Dr. Chao Liu is currently working as a Research Assistant Professor at The Hong Kong Polytechnic University. His research interests focus on the smart manufacturing technologies. Address: Hong Kong
International Journal of Production Research, 2021
Machine tools play a pivotal role in the manufacturing world since their performance significantl... more Machine tools play a pivotal role in the manufacturing world since their performance significantly affects the product quality and production efficiency. In the era of Industry 4.0, machine tools are expected to have a higher level of accessibility, connectivity, intelligence, adaptivity, and autonomy. With the rapid development and application of various Industry 4.0 technologies, digitalisation and servitisation of machine tools have become a new research trend. However, few review articles on the development of machine tools in the context of Industry 4.0 have been reported. To understand the current status of digitalisation and servitisation of machine tools, this paper provides a systematic literature review combining both bibliometric and qualitative analysis. Our review results provide a comprehensive and in-depth understanding of recent advancements of digitalisation and servitisation of machine tools, including the key enabling technologies, methods, standards, architectures, and applications. Furthermore, we propose a novel conceptual framework of Cyber-Physical Machine Tool (CPMT) as a systematic approach to achieving digitalisation and servitisation of next-generation machine tools. Finally, major research issues, challenges, and future research directions are discussed. This work will help researchers and industrial practitioners spark new ideas for developing the next-generation machine tools in the era of Industry 4.0.
Robotics and Computer-Integrated Manufacturing, 2022
Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous a... more Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and ondemand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a serviceoriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool. Our research suggests that purposely developed service-oriented data schemas that capture the essential information for high-level cloud manufacturing decision-making via PnP IIoT technologies are a good solution for connecting field-level manufacturing equipment to a cloud manufacturing platform.
Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to ... more Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.
Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter,... more Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Development of CPMT requires standardized information modelling method and communication protocols for machine tools. This paper proposes a CPMT Platform based on OPC UA and MTConnect that enables standardized, interoperable and efficient data communication among machine tools and various types of software applications. First, a development method for OPC UA-based CPMT is proposed based on a generic OPC UA information model for CNC machine tools. Second, to address the issue of interoperability between OPC UA and MTConnect, an MTConnect to OPC UA interface is developed to transform MTConnect information model and its data to their OPC UA counterparts. An OPC UA-based CPMT prototype is developed and further integrated with a previously developed MTConnectbased CPMT to establish a common CPMT Platform. Third, different applications are developed to demonstrate the advantages of the proposed CPMT Platform, including an OPC UA Client, an advanced AR-assisted wearable Human-Machine Interface and a conceptual framework for CPMT powered cloud manufacturing environment. Experimental results have proven that the proposed CPMT Platform can significantly improve the overall production efficiency and effectiveness in the shop floor.
Machine Tool 4.0 introduces a new generation of machine tools that are smarter, well connected, w... more Machine Tool 4.0 introduces a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Cyber-Physical Machine Tools (CPMT), based on recent advancements of the Information and Communication Technology, provides a promising solution for Machine Tool 4.0. This paper proposes a systematic development method for CPMT. Generic system architecture is developed to provide guidelines for advancing existing Computer Numerical Control (CNC) machine tools to CPMT. The proposed architecture allows machine tool, machining processes, real-time machining data and intelligent algorithms to be deeply integrated through various types of networks. The development methodologies for the core of the CPMT, the Machine Tool Cyber Twin (MTCT), are studied and discussed in detail. MTCT enables different types of feedback loops among the physical world, the cyber space and humans to be realized. An MTConnect-based CPMT prototype is developed to validate the proposed CPMT. Experimental results have proved great interoperability, connectivity and extensibility of the proposed CPMT. The potential of implementing artificial intelligence in CPMT is also discussed.
Aiming to advance current machine tools to a higher level of intelligence and autonomy, this pape... more Aiming to advance current machine tools to a higher level of intelligence and autonomy, this paper presents a new generation of machine tools, i.e. Cyber-Physical Machine Tool (CPMT), inspired by the recent advances in Cyber-Physical Systems (CPS). CPMT refers to a CPS-enabled machine tool that integrates physical machine tool and machining processes with computation and networking capabilities. Augmented Reality (AR) is used to enable intuitive and efficient human-machine interactions between humans and CPMT. An AR-assisted Intelligent Window for CPMT is proposed. The Intelligent Window is essentially an advanced Human-Machine Interface (HMI) which provides users with intuitive interactions with CPMT. The proposed Intelligent Window consists of four main functional modules, Real-time Control, AR-enabled Process Monitoring, AR-enabled Machining Simulation, and Process Optimization. An AR-assisted Intelligent Window for an EMCO Concept 105 milling machine is developed making use of a touch-screen computer. The advantages and potentials of CPS and AR in manufacturing are discussed based on the experience gained from the experiments.
In the past five years, several industrial initiatives such as " Industry 4.0 " , " Industrial In... more In the past five years, several industrial initiatives such as " Industry 4.0 " , " Industrial Internet of Things " , " Factories of the Future " and " Made in China 2025 " , have been announced by different governments and industrial leaders. These initiatives lead to an urgent need to advance current manufacturing systems into a high level of intelligence and autonomy. As the main component of any manufacturing system, machine tools have evolved from manually operated machines into the current computer numerically controlled (CNC) machine tools. It is predicted that current CNC machine tools are not intelligent and autonomous enough to support the smart manufacturing systems envisioned by the aforementioned initiatives. Inspired by recent advances in ICT such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), this paper proposes a new generation of machine tools, i.e. Machine Tool 4.0, as a future development trend of machine tools. Machine Tool 4.0, otherwise known as Cyber-Physical Machine Tool (CPMT), is the integration of machine tool, machining processes, computation and networking, where embedded computers and networks can monitor and control the machining processes, with feedback loops in which machining processes can affect computations and vice versa. The main components and functions of a CPMT are presented. The key research issues related to the development of CPMT are identified and discussed. A three-layer CPMT-centered Cyber Physical Production System (CPPS) is proposed to illustrate both the vertical integration of various smart systems at different hierarchical levels and the horizontal integration of field-level manufacturing facilities and resources.
Recent advancements in Information and Communication Technology have shown immense potential of a... more Recent advancements in Information and Communication Technology have shown immense potential of advancing current CNC machine tools to Cyber-Physical Machine Tools (CPMT). CPMT deeply integrates machine tool and machining processes with computation and networking, thus becoming more intelligent, interconnected and autonomous. However, challenges of data communication and management remain. This is due to the diversity of manufacturing devices, sensors used and hence the delivery of data types. This paper presents an MTConnect-based CPMT that allows diverse types of real-time manufacturing data to be effectively and efficiently collected and managed, to enable advanced human machine interactions and cloud-based decision-making supports in CPMT.
Cyber-Physical Production Systems (CPPS) often use wireless sensor networks (WSNs) for monitoring... more Cyber-Physical Production Systems (CPPS) often use wireless sensor networks (WSNs) for monitoring purposes. However , data from WSNs may be inaccurate and unreliable due to power exhaustion, noise and other issues. In order to achieve a reliable and accurate data acquisition while ensuring low energy consumption and long lifetime of WSNs, data cleansing algorithms for energy-saving are proposed in this research. The cleansing algorithms are computationally lightweight in local sensors and energy-efficient due to low energy consumption in communications. Dynamic voltage scaling and dynamic power management are adopted for reducing energy consumption, without compromising the performance at system level. A low-power protocol for sink node communication is proposed at network level. A health monitoring system for a Cyber-Physical Machine Tool (a typical example of CPPS) is designed. Experiment results show that the proposed energy-saving data cleansing algorithm yields high-performance and effective monitoring.
Information and communication technology is undergoing rapid development, and many disruptive tec... more Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.
International Journal of Production Research, 2021
Machine tools play a pivotal role in the manufacturing world since their performance significantl... more Machine tools play a pivotal role in the manufacturing world since their performance significantly affects the product quality and production efficiency. In the era of Industry 4.0, machine tools are expected to have a higher level of accessibility, connectivity, intelligence, adaptivity, and autonomy. With the rapid development and application of various Industry 4.0 technologies, digitalisation and servitisation of machine tools have become a new research trend. However, few review articles on the development of machine tools in the context of Industry 4.0 have been reported. To understand the current status of digitalisation and servitisation of machine tools, this paper provides a systematic literature review combining both bibliometric and qualitative analysis. Our review results provide a comprehensive and in-depth understanding of recent advancements of digitalisation and servitisation of machine tools, including the key enabling technologies, methods, standards, architectures, and applications. Furthermore, we propose a novel conceptual framework of Cyber-Physical Machine Tool (CPMT) as a systematic approach to achieving digitalisation and servitisation of next-generation machine tools. Finally, major research issues, challenges, and future research directions are discussed. This work will help researchers and industrial practitioners spark new ideas for developing the next-generation machine tools in the era of Industry 4.0.
Robotics and Computer-Integrated Manufacturing, 2022
Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous a... more Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and ondemand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a serviceoriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool. Our research suggests that purposely developed service-oriented data schemas that capture the essential information for high-level cloud manufacturing decision-making via PnP IIoT technologies are a good solution for connecting field-level manufacturing equipment to a cloud manufacturing platform.
Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to ... more Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.
Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter,... more Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Development of CPMT requires standardized information modelling method and communication protocols for machine tools. This paper proposes a CPMT Platform based on OPC UA and MTConnect that enables standardized, interoperable and efficient data communication among machine tools and various types of software applications. First, a development method for OPC UA-based CPMT is proposed based on a generic OPC UA information model for CNC machine tools. Second, to address the issue of interoperability between OPC UA and MTConnect, an MTConnect to OPC UA interface is developed to transform MTConnect information model and its data to their OPC UA counterparts. An OPC UA-based CPMT prototype is developed and further integrated with a previously developed MTConnectbased CPMT to establish a common CPMT Platform. Third, different applications are developed to demonstrate the advantages of the proposed CPMT Platform, including an OPC UA Client, an advanced AR-assisted wearable Human-Machine Interface and a conceptual framework for CPMT powered cloud manufacturing environment. Experimental results have proven that the proposed CPMT Platform can significantly improve the overall production efficiency and effectiveness in the shop floor.
Machine Tool 4.0 introduces a new generation of machine tools that are smarter, well connected, w... more Machine Tool 4.0 introduces a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Cyber-Physical Machine Tools (CPMT), based on recent advancements of the Information and Communication Technology, provides a promising solution for Machine Tool 4.0. This paper proposes a systematic development method for CPMT. Generic system architecture is developed to provide guidelines for advancing existing Computer Numerical Control (CNC) machine tools to CPMT. The proposed architecture allows machine tool, machining processes, real-time machining data and intelligent algorithms to be deeply integrated through various types of networks. The development methodologies for the core of the CPMT, the Machine Tool Cyber Twin (MTCT), are studied and discussed in detail. MTCT enables different types of feedback loops among the physical world, the cyber space and humans to be realized. An MTConnect-based CPMT prototype is developed to validate the proposed CPMT. Experimental results have proved great interoperability, connectivity and extensibility of the proposed CPMT. The potential of implementing artificial intelligence in CPMT is also discussed.
Aiming to advance current machine tools to a higher level of intelligence and autonomy, this pape... more Aiming to advance current machine tools to a higher level of intelligence and autonomy, this paper presents a new generation of machine tools, i.e. Cyber-Physical Machine Tool (CPMT), inspired by the recent advances in Cyber-Physical Systems (CPS). CPMT refers to a CPS-enabled machine tool that integrates physical machine tool and machining processes with computation and networking capabilities. Augmented Reality (AR) is used to enable intuitive and efficient human-machine interactions between humans and CPMT. An AR-assisted Intelligent Window for CPMT is proposed. The Intelligent Window is essentially an advanced Human-Machine Interface (HMI) which provides users with intuitive interactions with CPMT. The proposed Intelligent Window consists of four main functional modules, Real-time Control, AR-enabled Process Monitoring, AR-enabled Machining Simulation, and Process Optimization. An AR-assisted Intelligent Window for an EMCO Concept 105 milling machine is developed making use of a touch-screen computer. The advantages and potentials of CPS and AR in manufacturing are discussed based on the experience gained from the experiments.
In the past five years, several industrial initiatives such as " Industry 4.0 " , " Industrial In... more In the past five years, several industrial initiatives such as " Industry 4.0 " , " Industrial Internet of Things " , " Factories of the Future " and " Made in China 2025 " , have been announced by different governments and industrial leaders. These initiatives lead to an urgent need to advance current manufacturing systems into a high level of intelligence and autonomy. As the main component of any manufacturing system, machine tools have evolved from manually operated machines into the current computer numerically controlled (CNC) machine tools. It is predicted that current CNC machine tools are not intelligent and autonomous enough to support the smart manufacturing systems envisioned by the aforementioned initiatives. Inspired by recent advances in ICT such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), this paper proposes a new generation of machine tools, i.e. Machine Tool 4.0, as a future development trend of machine tools. Machine Tool 4.0, otherwise known as Cyber-Physical Machine Tool (CPMT), is the integration of machine tool, machining processes, computation and networking, where embedded computers and networks can monitor and control the machining processes, with feedback loops in which machining processes can affect computations and vice versa. The main components and functions of a CPMT are presented. The key research issues related to the development of CPMT are identified and discussed. A three-layer CPMT-centered Cyber Physical Production System (CPPS) is proposed to illustrate both the vertical integration of various smart systems at different hierarchical levels and the horizontal integration of field-level manufacturing facilities and resources.
Recent advancements in Information and Communication Technology have shown immense potential of a... more Recent advancements in Information and Communication Technology have shown immense potential of advancing current CNC machine tools to Cyber-Physical Machine Tools (CPMT). CPMT deeply integrates machine tool and machining processes with computation and networking, thus becoming more intelligent, interconnected and autonomous. However, challenges of data communication and management remain. This is due to the diversity of manufacturing devices, sensors used and hence the delivery of data types. This paper presents an MTConnect-based CPMT that allows diverse types of real-time manufacturing data to be effectively and efficiently collected and managed, to enable advanced human machine interactions and cloud-based decision-making supports in CPMT.
Cyber-Physical Production Systems (CPPS) often use wireless sensor networks (WSNs) for monitoring... more Cyber-Physical Production Systems (CPPS) often use wireless sensor networks (WSNs) for monitoring purposes. However , data from WSNs may be inaccurate and unreliable due to power exhaustion, noise and other issues. In order to achieve a reliable and accurate data acquisition while ensuring low energy consumption and long lifetime of WSNs, data cleansing algorithms for energy-saving are proposed in this research. The cleansing algorithms are computationally lightweight in local sensors and energy-efficient due to low energy consumption in communications. Dynamic voltage scaling and dynamic power management are adopted for reducing energy consumption, without compromising the performance at system level. A low-power protocol for sink node communication is proposed at network level. A health monitoring system for a Cyber-Physical Machine Tool (a typical example of CPPS) is designed. Experiment results show that the proposed energy-saving data cleansing algorithm yields high-performance and effective monitoring.
Information and communication technology is undergoing rapid development, and many disruptive tec... more Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.
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Selected Publications by Chao Liu
to Cyber-Physical Machine Tools (CPMT). CPMT deeply integrates machine tool and machining processes with computation and networking, thus becoming more intelligent, interconnected and autonomous. However, challenges of data communication and management remain. This is due to the diversity of manufacturing devices, sensors used and hence the delivery of data types. This paper presents an MTConnect-based CPMT that allows diverse types of real-time manufacturing data to be effectively and efficiently collected and managed, to enable advanced human machine interactions and cloud-based decision-making supports in CPMT.
to Cyber-Physical Machine Tools (CPMT). CPMT deeply integrates machine tool and machining processes with computation and networking, thus becoming more intelligent, interconnected and autonomous. However, challenges of data communication and management remain. This is due to the diversity of manufacturing devices, sensors used and hence the delivery of data types. This paper presents an MTConnect-based CPMT that allows diverse types of real-time manufacturing data to be effectively and efficiently collected and managed, to enable advanced human machine interactions and cloud-based decision-making supports in CPMT.