Yuchen Liang

Coventry University | CU · Faculty of Engineering Environment and Computing

Effective fault diagnostics on rolling bearings is vital to ensuring safe and reliable operations of industrial equipment. In recent years, enabled by Machine Learning (ML) algorithms, data-based fault diagnostics approaches have been steadily developed as promising solutions to support industries. However, each ML algorithm exhibits some shortcomi...

Heavy-duty CNC machines are important equipment in manufacturing large-scale and high-end products. During the machining processes, a significant amount of heat is generated to bring working temperatures rising, which leads to deformation of machine elements and further machining inaccuracy. In recent years, data-driven approaches for predicting th...

Faults on machines or cutting tooling during machining processes generate negative impacts on productivity, production quality and scrap rate. Effective diagnostics to identify faults throughout the lifecycle of a machining process adaptively is foremost for achieving overall manufacturing sustainability. In recent years, the research of leveraging...

Adhesive bonded joints are one of important joining technologies in supporting various manufacturing applications. It is important to predict the optimal strength of adhesive bonded joints in order to fit design requirements. Prediction on joint strengths is usually based on experimental tests and Finite Element Analysis (FEA). However, it is a tim...

Cloud enabled prognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud. To overcome the limitation, this chapter presents an innovative fog enabled prognosis system for machining process optimizati...

The Big Data driven approach has become a new trend for manufacturing optimization. In this chapter, an innovative Big Data enabled Intelligent Immune System (I²S) has been developed to monitor, analyze and optimize machining processes over lifecycles in order to achieve energy efficient manufacturing. There are two major functions in I²S: (1) an A...

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some importa...

Heavy-duty CNC machines are important equipment in manufacturing large-scale and high-end products. During the machining processes, a significant amount of heat is generated to bring working temperatures rising, which leads to deformation of machine elements and further machining inaccuracy. In recent years, data-driven approaches for predicting th...

Faults during machining processes generate negative impacts on productivity, product quality and scrap rate. In recent years, the research of leveraging deep learning algorithms for developing fault diagnostics approaches has been actively conducted. However, the approaches have not been widely adopted by industries yet due to their inadaptability...

To achieve zero-defect production during CNC machining processes, it is imperative to develop effective diagnosis systems to detect anomalies efficiently. However, due to the dynamic conditions of machine and tooling during machining processes, relevant diagnosis systems adopted in industries are incompetent. To address the issue, in this paper, a...

Cloud enabled prognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud. To overcome the limitation, this paper presents an innovative fog enabled prognosis system for machining process optimization...