Cloud manufacturing is emerging as a new manufacturing paradigm as well as an integrated technology, which is promising in transforming today's manufacturing industry towards service-oriented, highly collaborative and innovative manufacturing in the future. In order to better understand cloud manufacturing, this paper provides a critical review of relevant concepts and ideas in cloud computing as well as advanced manufacturing technologies that contribute to the evolution of cloud manufacturing. The key characteristics of cloud manufacturing are also presented in order to clarify the cloud manufacturing concept. Furthermore, a four-process structure is proposed to describe the typical scenario in cloud manufacturing, hoping to provide a theoretical reference for practical applications. Finally, an application case of a private cloud manufacturing system for a conglomerate is presented.
Rapid development in cloud computing has made an impact on the manufacturing industry. Consequently, cloud manufacturing has been proposed and has become a hot topic in the past 3 years. Many technologies such as service-oriented architecture, resource virtualisation, service ontology and modelling, service composition and management, and product data integration have been used to build the architecture of cloud manufacturing platforms. In this article, the authors survey the state of the art in the area of cloud manufacturing, identify recent research directions, and discuss potential research opportunities.
Currently, the typical challenges that manufacturing enterprises faced are the lack of timely, accurate and consistent information of manufacturing things (resources) during manufacturing execution. Real-time information visibility and traceability allows decision makers to make better-informed shop-floor decisions. In this article, a real-time information capturing and integration architecture of the internet of manufacturing things (IoMT) is presented to provide a new paradigm by extending the techniques of IoT to manufacturing field. Under this architecture and its key components, the manufacturing things such as operators, machines, pallets, materials etc. can be embedded with sensors, they can interact with each other. Considering the challenges of processing a huge amount of real-time data into useful information and exchange it among the heterogeneous application systems, a Real-time Manufacturing Information Integration Service (RTMIIS) has been designed to achieve seamless dual-way connectivity and interoperability among enterprise layer, workshop floor layer and machine layer. Finally, a near-life scenario has been used to illustrate the proof-of-concept application of the proposed IoMT.
The wide interest of research and industry in the human-robot interaction (HRI) related topics is proportional to the increased productivity and flexibility of the production lines, as it combines human and robot capabilities. This paper presents a review of recent research and progress on HRI, related to task planning/coordination and programming with emphasis on the manufacturing/production environment. Human-robot task allocation and scheduling, metrics for HRI, as well as the social aspects are reviewed. The role of digital human modelling systems for human-robot task planning related issues is also discussed. The process of learning by demonstration as well as the instructive systems is reviewed, focussing mainly on programming through visual guidance and imitation, voice commands and haptic interaction. The aspect of physical HRI as well as the safety related issues are also discussed. Additionally, a survey on multimodal communication frameworks is presented. Challenges encountered and directions for future research are discussed.
For the past three decades, computer-aided process planning (CAPP) has attracted a large amount of research interest. A huge volume of literature has been published on this subject. Today, CAPP research faces new challenges owing to the dynamic markets and business globalisation. Thus, there is an urgent need to ascertain the current status and identify future trends of CAPP. Covering articles published on the subjects of CAPP in the past 10 years or so, this article aims to provide an up-to-date review of the CAPP research works, a critical analysis of journals that publish CAPP research works, and an understanding of the future direction in the field. First, general information is provided on CAPP. The past reviews are summarised. Discussions about the recent CAPP research are presented in a number of categories, i.e. feature-based technologies, knowledge-based systems, artificial neural networks, genetic algorithms, fuzzy set theory and fuzzy logic, Petri nets, agent-based technology, Internet-based technology, STEP-compliant CAPP and other emerging technologies. Research on some specific aspects of CAPP is also provided. Discussions and analysis of the methods are then presented based on the data gathered from the Elsevier's Scopus abstract and citation database. The concepts of 'Subject Strength' of a journal and 'technology impact factor' are introduced and used for discussions based on the publication data. The former is used to gauge the level of focus of a journal on a particular research subject/domain, whereas the latter is used to assess the level of impact of a particular technology, in terms of citation counts. Finally, a discussion on the future development is presented.
There is an ongoing paradigm shift in manufacturing, in which the modern manufacturing industry is changing towards global manufacturing networks and supply chains. This will lead to the flexible usage of different globally distributed, scalable and sustainable, service-oriented manufacturing systems and resources. Combining recently emerged technologies, such as Internet of Things, Cloud Computing, Semantic Web, service-oriented technologies, virtualisation and advanced high-performance computing technologies, with advanced manufacturing models and information technologies, Cloud Manufacturing is a new manufacturing paradigm built on resource sharing, supporting and driving this change. It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities and equipment, will then be made accessible to presumptive consumers on a worldwide basis. Cloud Manufacturing has been in focus for a great deal of research interest and suggested applications during recent years, by both industrial and academic communities. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with identified outstanding research issues, and future trends and directions within Cloud Manufacturing.
Today, hybrid manufacturing technology has drawn significant interests from both academia and industry due to the capability to make products in a more efficient and productive way. Although there is no specific consensus on the definition of the term 'hybrid processes', researchers have explored a number of approaches to combine different manufacturing processes with the similar objectives of improving surface integrity, increasing material removal rate, reducing tool wear, reducing production time and extending application areas. Thus, hybrid processes open up new opportunities and applications for manufacturing various components which are not able to be produced economically by processes on their own. This review paper starts with the classification of current manufacturing processes based on processes being defined as additive, subtractive, transformative, joining and dividing. Definitions of hybrid processes from other researchers in the literature are then introduced. The major part of this paper reviews existing hybrid processes reported over the past two decades. Finally, this paper attempts to propose possible definitions of hybrid processes along with the authors' classification, followed by discussion of their developments, limitations and future research needs.
This is a study of a Human-Robot Collaboration (HRC) framework for the execution of collaborative tasks in hybrid assembly cells. Robots and humans coexist in the same cell and share tasks according to their capabilities. An intelligent decision-making method that allows human-robot task allocation is proposed and is integrated within a Robot Operating System (ROS) framework. The proposed method enables the allocation of sequential tasks assigned to a robot and a human in separate workspaces. The focus is rather given to the human-robot coexistence for the execution of sequential tasks, in order for the automation level in manual or even hybrid assembly lines to be increased. Body gestures are the means of a human's interaction with a robot for commanding and guiding reasons. The proposed framework is implemented into a case coming from the manual assembly lines of an automotive industry. A preliminary design of a hybrid assembly cell is presented, focusing on the assembly of a hydraulic pump by robots and humans.
Supplier selection is a critical process in sustainable supply chain management. Increased pressure from stakeholders has forced companies to search for methodologies that help in arriving at intelligent supplier selection decisions. This is a unique study as it illustrates how to optimise orders among various suppliers while taking into consideration all three dimensions of sustainability - economic, social, and environmental. Previous studies have mostly relied on simple ranking of suppliers on the basis of past performance for selection. Those studies that did emphasise on optimisation of orders among suppliers, did not consider all three dimensions of sustainability. To establish an improved sustainable supply chain, this study uses integrated fuzzy AHP and fuzzy multi-objective linear programming approach for order allocation among suppliers. fuzzy AHP has been used for weighing various factors such as quality, lead time, cost, energy use, waste minimisation, emission, and social contribution, and weights of the factors have been considered for developing linear programming. Demand has been taken as a fuzzy variable in this model. The case of an Indian automobile company has been taken as illustration.
Digital manufacturing technologies have been considered an essential part of the continuous effort towards the reduction in a product's development time and cost, as well as towards the expansion in customisation options. The simulation-based technologies constitute a focal point of digital manufacturing solutions, since they allow for the experimentation and validation of different product, process and manufacturing system configurations. This article investigates simulation-based applications in a series of different technological and manufacturing domains. First, this article discusses the current industrial practice, focusing on the use of information technology. Next, a series of simulation-based solutions are explored in the domains of product and production process design, as well as in the area of enterprise resource planning. The current technologies and research trends are discussed in the context of the new landscape of computing hardware technologies and the emerging computing services, including the initiatives comprising both the Internet cloud and the Internet of things.
Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in products, processes or systems before they occur. Although many industries use the traditional FMEA technique, it has been criticised for having several setbacks. First, the current FMEA determines the risk priorities of failure modes by using risk priority numbers (RPNs), which require the risk factors, occurrence (O), severity (S) and detection (D), to be evaluated in crisp values. Second, the conventional RPN method has not considered the indirect relations between components of a system and is insufficient for complex systems with many subsystems or components. Therefore, a new risk assessment methodology combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) is proposed in this article to rank the risk of failures in system FMEA. The new method can address some of the inherent limitations of the traditional FMEA. Also, an application to the thin film transistor liquid crystal display (TFT-LCD) product is presented to demonstrate the proposed FMEA. By comparing with the listed approaches, the results show that the proposed method is a suitable and effective method for prioritisation of failures in system FMEA.
Developing competencies of employees on all hierarchy levels is a crucial prerequisite to enable fast problem solving and adaption to changing market conditions. Action-oriented learning approaches in learning factories show promising results, though a systematic approach for the design of learning factory courses and systems is missing. This article presents such a systematic approach for the competency-oriented development of learning factories integrating the conceptual design levels 'learning factory', 'teaching module' and 'learning situation'. The presented approach enables an effective competency development in learning factories by addressing problems of intuitively designed learning systems. As a result learning factories, teaching modules and single teaching-learning situations meeting industries' requirements can be realised with less effort and an increased success in applied competencies in real situations.
In the last decades, many researchers have studied open shop scheduling (OSS) problem by considering deterministic parameters using mathematical modelling, heuristics and meta-heuristics. However, it is important to study the problem as close as possible to real world conditions which consists of uncertainty and stochastic parameters. In this study, dispatching rules, as accepted tools for real-time scheduling, are applied for optimising the OSS problem. Since none of conventional dispatching rules performs well for all performance measures, a simulation-based real-time scheduling composite dispatching rule is developed. For this purpose, a multi response optimisation approach based on computer simulation for scheduling a non-preemptive open shop with stochastic ready times is presented in order to minimise the mean waiting time of jobs. The presented approach composed of design of experiments, discrete event simulation, multi-layer perceptron artificial neural network, radial basis function and data envelopment analysis to determine the most efficient dispatching rule for each machine.
Rising energy prices, increasing fierce competition, new environmental legislation and concerns over climate change are forcing energy-intensive manufacturing enterprises to increase production energy efficiency and reduce their associated environmental impacts. Thanks to the rapid developments of technologies in Internet of Things (IoT), the real-time status of resources and the data of energy consumption from manufacturing processes can be collected easily. These manufacturing information can provide an opportunity to enhance the energy efficiency in real-time production management. To achieve this target, this work presents a real-time energy efficiency optimisation method (REEOM) for energy-intensive manufacturing enterprises. By this method, IoT technologies are applied to sense the real-time primitive production data, including the energy consumption data and the resources status data. Multilevel event model and complex event processing are used to obtain real-time energy-related key performance indicators (e-KPIs) which extend production performance indicators to the energy efficiency area. Then, the non-dominant sorting genetic algorithm II is used to schedule or reschedule the production plan in an energy-efficient way based on real-time e-KPIs. Finally, a case is used to demonstrate the presented REEOM.
This article is a state-of-the-art review of the use of cryogenic cooling using liquefied gases in machining. The review is classified into two major categories, namely cryogenic processing and cryogenic machining. In cryogenic processing also known as cryo-processing, the cutting tool material is subjected to cryogenic temperatures as a part of its heat treatment process. The majority of the reported studies identify that cryo-processing can considerably increase cutting tool life especially for high speed steel tools. It also identified that, in cryogenic machining, a cryogen is used as a cooling substance during cutting operations. The cryogen can be used to freeze the workpiece material and/or cutting tool. This article concludes that cryogenic cooling has demonstrated significant improvements in machinability by changing the material properties of the cutting tool and/or workpiece material at the cutting zone, altering the coefficient of friction and reducing the cutting temperature.
In order to make the factory of the future vision a reality, various requirements need to be met. There is a need to continuously qualify the human worker about new and changing technology trends since the human is the most flexible entity in the production system. This demands introducing novel approaches for knowledge-delivery and skill transfer. This paper introduces the design, implementation and evaluation of an advanced virtual training system, which has been developed in the EU-FP7 project VISTRA. The domain of interest is automotive manufacturing since it is one of the leading industries in adopting future factory concepts and technologies such as cyber-physical systems and internet of things. First of all, the authors motivate the topic based on the state-of-the-art concerning training systems for manual assembly and relevant technologies. Then, the main challenges and research questions are presented followed by the design and implementation of the VISTRA project including its methodologies. Furthermore, the results of experimental and technical evaluation of the system are described and discussed. In the conclusion, the authors give an outlook at the implementation and evaluation of the example application in related industries.
Notwithstanding the widespread use and large number of advantages over traditional subtractive manufacturing techniques, the application of additive manufacturing technologies is currently limited by undesirable support material waste. Support structures are unavoidable when manufacturing objects with overhangs in extrusion-based Additive manufacturing, leading to extra build time and material waste. In a manufacturing process, different parameters such as cooling fan speed, print speed and print temperature can make a great contribution to printable overhang angle size. In this study, effects of these parameters on printable overhang angle size are studied theoretically and experimentally. First, theoretical analysis of printable overhang is conducted. Then experiments of overhangs with 20°, 30°, 40° and 50° are carried out with regard to different parameters on an FDM printer. According to the results, the printable threshold overhang angle varies substantially with printing conditions. The findings of this paper can then be applied for setting a lower threshold overhang angle for reducing support waste in the future. The findings can also provide some reference for future research in high-precision printing by adjusting relevant print parameters.
Automotive vehicle manufacturers have been at the forefront of employing radio frequency identification (RFID) technology for their manufacturing logistics management. They have benefited from RFID-enabled shop-floor visibility and traceability, which have in turn facilitated the implementation of advanced manufacturing strategies such as just-in-time lean manufacturing and mass customisation. Initial successes have attracted attention and interests from small- and medium-sized enterprises (SMEs) involved in automotive part and component manufacturers down the automotive vertical. However, high levels of capital investments and technical skills have created practical hurdles for automotive SMEs to gain RFID benefits. This article reports on an industrial case study about the RFID implementation project at a typical SME engine valve manufacturer. This company manufactures a large variety of engine valves with a mixture of large and small orders. Work-in-progress items across the company have accumulated to an extreme level for human operations and decisions. The company adopted RFID-enabled shop-floor manufacturing solutions across the whole operations with little experience in the use of information systems/technology. Based on RFID-enabled real-time shop-floor data, the company has extended the efforts in setting up and integrating manufacturing execution system and enterprise resource planning system. The success of this case company demonstrates that RFID is not just for automotive giants but also practically useful to SME suppliers. The article presents a framework that has been followed by this case company with a hope that experiences and insights are useful to other automotive SMEs.
In many real-life scheduling situations, the jobs deteriorate at a certain rate while waiting to be processed. This study introduces a new deterioration model where the actual processing time of a job depends not only on the starting time of the job but also on its scheduled position. The objective is to find the optimal schedule such that the makespan or total completion time is minimised. This study first shows that both problems are solvable in O(n log n) time. This study further shows that in both cases there exists an optimal schedule that is the shortest processing time, longest processing time, or V-shaped with respect to the job normal processing times, depending on the relationships between problem parameters.
In a radio frequency identification (RFID)-enabled real-time manufacturing environment, different decision makers are often confronted with the inconsistency between the highly synchronised information flow and unstandardised decision-making procedures, especially under conflicting objectives and dynamic situations. This study proposes an RFID-enabled real-time advanced production planning and scheduling shell (RAPShell, in short) to coordinate different decision makers across production processes. RAPShell has several key innovations. First, it uses RFID technology for enhancing information sharing, which provides the basis for coordinating decisions and operations of different parties involved in production planning, scheduling, execution and control. Second, it adopts Software as a Service (SaaS) model and a standard service-oriented architecture (SOA) with key modules, adaptive optimisation models, solution algorithms as well as scheduling rules developed and deployed as web services. Finally, extensible makeup language (XML)-based data models are utilised to achieve easy-to-deploy and simple-to-use system customisation and implementation. A case study demonstrates how RAPShell is customised and deployed in a small- and medium-sized company to facilitate the operations of typical production decision makers and operators. Benefits from qualitative and quantitative are discussed from the case study to show RAPShell's practical effectiveness and efficiency on production decision making.