位置。统计是基于广义似然比计算的。控制极限是根据指定的时间段和标称的控制中PASP确定的。提出了针对缺陷的平均位移的不同位置，大小和大小的仿真研究。在案例研究中，将所提出的SPM方法应用于监测长方体的FDM过程，从而验证了所提出方法的有效性。给出了缺陷平均位移的大小和大小。在案例研究中，将所提出的SPM方法应用于监测长方体的FDM过程，从而验证了所提出方法的有效性。给出了缺陷平均位移的大小和大小。在案例研究中，将所提出的SPM方法应用于监测长方体的FDM过程，从而验证了所提出方法的有效性。