Jay Lee

University of Maryland, College Park | UMD, UMCP, University of Maryland College Park · Department of Mechanical Engineering

Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked mac...

The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI...

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systemat...

The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such s...

This paper provides a compre- hensive review of machine learning approaches for diag- nostics and prognostics of industrial systems using open- source datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Re- liability Society and summarizes a unified ML framework. This review systematically categorizes an...

This study introduces a novel three-stage diagnostic methodology aimed at enhancing the prediction and classification of gearbox degradation under various operating conditions and multiple degradation levels, addressing the complexities encountered in real-world industrial settings. Leveraging the latest advancements in data-driven approaches, from...

In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial ope...

How to realize industrial ai with speed, scale, and systematically.

The IAI Center's mission is to bring the potential of Artificial Intelligence to Industrial Applications. The Center is able to rapidly research, develop, prototype, and deploy artificial intelligence-enabled solutions to bring operational, technological, and economic impacts to a wide range of industries, including semiconductor, aerospace, energy...

Artificial intelligence is increasingly making its way across industries and could reshape the jobs of the future. Rep. Don Beyer (D-Va.), vice chair of the Congressional Artificial Intelligence Caucus, and top experts including Prof. Jay Lee join Washington Post Live for conversations about the impact of AI on America’s economy and technological c...

AI as a Driving Force for Industry and Economy In a significant gathering of global leaders and experts, Professor Jay Lee was part of the panel at the World Economic Forum's Annual Meeting of the Global Future Councils held in Dubai from October 16-18, 2023. The session, titled "AI as a Driving Force for Industry and Economy," delved deep into th...

Pharmacy has followed a trajectory similar to that of industry. Pharmacy 1.0 greatly reduced workforce requirements for compounding by incorporating machinery. It moved pharmacy from a cottage industry to Pharmacy 2.0 with the mass production of standardized medications. This was the birth of modern pharmacy practice. During this time, new practice...

This paper proposes a novel methodology for enhancing multi-class classification accuracy in fault diagnosis problems among domains with highly-connected fleets of assets using time series data. The approach involves appending specially tailored models to an initial model and incorporating domain adaptation techniques to account for domain variatio...

This paper presents a methodology designed for the Prognostics and Health Management (PHM) Asia-Pacific 2023 Conference Data Challenge. In particular, this study targets the health assessment of spacecraft propulsion systems. The challenge involved analyzing and categorizing a simulation-generated dataset that included four unique spacecraft and mu...

Jay Lee - Industrial AI Augmented Prognostics of Highly Connected and Complex Industrial Systems

Deep learning is one of the emerging techniques that shows good failure modes classification prediction results due to its flexibility in recognizing patterns from raw sensor data. However, it requires complex hyperparameter optimization, high training time, and high computational hardware resources for neural network architecture. On the other han...

This paper presents author’s perspectives on the manufacturing engineering systems in a changing world. A brief historical review of manufacturing evolution is described. Perspectives in making manufacturing engineering a science is presented. At last, issues and challenges of the manufacturing engineering systems in the future are discussed.

Electric Vehicles (EVs) have become a trending topic in recent years due to the industry’s race for competitive pricing as well as environmental awareness. These concerns have led to increased research into the development of both affordable and environmentally friendly EV technology. This paper aims to review EV-related issues beginning with the c...

The degradation of the ball screw drive reduces preload effect and eventually accounts for precision loss and backlash. Traditional methods detect the inception of preload loss considering features from the overall vibration sensor or controller signal. The sensor signal obtained from a ball screw assembly is nonlinear and non-stationary as its dyn...

The process of semiconductor manufacturing is very complex, and the downtime caused by equipment degradation at any process stage reduces yield and production efficiency. Thus, semiconductor machines’ maintenance and calibration are important in maintaining stable production output and yield improvement. Traditionally, semiconductor manufacturers e...

Industrial Cyber-Physical Systems (ICPS) are a key element that acts as the backbone infrastructure for realising innovative systems compliant with the fourth industrial revolution vision and requirements to realize it. Several architectures, such as the Reference Architectural Model Industry 4.0 (RAMI4.0), the Industrial Internet Reference Archite...

Nowadays, profile data mining techniques facilitate effective process monitoring, quality control, fault diagnosis, etc., with considerable benefits to manufacturing industry. However, regarding the complex system in modern manufacturing industry, there are two significant challenges for application development based on profile data mining. Firstly...

Unit-to-unit variation among the production chambers is a long-lasting challenge for Fault Detection and Classification (FDC) development in the semiconductor industry. Currently, various methods are applied for knowledge transfer among chambers and generalized FDC model development. However, the existing methods cannot give a quantitative or quali...

Industrial AI, Big Data Analytics, Machine Learning, and Cyber Physical Systems are changing the way we design product, manufacturing, and service systems. As more sensors and smart analytics software are integrated in the networked industrial products, manufacturing, and maintenance systems, predictive technologies can further learn and autonomous...

Human healthcare data cover different signals related to the functioning of the human body such as blood pressure, blood glucose, heart rate, among others. This kind of data plays an important role on ill patients in the intensive care unit. Unfortunately, the recorded data may include connection & human errors, measurement errors due to the moveme...

Modern production equipment and operations are connected together due to rapid growth in the industrial internet of things. Hence, the relationship between different quality operations can be easily evaluated in a multistage manufacturing system. This work presents the concept of Industrial Artificial Intelligence (AI) augmented data-centric metrol...

"As a leader you don't just make continuous improvement: you disrupt the system"

In semiconductor manufacturing, clustering the fab-wide wafer map images is of critical importance for practitioners to understand the subclusters of wafer defects, recognize novel clusters or anomalies, and develop fast reactions to quality issues. However, due to the high-mix manufacturing of diversified wafer products of different sizes and tech...

This paper aims to help improve the creation of deep learning-based vision systems for consumer goods manufacturing at Procter and Gamble (P&G) using smart, realistic synthetic data creation. This synthetic data creation is based on a novel data resampling technique that utilizes ordinal class information to create hard-to-capture minority class de...

Production scheduling has a long history of research but still presents open challenges, when considering production systems with uncertainty. The digitization, and in particular Digital Twins, may play a role in progressing the research field. The paper proposes a framework to exploit the Digital Twin synchronization with the field to include heal...

Collecting useful and informative data play an essential role in ensuring the performance of data-driven solutions for intelligent maintenance. However, there is still a lack of methodology to systematically assess the data usefulness (or data suitability) for modeling. This lack of data suitability assessment becomes a more pressing issue in the b...

Modern production systems still use traditional quality management methods, even in the Industry 4.0 transformation. With the advent of industrial internet technologies, more advancements are needed to improve the quality management systems. This paper introduces the Stream-of-QualityTM (SoQ) methodology, which defines the 3 T's Transparency, Trace...

This study presents the concept of the Industrial Metaverse for smart manufacturing systems. In the manufacturing domain, the Industrial Metaverse's purpose would be to speed up processes like repairs/maintenance, starting new manufacturing lines, remote monitoring/troubleshooting, remote control, and new user/manager training through simulation. M...

The demand for Printed circuit boards (PCBs) has increased due to the rapid change in technology in recent years. Consequently, PCBs health assessment and fault detection play an important role in improving productivity. This study proposed a novel method which focused on feature engineering for health assessment in PCBs. The performance of the pro...

Surge of compressor will cause severe damage to the machine. Used PCA to select the most critical parameters then used Support Vector machine (SVM) to perform classification to deter the surge.

Injection molding (IM) is a versatile manufacturing process capable of rapid prototyping and mass-producing high-quality polymer parts. The present study mainly investigates the challenge of designing multiple molding gates on the complex arbitrary part surface in 3D. Currently, this problem is a challenge in mold design and engineering experience...

In this study, we investigate the transferability of the process-property relationship between two Inconel alloys for laser powder bed fusion (LPBF). By developing a Bayesian learning approach, the process-property model of Inconel 625 learned from Inconel 718 demonstrates high accuracy with R of 0.95, which verifies the feasibility of this innovat...

Recent COVID pandemic has revealed the vulnerability of modern manufacturing systems to endure disruptive changes. In response to the distress caused by disruptions, there is a pressing need to improve manufacturing resilience by embracing automation, digitization, and Artificial Intelligence (AI). This paper re-conceptualizes manufacturing resilie...

Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasi...

Due to the successful implementation of intelligent data-driven approaches, these methods are gaining remarkable attention in predicting the remaining useful life (RUL) problems. Within this scope, transfer learning approaches are exploited to transfer the obtained knowledge from the source domain data to the target domain data. Due to the differen...

Lens matching is a trial assembly step in compact lens module manufacturing that can effectively improve the production yield and compensate for the manufacturing error to some degree. However, conventional lens matching (i.e., through trial and error (T&E)) is extremely time-consuming and has a low success rate. To address this issue, this paper p...

In industrial applications, the mechanical wear on ball screw components can lead to a loss of positioning accuracy that reduces the operational reliability and reproducibility of production systems. Existing monitoring solutions are impractical for real industrial settings or are unable to provide quantifiable estimates of the magnitude of degrada...

This research presents optimized maintenance design using simulation to analyze the capability of auto part manufacturing production system. The integration of simulation and optimization is used to identify critical stations, an optimal system design and maintenance scheduling schemes and evaluates their effects on the overall system performance....

Difficulty in obtaining enough run-to-fail datasets is a major barrier that impedes the widespread acceptance of Prognostic and Health Management (PHM) technology in many applications. Recent progress in federated learning demonstrates great potential to overcome such difficulty because it allows one to train PHM models based on distributed databas...

Prognostics and Health Management (PHM) methodologies and techniques have been much widely studied in the academia and practiced by the industry in recent years. Prognostic approaches commonly try to establish the relationship between Remaining Useful Life (RUL) and a single variable or health indicator (HI) which can be obtained from multi-sensor...

The prediction of average Material Removal Rate (MRR) in Chemical Mechanical Planarization (CMP) process is regarded as a crucial research objective of Virtual Metrology (VM) for semiconductor manufacturing. In this paper, a novel VM model is proposed to predict MRR in CMP process based on the integration of Gaussian Process Regression (GPR) with a...

A Cyber-Physical System (CPS)-enabled rehabilitation system framework for enhanced recovery rate in gait training systems is presented in this paper. Recent advancements in sensing and data analytics have paved the way for the transformation of healthcare systems from experience-based to evidence-based. To this end, this paper introduces a CPS-enab...

In the wake of COVID-19, significant influence on the manufacturing industries has been observed in the past year due to the restrictions of in-person communications and interactions. As a consequence, manufacturing efficiency has reduced remarkably all over the world. Despite the great harm to the industrial operations under the pandemic, the oppo...

This paper proposes a unified filtering framework for multi-horizon wind speed prediction. The novelty of this paper focuses on the integration of the short-term prediction model, the Numerical Weather Prediction (NWP) and a smoothing term into a unified framework based on Bayesian filters. In the proposed framework, the system state function of th...

Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze a great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possib...

Closed-Loop Lifecycle Management (CL2M) is an integral part of the circular economy. Managing the CL2M enables manufacturers and associated digital factories to connect in-service issues back to process conditions and product information at manufacturing and other stages of the life cycle with the aim of having Zero Defect Manufacturing (ZDM). ZDM...

Wind speed prediction is an important research topic in the wind industry and many algorithms have been proposed to fulfill the prediction tasks. By reviewing the existing methods, one can find that the supplemental information, such as acceleration and turbulence intensity, that can be indirectly derived from wind speed is still less considered in...

Digital Twin and Cyber Physical Systems (CPS) are gaining popularity due to their significant impacts on the realization of smart manufacturing within which data and information from different systems is used to increase self-awareness, self-predict, and self-configure functionalities. Throughout the integration of digital twins with CPS, intellige...

Industry 4.0 is an advanced architecture which aims to improve the manufacturing process and product quality by using large-scale machine to machine communication and Internet of things deployments to offer increased automation, enhanced communication and self-monitoring, without the need for human intervention. The artificial intelligence (AI) tec...

Maintenance scheduling and vessel routing are critical for the off-shore wind farm to reduce maintenance costs. In this research, a systematic framework that takes the advantage of predictive analysis for off-shore wind farm maintenance optimization is sketched and the optimization results are presented. The proposed framework consists of three dif...

Stanford Univ announced top 2% world scientists. Prof Lee’s rank is 18435 out of 159,684 or 18771 by C Score) . In the field of Industrial Intelligence and Automation, Prof. Lee is rank 125 among 87535 scientists in this field.

The 1st Industrial AI Data Challenge is a competition open to all potential participants. The challenge this year focuses on estimating the remaining useful life (RUL) of the cutting tools.

Intelligent data-driven fault diagnostics for rotating machinery is well established. However, ball screws pose a unique challenge of impractical sensor locations for long-term deployment due to their complex motion trajectory and sophisticated mechanical structure. To overcome this challenge, an indirect sensing method is proposed. While technique...

The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of th...

As a promising modern technology, additive manufacturing (AM) has been receiving increasing research and industrial attention in the recent years. With its rapid development, the importance of quality monitoring in AM process has been recognized, which significantly affects the property of the manufactured parts. Since the conventional hand-crafted...

Recently, the development of intelligent data-driven machinery fault diagnosis methods have received significant attention. In most studies, the training and testing data are assumed to be collected from the same sensor. However, in real practice, due to the mounting limitation and sensor malfunctioning, it cannot be generally guaranteed to obtain...

The high-speed railway (HSR) transportation system in China has been growing rapidly during the past decade. In 2016, the total length of HSR in China has reached to 22,000 kilometers, and there are over 2,000 pairs of high speed trains operating daily. With the advancement of design and manufacturing technologies, the reliability and construction...

The age of Smart Manufacturing has arrived where more and more organizations are embracing it to innovate and maintain their competitiveness. Smart Manufacturing blends information technology (IT) with operations technology (OT) to enable greater productivity, efficiency, quality, and customization within factory operations. More specifically, emer...

Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful se...

Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful se...

Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are...

This paper presents an effective health assessment and predictive maintenance technique for industrial assets. The technique and algorithms applied to data sets provided by the Prognostics and Health Management Society 2014 Data Challenge. The data contains usage and part consumption for three years. In short, the usage data contains a parameter th...

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is f...

This article aims to present a comprehensive review of the recent efforts and advances in applying machine learning (ML) techniques in the area of diagnostics and prognostics of rolling element bearings. The significant goal of this study is to review, recognize and evaluate the performance of various ML techniques and compare them on criteria's su...

Gearboxes are widely used in rotating machinery and various industrial applications for transmission of power and torque. They operate for prolong hours and under different working conditions which may increase their probability of failure. Sudden failure of a gearbox may lead to significant downtime and increase maintenance costs. In industrial ap...

Industrial robots are widely used in modern factories. Robot faults lead to the inevitable suspension of production lines. The prediction of robot failure can improve production capacity. However, it is challenging due to the variations of robots in dynamic working regimes. This paper presents a methodology of fault prognosis of industrial robots,...

Please see the published version @Manufacturing Leadership Council: https://www.manufacturingleadershipcouncil.com/2020/09/30/5g-and-smart-manufacturing/