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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.

Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.

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Part design and process parameters directly influence the instantaneous spatiotemporal distribution of temperature in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as the thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step toward this goal, the objectives of this work are two-fold. First, to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts. Second, validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different material deposition speeds. In the specific context of FFF, this foray is the critical first-step toward understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high degree of correlation therein, with a mean absolute percentage error less than 6% (root mean squared error <6 °C). This work thus presents one of the first efforts in validating thermal profiles in FFF via direct in situ measurement of the temperature. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

In this article, the design problem of switching gain-scheduled output-feedback (SGSOF) controller with mixed  2 ∕ ∞ performance exploiting inexact scheduling parameters for continuous-time linear parameter-varying (LPV) systems is tackled. The absolute and proportional uncertainties on the scheduling parameters are considered. All the system matrices of the LPV plant are supposed to have polynomial dependency of arbitrary degree on the scheduling parameters. By exploiting hysteresis switching law and utilizing two independent families of parameter-dependent Lyapunov matrices and a family of slack variables, introducing additional degrees of freedom, performance improvement is achieved. A design approach, involving a two-step algorithm, is proposed in terms of solutions to a set of parameter-dependent linear matrix inequalities (LMIs) including parameter searches for two scalar values. The merit of the proposed method lies in its less conservativeness in comparison with the available approaches in the literature. Numerical studies are carried out to demonstrate the effectiveness of the presented method.

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