During the past decade, the Internet, Web and intelligent agents have been used in attempts to implement distributed intelligent manufacturing systems. Network communication and message services are important aspects for such systems. This paper first introduces a new concept called iShopFloor-an intelligent shop floor based on the Internet, Web and agent technologies. The paper then reports some of our recent work on the implementation of XML-based message services for Internet-enabled, agent-based, intelligent shop floors. The objective is to investigate XML for message exchanges among Internet-enabled shop floor devices that are represented by intelligent agents. The paper discusses the advantages of using XML for message services and presents our initial implementation within the iShopFloor prototype environment. This implementation has demonstrated the following advantages of using XML-based message services on the shop floor: (1) simplification and standardization of message services in Internet-based, intelligent shop floors; (2) facilitation of the integration of an agent-based scheduling system with other intelligent shop floor systems, including Web-based shop floor monitoring and control systems; and (3) possibility of creating data views on the fly.

The aim of this paper to give a comprehensive overview of existing techniques and state-of-the-art systems for indoor localization that could be adopted in smart factories of the future. We present different techniques for calculating the position of a moving object using signal transmission and signal measurement, and compare their advantages and disadvantages. The paper also includes a discussion of various localization systems available in the market and compares their most important features. It ends with a discussion of important issues to consider in future work in order to fully implement indoor, real-time localization of operators in the smart factory.

Augmented reality is currently a hot research topic within manufacturing and a great potential of the technique is seen. This study aims to increase the knowledge of the adaptation and usability of augmented reality for the training of operators. A new approach of using dynamic information content is proposed that is automatically adjusted to the individual operator and his/her learning progress for increased efficiency and shorter learning times. The approach make use of the concept of expert systems from the field of artificial intelligence for determine the information content on-line. A framework called "Augmented Reality Expert System" (ARES) is developed that combines AR and expert systems. A proof-of-concept evaluation of the framework is presented in the paper and possible future extensions are discussed.

This paper describes a study of using the concept of augmented reality for supporting assembly line workers in carrying out their task optimally. By overlaying virtual information on real world objects and thereby enhance the human's perception of reality - augmented reality makes it possible to improve the visual guidance to the workers. In the study, a prototype system is developed based on the Oculus Rift platform and evaluated using a simulated assembling task. The main aim is to investigate user acceptance and how this can possible be improved.

With augmented reality, virtual information can be overlaid on the real world in order to enhance a human's perception of reality. In this study, we aim to deepen the knowledge of augmented reality in the shop-floor context and analyze its role within smart factories of the future. The study evaluates a number of approaches for realizing augmented reality and discusses advantages and disadvantages of different solutions from a shop-floor operator's perspective. The evaluation is done in collaboration with industrial companies, including Volvo Cars and Volvo GTO amongst others. The study also identifies important future research directions for utilizing the full potential of the technology and successfully implement it on industrial shop-floors.

As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) has experienced rapid development in the past five years. The research on its theories, key technologies, developments, and applications still keeps attracting attentions from more and more researchers. One of the most important issues to its improvements and quality of service (QoS) is the manufacturing service management (MSM). CMfg aims to realize the full-scale sharing, free circulation and transaction, and on-demand use of various manufacturing resource and capabilities in the form of manufacturing service. Without the effective operation and technical support of MSM, the implementation of CMfg and its aim cannot be achieved. It is therefore necessary to summarize the existing works and technologies on MSM in CMfg. This paper first provides a brief overview of CMfg and then focuses on the problem of MSM in CMfg from the service lifecycle perspective. The advances on MSM technology from eleven aspects are investigated and summarized. Finally, future research directions are identified and discussed. It is evident that the future MSM in CMfg is closely related to Internet of things (IoT), big data, and cloud computing.

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis.

The objective of this research is to develop a set of enabling technologies for Web-based remote machining in a decentralized environment. Particularly, this paper presents our latest development on 3D model-based and sensor-driven remote machining. Once a product design is given, its process plan and NC codes are generated by using a distributed process planning (DPP) system. The NC codes are then used for remote machining via a standard Web browser. In this paper, the focus is given to the concept and prototype implementation of the technology. A case study of a test part machining on a 5-axis milling machine is also completed for testing and validation. It is expected that the developed technology can also be applied to design verification as well as production in a distributed manufacturing environment.

The objective of this research is to develop methodology and algorithms for web-based digital manufacturing, supported by real-time monitoring for dynamic scheduling. This paper presents in particular an integrated approach for developing a web-based system, including distributed process planning, real-time monitoring and remote machining. It is enabled by a Wise-ShopFloor (Web-based integrated sensor-driven e-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilizing the latest Java technologies (Java 3D and Java Servlet) for system implementation, this approach allows users to plan and control distant shop floor operations based on runtime information from the shop floor. Details on the principle of the Wise-ShopFloor framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward web-based digital manufacturing.

Owing to the business decentralisation and outsourcing, manufacturing is moving toward the direction in distributed environment. A new enabling technology is required, especially in remote monitoring and control of daily manufacturing operations. As web is rooted into business, a web-based solution for real-time monitoring and control is preferable due to its popularity, low cost, and availability. However, unpredictable network traffic posts a major challenge for web-based real-time application development. This paper proposes to use computer graphics augmented with real sensor data for reducing data transmission over the network. Particularly, a web-based technology for remote robot operations is presented.

The turbulent environment of dynamic job-shop operations affects shop-floor layout as well as manufacturing operations. Due to the dynamic nature of shop-floor layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues for material handling and machine relocation when reconfiguring a shop floor’s layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. Genetic algorithm is used where changes may cause a re-layout of the entire shop, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the author’s previous work.

Small- and medium-sized enterprises (SMEs) in job-shop machining are experiencing more shop-floor uncertainties today than ever before, due to multi-tier outsourcing, customised product demands and shortened product lifecycle. In a fluctuating shop floor environment, a process plan generated in advance is often found unsuitable or unusable to the targeted resources, resulting both in wasted effort spent in early process planning and in productivity drop when idle machines have to wait for operations to be re-planned. Consequently, an adaptive process planning approach is in demand. Targeting shop-floor uncertainty, the objective of this research is to develop a novel adaptive process planning method that can generate process plans at runtime to unplanned changes. This paper, in particular, presents an overview of adaptive process planning research and a new methodology, including two-layer system architecture, generic supervisory planning, machine-specific operation planning, and adaptive setup planning. Particularly, function blocks are introduced as a core enabling technology to bridge the gap between computer systems and CNC systems for adaptive machining.

This paper presents an overview of the latest advancement of Internet-enabled cloud-based cyber manufacturing and new approaches for hardware-in-the-loop real-time applications, including cloud-based remote monitoring and control of industrial robots, remote assembly in a cyber-physical environment, and a cloud robotic system for energy-efficient operations. Altogether, they form an integrated cloud-based cyber manufacturing system. In terms of enabling technologies, they are the unique combination of virtual 3D models driven by real sensors, and image-to-model based representation of dynamic environment to guide cyber users. The objective of this research is to significantly reduce network traffic over the Internet for cyber manufacturing. This paper includes case studies, the results of which show that the integrated cyber-physical system consumes less than 1% of network bandwidth of traditional camera-based systems with a 30msec latency of real-time operations. They are feasible and practical as cyber manufacturing solutions.

This paper introduces an adaptive meshing algorithm to handle problems in dynamic finite element analysis and runtime simulation, where mesh re-generation or dynamic adjustment is required. Based on a concept called CBC (coded box cell) Substitution, this algorithm can be applied to both initial mesh generation and dynamic mesh adjustment along the border zones of multiple primitives that form an entire model. During the initial mesh generation, appropriate labels are assigned to the nodes and the faces of each finite element. These labels are used to facilitate decision-makings in dynamic mesh adjustment. A mapping technique is adopted to transform curved surfaces to plain ones for the ease of automatic mesh adjustment while still using the same algorithm. The meshing examples show that a finite element mesh can be adjusted dynamically and locally around its border zone; and the algorithm can be utilised effectively to simulate the thermal behaviour of a device under real operating conditions.

Manufacturing has been one of the key areas that support and influence a nation’s economy since  the  18th  century.  Being  the  primary  driving  force  in  economy  growth,  manufacturing constantly serves as the foundation and contributes to other industries. In the past centuries, manufacturing contributed significantly to modern civilisation and created momentum that is driving today’s  economy. Despite of various revolutionary achievements, we are still facing challenges  when  striving  to  achieve  greater  success  in  manufacturing  in  the  21st  century. This paper highlights the challenges, particularly in adaptive and collaborative manufacturing, and offers a unique approach to solving the problems.

This paper presents a new approach for real-time collaborations in adaptive manufacturing, including web-based remote monitoring and control of an industrial robot, and active collision avoidance for human-robot collaborations. It is enabled by using virtual 3D models driven by real sensor data and depth images of human operators. The objectives of this research are to significantly reduce network traffic needed for real-time monitoring over the Internet and to increase the human safety in a human-robot coexisting environment. The results of a case study show that the approach consumes less than 1% of network bandwidth of traditional camera-based methods, and is feasible and practical as a web-based solution

Future manufacturing systems will be required to be agile, flexible, and fault-tolerant. Next generation manufacturing systems will be integrated networks of distributed resources simultaneously capable of combined knowledge processing and material processing. The objective of this research is to define a generic open architecture for such kind of distributed manufacturing systems, especially for holonic manufacturing systems (HMS). The primary focus will be given to its collaborative design and implementation approach based on agent technology and emerging function block standards. This paper will first address issues associated with HMS, and then discuss the two useful implementation techniques – agent technology and function block. Finally, a collaborative design approach for the next generation HMS will be proposed based on these implementation techniques. Emphasis will also be extended and given to metamorphic control of HMS using multi-agent negotiation and cooperation. The proposed approach, together with its open architecture, shows much promise for improving the entire manufacturing system performance under the ever-changing real-time and distributed environments.

This paper presents a new approach for real-time collaborations in adaptive manufacturing, including web-based remote monitoring and control of an industrial robot, and active collision avoidance for human-robot collaborations. It is enabled by using virtual 3D models driven by real sensor data and depth images of human operators. The objectives of this research are to significantly reduce network traffic needed for real-time monitoring over the Internet and to increase human safety in a human-robot coexisting environment. The ultimate goal is to enhance the sustainability of manufacturing operations in decentralised dynamic environments with safety protection. The results of a case study show that the approach consumes less than 1 % of network bandwidth of traditional camera-based methods and is feasible and practical as a web-based solution.

This paper presents a hybrid approach for facility layout redesign and dynamic job routing. More specifically, based on the source of uncertainty, the facility layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. Genetic algorithm is used where changes may cause a layout redesign of the entire shop, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. The method is verified in a case study of a hypothetic robotic assembly shop.

This paper presents an integrated intuitive system for disassembly planning by actively tracking the motion of an experienced operator. It can also be used for operators training by combining a virtual reality (VR) environment with the motion tracking. The developed conceptual prototype for disassembly planning and training enables individuals to interact with a virtual environment in real time. It extends the technology of motion tracking and integrates it with virtual environment technology to create real-time virtual work cell simulations in which disassembly operators may be immersed with hands-on experiences. In addition to the operators training, the experimental results to date are presented to demonstrate the potential contributions of human skills in achieving effective disassembly planning for remanufacturing. It is expected that this approach will lead to environment-friendly and sustainable operations by conserving energy and cost that are first tested in a human-emerged virtual system.

The objective of this research is to develop a new methodology for intelligent and distributed process planning. The primary focus of this paper is on the architecture of the new process planning approach, using function blocks as controller language. The secondary focus is on the other supporting technologies such as machining feature-based design and agent-based decision-making. The methodology proposed for distributed process planning is based on a design-for-machining concept that can seamlessly integrate feature-based design and agent-based planning into function block-based CNC control. Different from traditional methods, the proposed approach has a two-layer structure – supervisory planning and operation planning. The supervisory planning is performed in advance at shop floor level, followed by the operation planning accomplished at run-time at machine level by open CNC controllers. Through decentralization, the distributed process planning shows promise of improving system performance within today’s continually changing shop floor environment.

Next generation manufacturing systems will be integrated networks of distributed resources simultaneously capable of combined knowledge and material processing. These manufacturing systems will be required to be agile, flexible, and fault-tolerant. The objective of this research is to define a generic open architecture for such kind of distributed manufacturing systems, especially for holonic manufacturing systems (HMS). This paper will address issues associated with HMS, and propose a reference architecture based on a design-to-control concept. The primary focus will be given to the collaborative and integrated design-to-control approach based on machining feature, agent technology, and function block standards. Emphasis is also extended and given to metamorphic process planning and control of HMS using multi-agent negotiation and cooperation. The proposed approach, together with the open architecture, shows much promise for improving the entire manufacturing system performance under the ever-changing real-time and distributed shop floor environments.

Cloud manufacturing as a trend of future manufacturing would provide cost-effective, flexible and scalable solutions to companies by sharing manufacturing resources as services with lower support and maintenance costs. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces IEC 61499 function blocks for prototype implementation. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability and execution status monitoring during metal-cutting operations, both locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing.

This paper presents an overview of an adaptive setup planning system that considers both the availability and capability of machines on a shop floor. It integrates scheduling functions at setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific optimal setup plans. The objective is to enable adaptive setup planning for dynamic machining job shop operations. Particularly, this paper documents basic algorithms and architecture of the setup planning system for dynamically assigned machines. It is then validated through a case study.

This paper presents an integrated approach for developing a web-based system with enhanced adaptability, including distributed process planning, real-time monitoring and remote machining. The objective is to develop a new methodology and relevant processing algorithms for enhancing adaptability in digital manufacturing. This approach is enabled by a Wise-ShopFloor (Web-based integrated sensor-driven e-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilising the latest Java technologies (Java 3D and Java Servlet) for system implementation, it allows end-users to plan and control distant manufacturing operations based on runtime information from shop floors. Details on the principle of the Wise-ShopFloor framework, system architecture, and a prototype system are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward web-based digital manufacturing.

This paper presents methodologies of web-based decision making for collaborative manufacturing, including web-based knowledge sharing, distributed process planning, dynamic scheduling, real-time monitoring and remote control, targeting distributed yet collaborative manufacturing environments. The web-based decision making is enabled by a framework that allows users to plan and control manufacturing operations based on information either gathered via the Web or collected from manufacturing devices. The objective of this research is to develop an integrated system for web-based collaborative planning and control, supported by real-time monitoring for dynamic scheduling. Details on the principle of the framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of remote machining is chosen as a case study to demonstrate the effectiveness of this framework toward web-based collaborative manufacturing.