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Abstract Cyber-enabled manufacturing systems are becoming increasingly data-rich, generating vast amounts of real-time sensor data for quality control and process optimization. However, this proliferation of data also exposes these systems to significant cyber-physical security threats. For instance, malicious attackers may delete, change, or replace original data, leading to defective products, damaged equipment, or operational safety hazards. False data injection attacks can compromise machine learning models, resulting in erroneous predictions and decisions. To mitigate these risks, it is crucial to employ robust data processing techniques that can adapt to varying process conditions and detect anomalies in real-time. In this context, the incremental machine learning (IML) approaches can be valuable, allowing models to be updated incrementally with newly collected data without retraining from scratch. Moreover, although recent studies have demonstrated the potential of blockchain in enhancing data security within manufacturing systems, most existing security frameworks are primarily based on cryptography, which does not sufficiently address data quality issues. Thus, this study proposes a gatekeeper mechanism to integrate IML with blockchain and discusses how this integration could potentially increase the data integrity of cyber-enabled manufacturing systems. The proposed IML-integrated blockchain can address the data security concerns from both intentional alterations (e.g., malicious tampering) and unintentional alterations (e.g., process anomalies and outliers). The real-world case study results show that the proposed gatekeeper integration algorithm can successfully filter out over 80% of malicious data entries while maintaining comparable classification performance to standard IML models. Furthermore, the integration of blockchain enables effective detection of tampering attempts, ensuring the trustworthiness of the stored information. 

Recurrent respiratory papillomatosis (RRP) is a chronic condition primarily affecting children, known as juvenile onset RRP (JORRP), caused by a viral infection. Antiviral medications have been used to reduce the need for frequent surgeries, slow the growth of papillomata, and prevent disease spread. Effective treatment of JORRP necessitates targeted drug delivery (TDD) to ensure that inhaled aerosolized drugs reach specific sites, such as the larynx and glottis, without harming healthy tissues. Using computational fluid particle dynamics (CFPD) and machine learning (ML), this study (1) investigated how drug properties and individual factors influence TDD efficiency for JORRP treatment and (2) developed personalized inhalation therapy using an ML-empowered smart inhaler control algorithm for precise medication release. This algorithm optimizes the inhaler nozzle position and diameter based on drug and patient-specific data, enhancing drug delivery to the larynx and glottis. CFPD simulations show that particle size significantly affects deposition fractions in the upper airway, emphasizing the importance of particle size selection. Additionally, optimal nozzle diameter and delivery efficiency depend on particle size, inhalation flow rate, and release time. The ML-based TDD strategy, employing a classification and regression tree model, outperforms conventional inhalation therapy by achieving a higher delivery efficiency to the larynx and glottis. This innovative concept of an ML-empowered smart inhaler represents a promising step toward personalized and precise pulmonary healthcare through inhalation therapy. It demonstrates the potential of AI-driven smart inhalers for improving the treatment outcomes of lung diseases that require TDD at designated lung sites. 

Abstract The advancement of sensing technology enables efficient data collection from manufacturing systems for monitoring and control. Furthermore, with the rapid development of the Internet of Things (IoT) and information technologies, more and more manufacturing systems become cyber-enabled, facilitating real-time data sharing and information exchange, which significantly improves the flexibility and efficiency of manufacturing systems. However, the cyber-enabled environment may pose the collected sensor data under high risks of cyber-physical attacks during the data and information sharing. Specifically, cyber-physical attacks could target the manufacturing process and/or the data transmission process to maliciously tamper the sensor data, resulting in false alarms or failures in anomaly detection in monitoring. In addition, the cyber-physical attacks may also enable illegal data access without authorization and cause the leakage of key product/process information. Therefore, it becomes critical to develop an effective approach to protect data from these attacks so that the cyber-physical security of the manufacturing systems could be assured in the cyber-enabled environment. To achieve this goal, this paper proposes an integrative blockchain-enabled data protection method by leveraging camouflaged asymmetry encryption. A real-world case study that protects cyber-physical security of collected sensor data in additive manufacturing is presented to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time (less than 0.05ms) and the risk of unauthorized data access is significantly reduced as well. 

Abstract The process uncertainty induced quality issue remains the major challenge that hinders the wider adoption of additive manufacturing (AM) technology. The defects occurred significantly compromise structural integrity and mechanical properties of fabricated parts. Therefore, there is an urgent need in fast, yet reliable AM component certification. Most finite element analysis related methods characterize defects based on the thermomechanical relationships, which are computationally inefficient and cannot capture process uncertainty. In addition, there is a growing trend in data-driven approaches on characterizing the empirical relationships between thermal history and anomaly occurrences, which focus on modeling an individual image basis to identify local defects. Despite their effectiveness in local anomaly detection, these methods are quite cumbersome when applied to layer-wise anomaly detection. This paper proposes a novel in situ layer-wise anomaly detection method by analyzing the layer-by-layer morphological dynamics of melt pools and heat affected zones (HAZs). Specifically, the thermal images are first preprocessed based on the g-code to assure unified orientation. Subsequently, the melt pool and HAZ are segmented, and the global and morphological transition metrics are developed to characterize the morphological dynamics. New layer-wise features are extracted, and supervised machine learning methods are applied for layer-wise anomaly detection. The proposed method is validated using the directed energy deposition (DED) process, which demonstrates superior performance comparing with the benchmark methods. The average computational time is significantly shorter than the average build time, enabling in situ layer-wise certification and real-time process control. 

As a pervasive issue, missing data may influence the data modeling performance and lead to more difficulties of completing the desired tasks. Many approaches have been developed for missing data imputation. Recently, by taking advantage of the emerging generative adversarial network (GAN), an effective missing data imputation approach termed generative adversarial imputation nets (GAIN) was developed. However, its modeling architecture may still lead to significant imputation bias. In addition, with the GAN structure, the training process of GAIN may be unstable and the imputation variation may be high. Hence, to address these two limitations, the ensemble GAIN with selective multi-generator (ESM-GAIN) is proposed to improve the imputation accuracy and robustness. The contributions of the proposed ESM-GAIN consist of two aspects: (1) a selective multi-generation framework is proposed to identify high-quality imputations; (2) an ensemble learning framework is incorporated for GAIN imputation to improve the imputation robustness. The effectiveness of the proposed ESM-GAIN is validated by both numerical simulation and two real-world breast cancer datasets.