The objective of this work is to identify failure modes and detect the onset of process anomalies in Additive Manufacturing (AM) processes, specifically focusing on Fused Filament Fabrication (FFF). We accomplish this objective using advanced Bayesian non-parametric analysis of in situ heterogeneous sensor data. The proposed method can ultimately lead to intelligent decision making and closed loop control in AM processes. Experiments are conducted on a desktop FFF machine (MakerBot Replicator 2X) instrumented with a heterogeneous sensor array including thermocouples, accelerometers, an infrared temperature sensor, and a real-time miniature video borescope. FFF process failures are detected online using the non-parametric Bayesian Dirichlet Process (DP) mixture model and evidence theory based on the experimentally acquired sensor data. This sensor data-driven defect detection approach facilitates real-time identification and correction of FFF process drifts with an accuracy and precision approaching 85% (average F-score). In comparison, the F-score from existing approaches, such as Probabilistic Neural Networks, Naïve Bayesian Clustering, Support Vector Machines, and Quadratic Discriminant Analysis was in the range of 55% to 75%.

This paper focuses on the in-mold monitoring of temperature and cavity pressure. The melt contact temperature and the cavity pressure along the flow path were directly measured using two pressure sensors and two temperature sensors fitted into the cavity of a spiral mold. Three melt temperatures and dies of different heights (1.0, 1.5 and 2 mm) were used to achieve a wide range of practically relevant shear rates. In order to analyze the extent to which the numerical simulation can predict the behavior of the molten polymer during the injection molding process, molding experiments were simulated using the Moldflow software and the simulation results were compared with the experimental data under the same injection molding conditions.

In semiconductor fabrication processes, reliable feature extraction and condition monitoring is critical to understanding equipment degradation and implementing the proper maintenance decisions. This paper presents an integrated feature extraction and equipment monitoring approach based on standard built-in sensors from a modern 300mm-technology industrial Plasma Enhanced Chemical Vapor Deposition (PECVD) tool. Linear Discriminant Analysis was utilized to determine the set of dynamic features that are the most sensitive to different tool conditions brought about by chamber cleaning. Gaussian Mixture Models of the dynamic feature distributions were used to statistically quantify changes of these features as the condition of the tool changed. Data was collected in the facilities of a well-known microelectronics manufacturer from a PECVD tool used for depositing various thin films on silicon wafers, which is one of the key steps in semiconductor manufacturing. Dynamic features coming from the radio frequency (RF) plasma power generator, matching capacitors, pedestal temperature, and chamber temperature sensors were shown to consistently have significant statistical changes as a consequence of repeated cleaning cycles, indicating physical connections to the chamber condition.