Robert X. Gao

Robert X. Gao
Case Western Reserve University | CWRU · Department of Mechanical and Aerospace Engineering

Professor and Department Chair

About

446
Publications
258,138
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
15,496
Citations
Citations since 2017
111 Research Items
11991 Citations
201720182019202020212022202305001,0001,5002,0002,5003,000
201720182019202020212022202305001,0001,5002,0002,5003,000
201720182019202020212022202305001,0001,5002,0002,5003,000
201720182019202020212022202305001,0001,5002,0002,5003,000
Introduction
Robert Gao is the Cady Staley Professor and Department Chair of Mechanical and Aerospace Engineering at Case Western Reserve University, USA. His research focuses on physics-based sensing, mechatronic design, stochastic modeling, and machine learning, with applications in manufacturing, building HVAC systems, aircraft engines, and health care. He has published three books, one of which (on wavelet transform for manufacturing) was translated into Chinese, over 380 scientific publications, and received multiple awards, including ASME Blackall Machine Tool and Gage Award, SME Eli Whitney Productivity Award, IEEE Instrumentation and Measurement Society Technical Award and Best Application in Instrumentation and Measurement Award, etc. He is a Fellow of CIRP, SME, IEEE, and ASME.
Additional affiliations
February 2015 - present
Case Western Reserve University
Position
  • Chair
Description
  • Research Interest: physics-based sensing methodology, stochastic modeling, prognosis, smart structures and materials, multi-resolution analysis for time series and image processing, energy-efficient sensor networks.

Publications

Publications (446)
Article
The commissioning of Computerized Numerical Control Machine Tools (CNCMTs) is particularly important and the commissioning quality directly affects its product processing. However, traditional commissioning methods are not suitable for complex and changeable machining conditions during operation, and the derived commissioning results have limited e...
Article
Full-text available
Fault diagnosis keeps an essential tool to en-sure the safety and reliability of a motor system. Based on deep learning, fault diagnosis models constructed by mining historical fault data of equipment have received extensive attention. However, the high computational cost constrains the application of deep learning models for fault diagnosis, espec...
Article
Prognosis is crucial for tracking and predicting a system’s performance and provides the basis for predictive maintenance. Prognosis techniques are broadly classified into model-based and data-driven methods, which rely on physical knowledge and data learning, respectively. While data-driven methods alleviate limitations associated with model-based...
Article
Full-text available
Rapid advancement over the past decades in nanomanufacturing has led to the realization of a broad range of nanostructures such as nanoparticles, nanotubes, and nanowires. The unique mechanical, chemical, and electrical properties of these nanostructures have made them increasingly desired as key components in industrial and commercial applications...
Article
Full-text available
Predictive Maintenance (PdM) has continually attracted interest from the manufacturing community due to its significant potential in reducing unexpected machine downtime and related cost. Much attention to existing PdM research has been paid to perceiving the fault, while the identification and estimation processes are affected by many factors. Man...
Article
Robotic arc welding (RAW) has been an essential process in various assembly systems, such as automotive manufacturing. However, its implementations lack adaptivity to compensate for process variations. This paper presents a data-driven process characterization and online adaptive control framework for RAW to automatically and efficiently achieve de...
Article
Full-text available
To overcome the limitations associated with purely physics-based and data-driven modeling methods, hybrid, physics-based data-driven models have been developed, with improved model transparency, interpretability, and analytic capabilities at reduced computational cost. This paper reviews the state-of-the-art of hybrid physics-based data-driven mode...
Article
Full-text available
In recent years, deep learning (DL)-based fault diagnosis methods have demonstrated significant success in various industrial domains due to their high accuracy. Similar to other data-driven techniques, DL models are application-specific and strongly depend on the data which they are developed upon. Different DL models need to be designed for diffe...
Article
Tool wear prediction plays an important role in ensuring the reliability of machining operation due to their wide-ranging application in smart manufacturing. Massive effort has been devoted into exploring the methods of tool wear prediction. However, it remains a challenge to improve the accuracy of tool wear prediction under varying tool wear rate...
Article
Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent “black box” nature of ML techniques such as those represented by artificial neural networks has often presented a challenge to interpret ML outcomes in the fram...
Chapter
Smart manufacturing refers to an advanced mode of manufacturing, which incorporates computer-integrated manufacturing (CIM) and artificial intelligence (AI) for data-enabled adaptability throughout the production cycle, from product design over process scheduling, control, and optimization to product quality assurance. Enabling this mode of manufac...
Article
Full-text available
Heat exchangers play essential roles in power generation and the petrochemical industry. Although physical sensors are widely deployed in the process industry, timely fault detection and diagnosis method remain a significant challenge to its safe and reliable operation for accurate and reliable equipment condition monitoring. To address this challe...
Article
Quantification of machined surface roughness is critical to enabling estimation of part performance such as tribology and fatigue. As a contactless alternative to the traditional contact profilometry, photographic methods have been widely applied due to the advancement of image processing and machine learning techniques that allow the analysis of s...
Article
As one of the critical elements for smart manufacturing, human-robot collaboration (HRC), which refers to goal-oriented joint activities of humans and collaborative robots in a shared workspace, has gained increasing attention in recent years. HRC is envisioned to break the traditional barrier that separates human workers from robots and greatly im...
Article
Full-text available
Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to a...
Book
The paradigm shift from mass production to on-demand, personalized, customer-driven, and knowledge-based production reshapes manufacturing. Smart manufacturing leads to an automated world that relies more on information and communication technologies (ICTs) and sophisticated information-technology-intensive processes, enhancing flexibility. Further...
Preprint
Full-text available
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in ar...
Conference Paper
Full-text available
In human-robot collaborative assembly, robots help human operators complete complex assembly tasks. However, controlling these robots is not intuitive given the constraints posed by the close proximity of the operator. In response to this need, a novel approach using multimodal data is developed for human-centred robot control in human-robot collab...
Poster
Full-text available
Details can be found on RCIM journal website: https://www.sciencedirect.com/journal/robotics-and-computer-integrated-manufacturing/about/call-for-papers#special-issue-on-digitalization-and-servitization-of-machine-tools-in-the-era-of-industry-4-0
Article
As an emerging communication modality, brainwaves can be used to control robots for seamless assembly, especially in noisy environments where voice recognition is not reliable or when an operator is occupied with other tasks and unable to make gestures. This paper investigates human-robot collaborative assembly based on function blocks and driven b...
Article
Proper functioning of rolling element bearings is critical to ensuring reliable and safe power transmission. The ability to automatically recognize fault-related characteristics is key to enabling intelligent bearing fault recognition. While many techniques have been developed, effective bearing fault recognition under non-stationary conditions rem...
Article
Full-text available
Remaining useful life (RUL) prediction is a challenging task for prognostics and health management (PHM). Due to the complexity physics involved for precisely modeling the machine degradation process, learning-based data-driven methods, which learn the degradation pattern solely from the historical data without referring to physical models, have be...
Chapter
The ever-increasing demand for higher productivity, lower cost and improved safety continues to drive the advancement of manufacturing technologies. As one of the key elements, human–robot collaboration (HRC) envisions a workspace where humans and robots can dynamically collaborate for improved operational efficiency while maintaining safety. As th...
Article
Modern manufacturing systems are becoming increasingly complex, dynamic, and connected, and their performance is being affected by not only their constituent processes but also their system-level interactions. This paper presents an integrated modelling method based on a graph neural network (GNN) and multi-agent reinforcement learning (MARL) colla...
Article
Accurate and reliable machine performance degradation tracking and remaining useful life (RUL) prognosis establish the foundation for predictive maintenance scheduling towards improved safety and productivity of machine operations. In general, machine performance degradation exhibits a non-linear and non-homogeneous pattern that arises from time-va...
Article
Full-text available
Tool wear prediction is of significance to improve the safety and reliability of machining tools, given their widespread applications in nearly every branch of manufacturing. Mathematical modelling, including data driven modelling and physics-based modelling, is an important tool to predict the degree of tool wear. Howerver, the performance of conv...
Article
Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligen...
Article
Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these “big data”, how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has b...
Article
Full-text available
Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potenti...
Article
Effective and safe human-robot collaboration in assembly requires accurate prediction of human motion trajectory, given a sequence of past observations such that a robot can proactively provide assistance to improve operation efficiency while avoiding collision. This paper presents a deep learning-based method to parse visual observations of human...
Conference Paper
Machine learning has demonstrated its effectiveness in fault recognition for mechanical systems. However, sufficient data for establishing accurate and reliable fault detection methods is not always available in real-world applications. Transfer learning leverages the knowledge learned from a source domain in order to bypass limitations in data ava...
Article
Given its demonstrated capability in pattern recognition, Deep Learning (DL) has been increasingly investigated for advanced manufacturing. One limiting factor for successful DL applications is the availability of sufficient amount of data of relevance to the specific application. A solution is presented in this paper for cross-domain data learning...
Article
Human-Robot Collaboration (HRC), which enables a workspace where human and robot can dynamically and safely collaborate for improved operational efficiency, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization of HRC, as it helps identify current human action and provides the basi...
Article
Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial int...
Article
The variability of machinery fault signatures causes the data samples to follow different distributions under various operating conditions, which poses significant challenges on autonomous diagnosis based on machine learning techniques. This paper presents a new transfer learning method for cross-domain feature learning by mitigating the domain dif...
Article
Full-text available
Fault detection and diagnosis of induction motors in variable frequency drive (VFD) applications is essential for minimizing unexpected downtime, material waste and equipment damage, ultimately contributing to sustainable manufacturing. This paper presents a multi-stream convolutional neural network (MS-CNN) for automatic feature extraction from an...
Article
Full-text available
Acoustic monitoring presents itself as a flexible but under-reported method of tool condition monitoring in milling operations. This paper demonstrates the power of the monitoring paradigm by presenting a method of characterizing milling tool conditions by detecting anomalies in the time-frequency domain of the tools’ acoustic spectrum during cutti...
Article
Full-text available
Sonic monitoring presents itself as one of the least invasive but easiest to implement methods of machine condition characterization. This work investigates the viability of categorically classifying cutting tool wear using only sonic output from a vertical milling center and proposes a statistical model of milling acoustic signals as well as a nov...
Article
Full-text available
To improve the consistency of part quality in Additive Manufacturing, it is critical to understand the relationship between the mechanisms underlying the layer-by-layer printing process and the resulting product quality. This paper investigates this relationship by incorporating attention mechanism into a Long Short-term Memory network, using Fused...
Article
As a state-of-the-art pattern recognition technique, convolutional neural networks (CNNs) have been increasingly investigated for machine fault diagnosis, due to their ability in analyzing nonlinear and nonstationary high-dimensional data that are typically associated with the performance degradation process of machines. A key issue of interest is...
Article
In this paper, a novel signal optimization based generalized demodulation transform (SOGDT) is proposed for rolling bearing nonstationary fault characteristic extraction. This method mainly involves five steps: (a) the resonance frequency band excited by bearing fault is obtained using the spectral kurtosis (SK) based band-pass filtering algorithm;...
Article
Aiming at fault visualization and automatic feature extraction, this paper presents a new and intelligent bearing fault diagnostic method by combining Symmetrized Dot Pattern (SDP) representation with Squeeze-and-Excitation enabled Convolutional Neural Network (SE-CNN) model. Graphical representations of bearing states are shown intuitively by usin...
Preprint
Full-text available
Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a CNN architecture has rarely been studied. In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (W...
Article
Full-text available
In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbio...
Article
Condition monitoring and fault diagnosis are of significance to improve the safety and reliability of motors, given their widespread applications in virtually every branch of the industry. Sequential data modeling based on recurrent neural network (RNN) and its variants have drawn increasing attention because the temporal nature of motor signals ca...
Article
Full-text available
With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remai...
Article
Full-text available
Cybernetic manufacturing aims for flexible and adaptive manufacturing operations locally or globally by using integrated technologies that can combine the advanced computing power with manufacturing equipment. In recent years, research on Cyber-Physical Systems (CPS), Internet of Things (IoT), and Big Data has been active in such areas like transpo...
Article
Deep learning architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a deep learning-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of convolutio...
Article
The rapid advancement of additive manufacturing technology has led to new opportunities and challenges. One potential advantage of additive manufacturing is the possibility of producing systems with reduced volumes/weights. This research concerns a type of configuration optimization problems, where the envelope volume in space occupied by a number...
Article
Sparse subspace clustering (SSC) is an effective method to cluster sensing signal for fault diagnosis in mechanical systems. SSC is based on a global expression strategy describing each data point by other data points from all the potential clusters. A drawback of this strategy is that it generates non-zero elements in the non-diagonal blocks of th...
Article
Full-text available
This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement s...
Article
Forming is widely used due to its high efficiency in material utilization and its high production rate in general. Most forming processes control the geometry of final products through a set of tooling. The increasing demands on lightweight products have challenged the performances and functionalities of tooling. This paper provides a systematic re...
Article
As one of the transformative technologies, Additive Manufacturing (AM) has been facing the challenge of product property inconsistency, which prevents its adoption in various critical applications. Various studies have been dedicated to building the predictive models for improved AM quality control. However, the distinct layer-by-layer printing pro...
Article
Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold-Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time-frequency ridge is separated from the time-frequency represent...
Article
Full-text available
Cutting tool condition prognosis is critical to process stability and quality assurance, but affected by complex material-process interactions. This paper presents a hybrid machine learning method that integrates heterogeneous data (structured process parameters and unstructured power profiles and tool wear images) for tool condition prognosis. Sur...
Article
Machine vision based product inspection methods have been widely investigated to improve product quality and reduce labour costs. Recent advancement in deep learning provides advanced analytics tools with high inspection accuracy and robustness. However, the construction of deep learning model is typically computationally expensive, which may not m...
Article
A machine learning approach for on-line fault recognition via automatic image processing is developed to timely identify material defects due to process non-conformities in Selective Laser Melting (SLM) of metal powders. In-process images acquired during the layer-by-layer SLM processing are analyzed via a bi-stream Deep Convolutional Neural Networ...
Article
Chatter is a self-excited and unstable vibration phenomenon during machining operations, which affects the workpiece surface quality and the production efficiency. Active chatter control has been intensively studied to mitigate chatter and expand the boundary of machining stability. This paper presents a discrete time-delay optimal control method f...
Article
Full-text available
This paper presents a probabilistic model based approach for machinery condition prognosis based on particle filter by integrating physical knowledge with in-process measurements into a state space framework to account for uncertainty and nonlinearity in machinery degradation process. One limitation of conventional particle filter is that condition...
Article
Manifold is considered to be a low dimensional surface embedded in a high dimensional vector space, and manifold learning is to find this surface based on data points sampled from this vector space. Neighborhood construction is a critical step in manifold learning to retain local relationship of data, i.e., neighbors and the connection weights. Cur...
Article
Full-text available
The rapid advancement of data analytics has opened up new opportunities for improving the life cycle of engineered products and enhancing sustainability by intelligent monitoring and fault diagnosis of the related manufacturing processes and systems. Recently, Deep Neural Networks (DNNs) have demonstrated improved accuracy and robustness in classif...
Article
Full-text available
As the power source for virtually all manufacturing systems, induction motor represents an integral part in modern manufacturing. Reliable functioning of induction motors is critical to minimizing machine downtime and maintaining high performance, which contributes to scrap-free production and overall sustainability in manufacturing. Due to the com...
Article
Full-text available
Digital twin introduces new opportunities for predictive maintenance of manufacturing machines which can consider the influence of working condition on cutting tool and contribute to the understanding and application of the predicted results. This paper presents a data-driven model for digital twin, together with a hybrid model prediction method ba...
Article
Full-text available
For energy storage, lithium-ion batteries have been widely utilized in cell phones, electric vehicles, and many electrical and mechanical devices. Accordingly, their performance significantly affects these devices’ usage experience. This paper presents a particle filter-enabled prognostic modeling method to identify and track battery performance de...
Article
Timely context awareness is key to improving operation efficiency and safety in human-robot collaboration (HRC) for intelligent manufacturing. Visual observation of human workers’ motion provides informative clues about the specific tasks to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this go...
Article
Based on B-spline wavelet on the interval (BSWI) and the multivariable generalized variational principle, the multivariable wavelet finite element for flat shell is constructed by combining the elastic plate element and the Mindlin plate element together. First, the elastic plate element formulation is derived from the generalized potential energy...
Conference Paper
As a critical element in rotating machines, remaining useful life (RUL) prediction of rolling bearings plays an essential role in realizing predictive and preventative machine maintenance in modern manufacturing. The physics of defect (e.g. spall) initiation and propagation describes bearing’s service life as generally divided into three stages: no...