In-Process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion

Abstract

The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock used in the process due to reasons such as poor cleaning of the machine after previous builds or inadequate quality control during production and storage of the powder. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time as the part is being built using in-process sensors. The central hypothesis is that transforming the process signals in the spectral graph domain leads to early and more accurate detection of material cross-contamination in L-PBF compared to the traditional delay-embedded Bon-Jenkins stochastic time series analysis techniques, such as autoregressive (AR) and autoregressive moving average (ARMA) modeling. To test this hypothesis, Inconel alloy 625 (UNS alloy 06625) test parts were made at Edison Welding Institute (EWI) on a custom-built L-PBF apparatus integrated with multiple sensors, including a silicon photodetector (with 300 nm to 1100 nm optical wavelength). During the process, two types of foreign contaminant materials, namely, tungsten and aluminum particulates, under varying degrees of severity were introduced. To detect cross-contamination in the part, the photodetector sensor signatures were monitored hatch-by-hatch in the form of spectral graph transform coefficients. These spectral graph coefficients are subsequently tracked on a Hotelling T2 statistical control chart. Instances of Type II statistical error, i.e., probability of failing to detect the onset of material cross-contamination, were verified against X-ray computed tomography (XCT) scans of the part to be within 5% in the case of aluminum contaminant particles. In contrast, traditional stochastic time series modeling approaches, e.g., ARMA, had corresponding Type II error exceeding 15%. Furthermore, the computation time for the spectral graph approach was found to be less than one millisecond, compared to nearly 100 ms for the traditional time series models tested.

Quality by Design for industry translation: Three‐dimensional risk assessment failure mode, effects, and criticality analysis for additively manufactured patient‐specific implants

“…In the case of EBM systems, this FM was ranked as the fifth most critical, whereas for DMLS machines this FM was ranked as the 10th most critical. The reason for this difference is that the occurrence of metallizations in EBM machines is much higher than in DMLS machines . The high occurrence of metallizations in EBM systems is due to the combination of high temperatures and the vacuum atmosphere during the fabrication process.…”

“…Blasting is one of the postfabrication activities necessary to reveal and remove the manufactured parts from the powder bed. Crosscontamination of the powder material used for AM is a common issue that occurs in AM when impurities, foreign bodies during preprocessing and postprocessing activities get into the powder . Crosscontamination can also occur when a machine is used to produce batches with different feedstock materials.…”

“…Crosscontamination can also occur when a machine is used to produce batches with different feedstock materials. These impurities can create interstitials in the build parts leading to alterations in the chemical composition and detrimental effects in the mechanical properties of the final product . On the other hand, during the sand blasting process, delicate areas of the implant can be damaged and inclusion of foreign particles within the trabecular and lattice structures of patient‐specific implants can occur .…”

Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

“…This could be worsen when metals and alloys are used and could endanger the system where weak or damaged components are used in safety-critical systems, potentially endanger human lives [39,40]. Similarly, any alteration in nozzle speed or motion of printer head in additive manufacturing could result in lower performance [41][42][43].…”

Development of a Cone CVT by SDPD and Topology Optimization

“…The other way to increase the precision and repeatability of the fabricated conical rollers is to employ online monitoring and control of the process. Several systems have been developing to monitor and/or remove the microstructural and macro-structural defects of the fabricated parts [22][23][24][25][26]. Specifically, the porous structures are at risk of damages more than simple solid structures [27,28].…”