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1. WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System

   https://doi.org/10.1109/UEMCON59035.2023.10315983  

   Tian, Beitong; Eslaminia, Ahmadreza; Lu, Kuan-Chieh; Wang, Yaohui; Shao, Chenhui; Nahrstedt, Klara (October 2023, IEEE)

   Chakrabarti, Satyajit; Paul, Rajashree (Ed.)

   Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors. Ensuring high-quality welding is vital, making tool condition monitoring systems essential for early-stage quality control. However, existing monitoring methods face challenges in cost, downtime, and adaptability. In this paper, we present WeldMon, an affordable ultrasonic welding machine condition monitoring system that utilizes a custom data acquisition system and a data analysis pipeline designed for real-time analysis. Our classification algorithm combines auto-generated features and hand-crafted features, achieving superior cross-validation accuracy (95.8% on average over all testing tasks) compared to the state-of-the-art method (92.5%) in condition classification tasks. Our data augmentation approach alleviates the dataset shift problem, enhancing tool condition classification accuracy by 8.3%. All algorithms run locally, requiring only 385 milliseconds to process data for each welding cycle. We deploy WeldMon and a commercial system on an actual ultrasonic welding machine, performing a comprehensive comparison. Our findings highlight the potential for developing cost-effective, high-performance, and reliable tool condition monitoring systems. 

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2. Fabrication and Processing of Magnesium-Based Metal Matrix Nanocomposites for Bioabsorbable Implants

   https://doi.org/10.3390/met14121318  

   Larraza, Andres; Burke, Shane; Sotoudehbagha, Pedram; Razavi, Mehdi (November 2024, Metals)

   A novel magnesium (Mg)-based metal matrix nanocomposite (MMNC) was fabricated using ultrasonic melt treatment to promote the de-agglomeration of the bioactive glass–ceramic nanoparticles and the homogenization of the melt. The cast samples were then heat treated, machined, and hot rolled to reduce grain size and remove structural defects. Standard mechanical and electrochemical tests were conducted to determine the effect of fabrication and processing on the mechanical and corrosion properties of MMNCs. Compression tests, potentiodynamic polarization tests, electrochemical impedance spectroscopy, and static immersion testing were conducted to determine the characteristics of the MMNCs. The results showed that the combination of ultrasonic melt processing and thermomechanical processing caused the corrosion rate to increase from 8.7 mmpy after 10 days of immersion to 22.25 mmpy when compared with the ultrasonicated MMNCs but remained stable throughout the immersion time, showing no statistically significant change during the incubation periods. These samples also experienced increased yield stress (135.5 MPa) and decreased elongation at break (21.92%) due to the significant amount of grain refinement compared to the ultrasonicated MMNC (σY = 59.6 MPa, elongation = 40.44%). The MMNCs that underwent ultrasonic melt treatment also exhibited significant differences in the corrosion rate calculated from immersion tests. 

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3. SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense

   https://doi.org/10.1109/SECON52354.2021.9491590  

   Lou, Jianzhi; Yan, Qiben; Hui, Qing; Zeng, Huacheng (July 2021, 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON))

   null (Ed.)

   Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors’ signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks. 

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4. Machine learning for microfluidic design and control

   https://doi.org/10.1039/D2LC00254J  

   McIntyre, David; Lashkaripour, Ali; Fordyce, Polly; Densmore, Douglas (August 2022, Lab on a Chip)

   Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations. 

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5. Machine learning enhanced control co-design optimization of an immersion cooled battery thermal management system

   https://doi.org/10.1063/5.0201438  

   Liu, Zheng; Kabirzadeh, Pouya; Wu, Hao; Fu, Wuchen; Qiu, Haoyun; Miljkovic, Nenad; Li, Yumeng; Wang, Pingfeng (July 2024, Journal of Applied Physics)

   The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption. 

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