Selective laser melting (SLM) is a commonly used technique in additive manufacturing to produce metal components with complex geometries and high precision. However, the poor process reproducibility and unstable product reliability has hindered its wide adoption in practice. Hence, there is a pressing demand for in-situ quality monitoring and real-time process control. In this paper, a feature-level multi-sensor fusion approach is proposed to combine acoustic emission signals with photodiode signals to realize in-situ quality monitoring for intelligence-driven production of SLM. An off-axial in-situ monitoring system featuring a microphone and a photodiode is developed to capture the process signatures during the building process. According to the 2D porosity and 3D density measurements, the collected acoustic and optical signals are grouped into three categories to indicate the quality of the produced parts. In consideration of the laser scanning information, an approach to transform the 1D signal to 2D image is developed. The converted images are then used to train a convolutional neural network so as to extract and fuse the features derived from the two individual sensors. In comparison with several baseline models, the proposed multi-sensor fusion approach achieves the best performance in quality monitoring.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.

In this paper, we develop a generic physics-informed neural network (PINN)-based framework to assess the reliability of multi-state systems (MSSs). The proposed framework follows a two-step procedure. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural network with two individual loss groups is constructed to encode the initial condition and the state transitions governed by ordinary differential equations in MSS, respectively. Next, we tackle the problem of high imbalance in the magnitudes of back-propagated gradients from a multi-task learning perspective and establish a continuous latent function for system reliability assessment. Particularly, we regard each element of the loss function as an individual learning task and project a task’s gradient onto the norm plane of any other task with a conflicting gradient by taking the projecting conflicting gradients (PCGrad) method. We demonstrate the applications of the proposed framework for MSS reliability assessment in a variety of scenarios, including time-independent or dependent state transitions, where system scales increase from small to medium. The computational results indicate that PINN-based framework reveals a promising performance in MSS reliability assessment and incorporation of PCGrad into PINN substantially improves the solution quality and convergence speed of the algorithm.

Teams formulated by aviation professionals are essential in maintaining a safe and efficient aerodrome environment. Nonetheless, the shared situational awareness between the flight crews under adverse weather conditions might be impaired. This research aims to evaluate the impact of a proposed enhancement in communication protocol on cognitive workload and develop a human-centred classification model to identify hazardous meteorological conditions. Thirty groups of subjects completed four post-landing taxiing tasks under two visibility conditions (CAVOK/CAT IIIA) while two different communication protocols (presence/absence of turning direction information) were adopted by the air traffic control officer (ATCOs). Electroencephalography (EEG) and the NASA Task Load Index were respectively used to reflect the pilot’s mental state and to evaluate the pilot’s mental workload subjectively. Results indicated that impaired visibility increases the subjective workload significantly, while the inclusion of turning direction information in the ATCO’s instruction would not significantly intensify their cognitive workload. Mutual information was used to quantitatively assess the shared situational awareness between the pilot flying and the pilot monitoring. Finally, this research proposes a humancentred approach to identify potentially hazardous weather conditions from EEG power spectral densities with Bayesian neural networks (BNN). The classification model has outperformed other baseline algorithms with an accuracy of 66.5%, an F1 score of 61.4%, and an area under the ROC of 0.749. Using the concept of explainable AI with Shapley Additive Explanations (SHAP) values, the exploration of latent mental patterns formulates novel knowledge to gain insights into the vital physiological indicators of the pilots in response to different scenarios from the BNN model. In the long term, the model facilitates the decision regarding the necessity of providing automation and decision-making aids to pilots.

The adoption of artificial intelligence (AI) and machine learning (ML) in risk-sensitive environments is still in its infancy because it lacks a systematic framework for reasoning about risk, uncertainty, and their potentially catastrophic consequences. In high-impact applications, inference on risk and uncertainty will become decisive in the adoption of AI/ML systems. To this end, there is a pressing need for a consolidated understanding on the varied risks arising from AI/ML systems, and how these risks and their side effects emerge and unfold in practice. In this paper, we provide a systematic and comprehensive overview of a broad array of inherent risks that can arise in AI/ML systems. These risks are grouped into two categories: data-level risk (e.g., data bias, dataset shift, out-of-domain data, and adversarial attacks) and model-level risk (e.g., model bias, misspecification, and uncertainty). In addition, we highlight the research needs for developing a holistic framework for risk management dedicated to AI/ML systems to hedge the corresponding risks. Furthermore, we outline several research related challenges and opportunities along with the development of risk-aware AI/ML systems. Our research has the potential to significantly increase the credibility of deploying AI/ML models in high-stakes decision settings for facilitating safety assurance, and preventing systems from unintended consequences.