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One of the major barriers that hinder the realization of significant potential of metal-based additive manufacturing (AM) techniques is the variation in the quality of the manufactured parts. Uncertainty quantification (UQ) and uncertainty management (UM) can resolve this challenge based on the modeling and simulation of the AM process. This paper reviews the research state of the art and discusses needs and opportunities in the UQ/UM of the AM processes, with a focus on laser powder bed fusion AM. The major methods and models of laser powder bed fusion AM process are summarized first. The current research work in UQ of AM processes is then reviewed. Based on the review of AM process models and current UQ approaches for the AM process, this paper presents insights into how the current state of the art UQ and UM techniques can be applied to AM to improve the product quality. Future research needs in UQ and UM of AM are also discussed. Laser sintering of metal nanoparticles, which is part of the micro-AM process, is used as an example to illustrate the application of UQ and UM in the AM.

This paper proposes formulations and algorithms for reliability-based design optimization (RBDO) of both single and multidisciplinary systems under both aleatory uncertainty (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information). The proposed formulations specifically deal with epistemic uncertainty arising from interval data. When the only information available for an input variable is in the form of interval data, it is likely that the distribution type for the input variable is not known or cannot be specified accurately. This paper uses a four-parameter flexible Johnson family of distributions to represent the uncertainty described by interval data. An efficient approach is proposed to decouple the design analysis from the uncertainty analysis. The proposed methodology for multidisciplinary system optimization does not require any coupled system level analysis. The proposed methods are illustrated through several example problems.

Recent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are implemented after predicting the state of the manufacturing process or product quality at a future time using process models. In a cyber-physical manufacturing process, the product quality predictions may be affected by uncertainty sources from the computing subsystem (resource and communication uncertainty), manufacturing process (input uncertainty, process variability and modeling errors), and sensors (measurement uncertainty). In addition, due to the continuous interactions between the computing subsystem and the manufacturing process, these uncertainty sources may aggregate and compound over time. In some cases, some process parameters needed for model predictions may not be precisely known and may need to be derived from real time sensor data. This paper develops a dynamic Bayesian network approach, which enables the aggregation of multiple uncertainty sources, parameter estimation and robust prediction for online control. As the number of process parameters increase, their estimation using sensor data in real-time can be computationally expensive. To facilitate real-time analysis, variance-based global sensitivity analysis is used for dimension reduction. The proposed methodology of online monitoring and control under uncertainty, and dimension reduction, are illustrated for a cyber-physical turning process.

An essential issue in surrogate model-based reliability analysis is the selection of training points. Approaches such as efficient global reliability analysis (EGRA) and adaptive Kriging Monte Carlo simulation (AK-MCS) methods have been developed to adaptively select training points that are close to the limit state. Both the learning functions and convergence criteria of selecting training points in EGRA and AK-MCS are defined from the perspective of individual responses at Monte Carlo samples. This causes two problems: (1) some extra training points are selected after the reliability estimate already satisfies the accuracy target; and (2) the selected training points may not be the optimal ones for reliability analysis. This paper proposes a Global Sensitivity Analysis enhanced Surrogate (GSAS) modeling method for reliability analysis. Both the convergence criterion and strategy of selecting new training points are defined from the perspective of reliability estimate instead of individual responses of MCS samples. The new training points are identified according to their contribution to the uncertainty in the reliability estimate based on global sensitivity analysis. The selection of new training points stops when the accuracy of the reliability estimate reaches a specific target. Five examples are used to assess the accuracy and efficiency of the proposed method. The results show that the efficiency and accuracy of the proposed method are better than those of EGRA and AK-MCS.

This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning models for predicting the bond quality and porosity of fused filament fabrication (FFF) parts. Three types of strategies are explored to incorporate physics constraints and multi-physics FFF simulation results into a deep neural network (DNN), thus ensuring consistency with physical laws: (1) incorporate physics constraints within the loss function of the DNN, (2) use physics model outputs as additional inputs to the DNN model, and (3) pre-train a DNN model with physics model input-output and then update it with experimental data. These strategies help to enforce a physically consistent relationship between bond quality and tensile strength, thus making porosity predictions physically meaningful. Eight different combinations of the above strategies are investigated. The results show how the combination of multiple strategies produces accurate machine learning models even with limited experimental data.

This article describes a novel method for detecting flaws in curing FRP composite materials while they are being manufactured. Such a method can improve the efficiency of the manufacturing process by minimizing, or potentially eliminating, the need for post-manufacturing inspection. The method utilizes a Kalman filter, a heat conduction model, and surface temperature measurements from infrared thermography to identify likely locations of flaw and/or curing anomalies. Specifically, a methodology that compares a metric of the time-history of Kalman filter corrections at different spatial locations to identify anomalous curing behavior was developed. Several numerical studies were performed using a previously-validated model to determine the proficiency of the technique. Results of the verification studies indicated that the proposed method was effective at identifying resin-rich regions without any modification to the detection criteria, while identifying resin-deficient regions required a more lenient detection criterion. In the case of multiple flaws, the proposed method was always able to identify the flaw closer to the surface, regardless of flaw significance, while the deeper flaw was only identified when the flaw was more significant than the near-surface flaw. The proposed method demonstrates promise for passive IR thermography-based flaw detection performed during the manufacturing of FRP composites and can serve to both improve the efficiency of the manufacturing process and the quality of FRP composite parts. Further experimental studies are required for validation of the technique before it can be applied for industrial application.

Identifying influential spreaders in networks, which contributes to optimizing the use of available resources and efficient spreading of information, is of great theoretical significance and practical value. A random-walk-based algorithm LeaderRank has been shown as an effective and efficient method in recognizing leaders in social network, which even outperforms the well-known PageRank method. As LeaderRank is initially developed for binary directed networks, further extensions should be studied in weighted networks. In this paper, a generalized algorithm PhysarumSpreader is proposed by combining LeaderRank with a positive feedback mechanism inspired from an amoeboid organism called Physarum Polycephalum. By taking edge weights into consideration and adding the positive feedback mechanism, PhysarumSpreader is applicable in both directed and undirected networks with weights. By taking two real networks for examples, the effectiveness of the proposed method is demonstrated by comparing with other standard centrality measures.

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach.

This work presents a Bayesian methodology for layer-by-layer predictive quality control of an additively manufactured part by integrating physics-based simulation with online monitoring data. The model and the sensor data are first used to infer porosity in the printed layers, prediction of porosity in future layers, and adjustment of process parameters. Since porosity is not directly observable during the printing process, the temperature profile obtained from the monitoring (using an infra-red thermal camera) is used to infer porosity in the finished part. The porosity inference model is constructed by first reducing the dimension of the thermal images by employing singular value decomposition. Next, in process control, the porosity in the final part is predicted, and if this predicted porosity is more than a desired threshold, the process parameters for printing the next layer are adjusted based on optimization. To ensure that the prediction model is both fast and accurate, the expensive finite element model is replaced by a surrogate model, and a discrepancy term calibrated using experimental data is used to correct the surrogate model prediction. The prediction model is also updated at every layer based on the monitoring data, and the updated model is used to predict the porosity in the final part. The effectiveness of the proposed method is demonstrated for controlling porosity in laser powder bed fusion by changing the process parameters such as laser power and laser speed.

A supply chain is a system which moves products from a supplier to customers, which plays a very important role in all economic activities. This paper proposes a novel algorithm for a supply chain network design inspired by biological principles of nutrients＇ distribution in protoplasmic networks of slime mould Physarum polycephalum. The algorithm handles supply networks where capacity investments and product flows are decision variables, and the networks are required to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of flux during the iterative process, which is used in real-time updating of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Numerical examples are provided to illustrate the practicality and flexibility of the proposed method algorithm.