Zhangyue Shi

• Doctor of Philosophy
• Data Scientist at Bayer

In advanced manufacturing, the incorporation of sensing technology provides an opportunity to achieve efficient in situ process monitoring using machine learning methods. Meanwhile, the advances of information technologies also enable a connected and decentralized environment for manufacturing systems, making different manufacturing units in the sy...

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, w...

Although data have been extensively leveraged for process monitoring and control in advanced manufacturing, it still suffers from the connection issues among sensors, machines, and computers, which may lead to significant data loss, i.e., missing region in the collected data, in the application of data-driven monitoring. To address the missing regi...

Objective: Aging is a complicated process that triggers age-related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence-associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue h...

In advanced manufacturing, the incorporation of sensing technology provides an opportunity to achieve efficient in-situ process monitoring using machine learning methods. Meanwhile, the advances of information technologies also enable a connected and decentralized environment for manufacturing systems, making different manufacturing units in the sy...

Metal additive manufacturing (AM) has become popular in a large variety of applications due to its excellent capabilities of handling complex geometries and novel materials. However, due to its process complexity, layer-wise surface quality issue is still one of the critical concerns to further broaden adoption of metal AM, because of the impact on...

Additive manufacturing (AM) has gained increasing popularity in a large variety of mission- critical fields, such as aerospace, medical, and transportation. The layer-by-layer fabrication scheme of the AM significantly enhances fabrication flexibility, resulting in the expanded vulnerability space of cyber- physical AM systems. This potentially lea...

Supervised machine learning techniques such as classification models have been widely applied to online process anomaly detection in advanced manufacturing. However, since abnormal process states rarely occur in regular manufacturing settings, the data collected for model training may be highly imbalanced, which may result in significant training b...

With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality mon...

Additive manufacturing (AM) has gained increasing popularity in various quality critical applications such as aerospace and healthcare due to its high flexibility in fabricating complex geometries with novel materials. However, the relatively poor layer-wise surface quality may lead to compromised property and functionality of AM products. The expe...

As an emerging technology, additive manufacturing (AM) is able to fabricate products with complex geometries using various materials. In particular, cyber-enabled AM systems have recently become widely applied in many real-world applications. It significantly improves the flexibility and productivity of AM but poses the system under high risks of c...