Cloud manufacturing has emerged as a new manufacturing paradigm, which combines technologies (such as Internet of Things, Cloud computing, semantic Web, virtualisation and service-oriented technologies) with advanced manufacturing models, information and communication technologies. It aims to be networked, intelligent, service-oriented, knowledge-based and energy efficient, and promises a variety of benefits and advantages by providing fast, reliable and secure on-demand services for users. 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, will then be made accessible to presumptive consumers on a worldwide basis. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with future trends and research directions in Cloud manufacturing.
The increasing globalization is a trend which forces manufacturing industry of today to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing (CM) is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. Providing a framework for collaboration within complex and critical tasks, such as manufacturing and design, it increases the companies' ability to successfully compete on a global marketplace. One of the major, crucial objectives for CM is the coordinated planning, control and execution of discrete manufacturing operations in a collaborative and networked environment. This paper describes the overall concept of adaptive Function Block control of manufacturing equipment in Cloud environments, with the specific focus on robotic assembly operations, and presents Cloud Robotics as "Robot Control-as-a-Service" within CM.
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.
The interest for implementing the concept of Manufacturing-as-a-Service is increasing as concepts for letting the manufacturing shop-floor domain take advantage of the cloud appear. Combining technologies such as Internet of Things, Cloud Computing, Semantic Web, virtualisation and service-oriented technologies with advanced manufacturing models, information and communication technologies, Cloud Manufacturing is emerging as a new manufacturing paradigm. The ideas of on-demand, scalable and pay-for-usage resource-sharing in this concept will move manufacturing towards distributed and collaborative missions in volatile partnerships. This will require a control approach for distributed planning and execution of cooperating manufacturing activities. Without control based on both global and local environmental conditions, the advantages of Cloud Manufacturing will not be fulfilled. By utilising smart and distributable decision modules such as event-driven Function Blocks, run-time manufacturing operations in a. distributed environment may be adjusted to prevailing manufacturing conditions. Packaged in a cloud service for manufacturing equipment control, they will satisfy the control needs. By combining different resource types, such as hard, soft and capability resources, the cloud service Robot Control-as-a-Service can be realised. This paper describes the functional perspective and enabling technologies for a distributed control approach for robotic assembly tasks in Cloud Manufacturing.
The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemived virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment. In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system's integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.
The ability to adaptively control manufacturing equipment in cloud environments is becoming increasingly more important. Industry 4.0, supported by Cyber Physical Systems and the concept of on-demand, scalable and pay-for-usage resource-sharing in cloud environments offers many promises regarding effective and flexible manufacturing. For implementing the concept of manufacturing services in a cloud environment, a cloud control approach for the sharing and control of networked manufacturing resources is required. This paper presents a cloud service-based control approach which has a product perspective and builds on the combination of event-driven IEC 61499 Function Blocks and product manufacturing features. Distributed control is realised through the use of a networked control structure of such Function Blocks as decision modules, enabling an adaptive run-time behaviour. The control approach has been developed and implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. An application scenario is presented to demonstrate the applicability of the control approach. In this scenario, Assembly Feature-Function Blocks for adaptive control of robotic assembly tasks have been used.
A developing trend within the manufacturing shop-floor domain is the move of manufacturing activities into cloud environments, as scalable, on-demand and pay-per-usage cloud services. This will radically change traditional manufacturing, as borderless, distributed and collaborative manufacturing missions between volatile, best suited groups of partners will impose a multitude of advantages. The evolving Cloud Manufacturing (CM) paradigm will enable this new manufacturing concept, and on-going research has described many of its anticipated core virtues and enabling technologies. However, a major key enabling technology within CM which has not yet been fully addressed is the dynamic and distributed planning, control and execution of scattered and cooperating shop-floor equipment, completing joint manufacturing tasks. In this paper, the technological perspective for a cloud service-based control approach is described, and how it could be implemented. Existing manufacturing resources, such as soft, hard and capability resources, can be packaged as cloud services, and combined to create different levels of equipment or manufacturing control, ranging from low-level control of single machines or devices (e.g. Robot Control-as-a-Service), up to the execution of high level multi-process manufacturing tasks (e.g. Manufacturing-as-a-Service). A multi-layer control approach, featuring adaptive decision-making for both global and local environmental conditions, is proposed. This is realized through the use of a network of intelligent and distributable decision modules such as event-driven Function Blocks, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system's integration to the CM cloud service management functionality is also described.
Many manufacturing systems are exposed to a variety of unforeseen changes, negatively restricting their performances. External variations depending on market demand (e.g. changes in design, quantity and product mix) and internal variations in production capability and flexibility (e.g. equipment breakdowns, missing/worn/broken tools, delays and express orders) all contribute to an environment of uncertainty. In these dynamically changing environments, adaptability is a key feature for manufacturing systems to be able to perform at a maximum level, while keeping unscheduled downtime to a minimum. Targeting manufacturing equipment adaptability, this paper reports an assembly feature (AF) based approach for robotic assembly, using IEC 61499 compliant Function Blocks (FBs). Through the use of a network of event-driven FBs, an adaptive controller system for an industrial gantry robot’s assembly operations has been designed, implemented and tested. Basic assembly operations have been mapped as AFs into Assembly Feature Function Blocks (AF-FBs). Through their combination in FB networks, they can be aggregated to perform higher level assembly tasks. The AF-FBs dynamic execution and behavior can be adaptively controlled through embedded eventdriven algorithms, enabling the ability of adaptive decisions to handle unforeseen changes in the runtime environment.
Many manufacturing companies are facing an increasing amount of changes and uncertainty, caused by both internal and external factors. Frequently changing customer and market demands lead to variations in manufacturing quantities, product design and shorter product life-cycles, and variations in manufacturing capability and functionality contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Such events are difficult for traditional planning and control systems to satisfactorily manage. For scenarios like these, with a dynamically changing manufacturing environment, adaptive decision making is crucial for successfully performing manufacturing operations. Relying on real-time information of manufacturing processes and operations, and their enabling resources, adaptive decision making can be realized with a control approach combining IEC 61499 event-driven Function Blocks (FBs) with manufacturing features. These FBs are small decision-making modules with embedded algorithms designed to generate the desired equipment control code. When dynamically triggered by event inputs, parameter values in their data inputs are forwarded to the appropriate algorithms, which generate new events and data output as control instructions. The data inputs also include monitored real-time information which allows the dynamic creation of equipment control code adapted to the actual run-time conditions on the shop-floor. Manufacturing features build on the concept that a manufacturing task can be broken down into a sequence of minor basic operations, in this research assembly features (AFs). These features define atomic assembly operations, and by combining and implementing these in the event-driven FB embedded algorithms, automatic code generation is possible. A test case with a virtual robot assembly cell is presented, demonstrating the functionality of the proposed control approach.
Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite. In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative runtime manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.
The chapter presents a framework for establishing human-robot collaborative assembly in industrial environments. To achieve this, the chapter first reviews the subject state of the art and then addresses the challenges facing researchers. The chapter provides two examples of human-robot collaboration. The first is a scenario where a human is remotely connected to an industrial robot, and the second is where a human collaborates locally with a robot on a shop floor. The chapter focuses on the human-robot collaborative assembly of mechanical components, both on-site and remotely. It also addresses sustainability issues from the societal perspective. The main research objective is to develop safe and operator-friendly solutions for human-robot collaborative assembly within a dynamic factory environment. The presented framework is evaluated using defined scenarios of distant and local assembly operations when the experimental results show that the approach is capable of effectively performing human-robot collaborative assembly tasks.
This chapter reports a framework that can facilitate the interactions between a human's EEG (electroencephalography) signals and an industrial robot. This can be achieved by using an EEG headset that captures the brain signals of the human and send it via Bluetooth to a local workstation for signal processing, feature extraction and classification. The system developed provides the ability for a shop-floor operator to control the robot using own brain signals. The system can cooperate with other channels of communications (gesture, voice, etc.) to strengthen the collaboration between the human and the robot during shared assembly operations. Such a collaboration aims to fuse the high accuracy of the robot with the high versatility of the human. Therefore, the aim is to exploit the strength of both sides and enhance the quality and adaptability of human-robot collaborative assembly operations. This approach is applicable to other types of robots as well, for example ones used for assisting people with severe disability.
This paper introduces an intelligent system that can manipulate an industrial robot using the electroencephalogram signals of human brains to perform collaborative assembly tasks. The system is initiated by capturing the brain signals using a wearable headset, and the signals are then filtered to remove any possible artifact. Consequently, the process continues by identifying the brain signals patterns using a classifier based on pre-recorded samples. The classifier's output determines the proper matching of the robot command that is intended by the human. To validate the results, an industrial collaborative assembly scenario of a car manifold is examined as a case study.
Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.
This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective.
In this paper, general requirements of next generation manufacturing systems are discussed, and the strategies to meet these requirements are considered. The production paradigms which apply these strategies are also classified. Particular emphasis is put on the paradigm of Reconfigurable Manufacturing System (RMS). Some key issues of the RMS design are discussed, and a critical review is presented concerning the developments of RMSs. Finally, suggestions of the RMS research are made and future research directions are identified.
The objective of collaborative manufacturing is to create the synergism from the collaboration of manufacturing resources. Most of the studied collaborations are made among intelligent machines; however, the collaboration can be realized even between machines and human being, and a collaborative robot (Cobot) belongs to the latter. A cobot is a robot designed to assist human beings as a guide or assistor in a constrained motion. Various prototypes have been developed and the potentials of these robots have been demonstrated. The research presented in this paper focuses on the control and simulation models of a tricycle cobot with three steered wheels, with the following two contributions: (i) A concise model for the closed-loop control is developed. Existing closed-loop control has been implemented in an intuitive way, and some control parameters have to be determined by a trial-and-error method. (ii) A simulation model is proposed to validate the control algorithms. No simulation model is available and the control models of other existing systems have to be validated experimentally. The developed control and simulation models have been implemented. Graphic simulation is also developed. Case studies are provided and the simulation results are analyzed.
Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans' safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.
A system paradigm is an abstract representation of the system; it is thesystem architecture that determines the types and numbers of the components andtheir relations in operation and interaction of the system. Its selection relies oncustomers’ requirements and manufacturing environment. Many system paradigmshave been proposed. However, most of them are based on an assumption that thelife-cycle and boundary of a system can be defined based on the customers’requirements. Since sustainability becomes essential to today’s manufacturingsystems, a new concern is how to evolve existing paradigms to meet new chal-lenges. The objectives of this chapter are, therefore, to examine the manufacturingrequirements in a wider scope, to revisit existing paradigms to clarify the limi-tations and bottlenecks, and eventually to identify future research directionstowards sustainable manufacturing. Within the context, this chapter focuses moreon Reconfigurable and Cloud manufacturing system paradigms, and highlights thefuture endeavors towards better sustainability.
One of the primary objectives of sustainable manufacturing is to minimize energy consumption in its manufacturing processes. A strategy of energy saving is to adapt new materials or new processes; but its implementation requires radical changes of the manufacturing system and usually a heavy initial investment. The other strategy is to optimize existing manufacturing processes from the perspective of energy saving. However, an explicit relational model between machining parameters and energy cost is required: while most of the works in this field treat the manufacturing processes as black or gray boxes. In this paper, analytical energy modeling for the explicit relations of machining parameters and energy consumption is investigated, and the modeling method is based on the kinematic and dynamic behaviors of chosen machine tools. The developed model is applied to optimize the machine setup for energy saving. A new parallel kinematic machine Exechon is used to demonstrate the procedure of energy modeling. The simulation results indicate that the optimization can result in 67% energy saving for the specific drilling operation of the given machine tool. This approach can be extended and applied to other machines to establish their energy models for sustainable manufacturing
Today's manufacturing environment forces manufacturing companies to make as many product variations as possible at affordable costs within a short time. Mass customisation is one of most important technologies for companies to achieve their objectives. Efforts to mass customisation should be made on two aspects: (1) To modularize products and make them as less differences as possible; (2) To design manufacturing resources and make them provide as many processes variations as possible. This paper reports our recent work on aspect (2), i.e. how to design a reconfigurable manufacturing system (RMS) so that it can be competent to accomplish various processes optimally; Reconfigurable robot system (RRS) is taken as an example. RMS design involves architecture design and configuration design, and configuration design is further divided in design analysis and design synthesis. Axiomatic design theory (ADT) is applied to architecture design, the features and issues of RRS configuration design are discussed, automatic modelling method is developed for design analysis, and concurrent design methodology is presented for design synthesis.
In this paper, energy models are developed based on the kinematic and dynamic behaviors of chosen machine tools. One significant benefit of the developed energy models is their inherited relationship to the design variables involved in the manufacturing processes. Therefore, they can be readily applied to optimize process parameters to reduce energy consumption. A new parallel kinematic machine Exechon is used as a case study to demonstrate the procedures of energy model development with direct relation to appropriate process parameters. The derived energy model is then used for simulation of drilling operations on aircraft components to demonstrate its feasibility. Simulation results indicate that the developed energy model has led to an optimized machine setup which only consumes less than one-third of the energy of an average machine setup over the workspace. This approach can be extended and applied to other machines to establish their energy models for green manufacturing.
A critical task of vision-based manufacturing applications is to generate a virtual representation of a physical object from a dataset of point clouds. Its success relies on reliable algorithms and tools. Many effective technologies have been developed to solve various problems involved in dataacquisition and processing. Some articles are available on evaluating and reviewing these technologies and underlying methodologies. However, for most practitioners who lack a strong background on mathematics and computer science, it is hard to understand theoretical fundamentals of the methodologies. In this paper, we intend to survey and evaluate recent advances in data acquisition and progressing, and provide an overview from a manufacturing perspective. Some potential manufacturing applications have been introduced, the technical gaps between the practical requirements and existing technologies discussed, and research opportunities identified.
Collaborative robots (Cobots) have been proposed to guide and assist human operators to move heavy objects in a given trajectory. Most of the existing cobots us steering wheels; typical drawbacks of using steering wheels include (i) the difficulty to follow a trajectory with a curvature larger than that of the base platform, (ii) the difficulty to mount encoders on steering wheels due to self-spinning of the wheels, and (iii) the difficulty to quarantine dynamic control performance since it is purely kinematic control. In this paper, a new cobot with the omni-wheels has been proposed, and its design model has been developed, and a simulation has been conducted to validate this control performance.
In this paper, a new collaborative robot with omni-wheels has been proposed and its dynamic control has been developed and validated. Collaborative robots (Cobots) have been introduced to guide and assist human operators to move heavy objects in a given trajectory. Most of the existing cobots use steering wheels: typical drawbacks of using steering wheels include the difficulties to (i) follow a trajectory with a curvature larger than that of the base platform, (ii) mount encoders on steering wheels due to self-spinning of the wheels, and (iii) quarantine dynamic control performance since it is purely kinematic control. The new collaborative robot is proposed to overcome the above-mentioned shortcomings. The methodologies for its dynamic control are focused and the simulation has been conducted to validate the control performance of the system.
In this paper, a new energy model is developed based on the kinematic and dynamic behaviors of a chosen machine tool. One significant benefit of the developed energy model is their inherited relationship to the design variables involved in the manufacturing processes. Without radical changes of the machine tool's structure, the proposed model can be readily applied to optimize process parameters to reduce energy consumption. A new parallel kinematic machine Exechon is used as a case study to demonstrate the modeling procedure. The derived energy model is then used for simulation of drilling operations on aircraft components to verify its feasibility. Simulation results indicate that the developed energy model has led to an optimized machine setup which only consumes less than one-third of the energy of an average machine setup over the workspace. This approach can be extended and applied to other machines to establish their energy models for green and sustainable manufacturing.
Robotics has brought radical changes to maximise the productivity of modern manufacturing. However, a full automation is not always advantageous; sometimes robots and human being must work together in a shared environment to meet specific requirements. A robot used in a collaborative environment is a collaborative robot. In this paper, a collaborative robot to assist human being’s locomotion is considered: omni-wheels are used to increase the flexibility and mobility of the robot and they are controlled dynamically to confine the robot in a prescribed trajectory. The new control algorithms are developed to meet the following challenges (a) unpredictable driving force from a human operator; (b) the rotation of an omni-wheel along two axes but with one independent motion; and (c) the strongly-coupled kinematics and dynamics of the mobile robot.
A reconfigurable machining system is usually a modularized system, and its configuration design concerns the selections of modules and the determination of geometric dimensions in some specific modules. All of its design perspectives from kinematics, dynamics, and control have to be taken into considerations simultaneously, and a multidisciplinary design optimization (MDO) tool is required to support the configuration design process. This paper presents a new MDO tool for reconfigurable machining systems, and it includes the following works: (i) the literatures on the computer-aided design of reconfigurable parallel machining systems have been reviewed with a conclusion that the multidisciplinary design optimization is essential, but no comprehensive design tool is available to reconfigurable parallel machining systems; (ii) a class of reconfigurable systems called reconfigurable tripod-based machining system has been introduced, its reconfiguration problem is identified, and the corresponding design criteria have been discussed; (iii) design analysis in all of the disciplines including kinematics, dynamics, and control have been taken into considerations, and design models have been developed to evaluate various design candidates; in particular, the innovative solutions to direct kinematics, stiffness analysis for the design configurations of tripod-based machines with a passive leg, and concise dynamic modelling have been provided; and (iv) A design optimization approach is proposed to determine the best solution from all possible configurations. Based on the works presented in this paper, a computer-aided design and control tool have been implemented to support the system reconfiguration design and control processes. Some issues relevant to the practical implementation have also been discussed.
Reconfigurable Manufacturing System (RMS) is one of most promising paradigms that provide an effective solution to changes and uncertainties in a competitive manufacturing environment. A Reconfigurable Assembly System (RAS) is a key component of an RMS. In this paper, our survey on the development of RAS has been summarised. The objectives of this literature survey are to: clarify the needs and drivers in developing reconfigurable assembly systems identify both academic and practical issues critical to the development of reconfigurable assembly systems understand the state of the art of R&D related to the studies on the critical issues reveal the future research directions, which are mostly beneficial to manufacturing industries.
The open architecture computer numerical control (OACNC) system meets the individualized demand of modern industry for its characteristics of flexibility, adaptability, versatility, and expansibility. Existing OACNC systems depend on specialized software which reduces the openness of the OACNC system. This paper introduces a new OACNC system based on a soft-integrated communication module. The module improves the data exchange principle in communication shared memory and is built by open API. Therefore the OACNC system can be compatible with different communication protocols between periodic and aperiodic. Then a case is proposed to test the compatibility and extensibility of the system. Finally, the prospect of the OACNC system and future research is discussed.
With the growing development of cloud manufacturing (CM) applications in industry, one concern potential users have is security of the data, remote machines and operators. Potential security risks in communication with remotely operated manufacturing equipment have been of recent interest. The purpose of this paper is to provide an overview of security measures being considered to ensure the protection of data being sent to physical machines in a CM system. Topics covered include: internet of things, remote equipment control, security concerns in remote equipment control, existing proposed security measures for remote equipment control, and the future outlook of remote equipment control and security in CM systems.
Manufacturing technology changes with the needs of consumers. The globalization of the world economy has helped to create the concept of cloud manufacturing (CM). The purpose of this paper is to provide both an overview and an update on the status of CM and define the key technologies that are being developed to make CM a dependable configuration in today's manufacturing industry. Topics covered include: cloud computing (CC), the role of small and medium enterprises (SMEs), pay-as-you-go, resource virtualization, interoperability, security, equipment control, and the future outlook of the development of CM.
Setup planning for machining a part is to determine the number and sequence of setups (including machining features grouping in setups) and the part orientation of each setup. Tool accessibility plays a key role in this process. An adaptive setup planning approach for different types of multi-axis machine tools is proposed in this paper by investigating Tool Access Directions (TADs) of machining features, Tool Orientation Spaces (TOSs) of machine tools, and Primary Locating Directions (PLDs) of workpieces. In our approach, feasible TADs of a machining feature are predefined based on feature geometry and best practice knowledge, and denoted by unit vectors; The TOS of a machine tool is generated according to its configuration through kinematic analysis, and represented by a unit spherical surface patch; Primary locating directions and their priorities of a workpiece are determined based on the surface areas and the surface accuracy grades of non-machining surfaces. Starting from a 3-axis based machining feature grouping, setups for a 3-, 4- (or 3-axis with indexing table), or 5-axis machine can be achieved effectively by tool accessibility examination. A so-generated setup plan can provide not only the best coverage of machining features but the optimal orientation for each setup. Prismatic parts are considered in the proof-of-concept phase. Algorithms introduced here are implemented in MATLAB, and a case study is used to show the results.
Setup planning for machining a part is to determine the number and sequence of setups (including machining features grouping in setups) and the part orientation of each setup. Tool accessibility plays a key role in this process. An adaptive setup planning approach for various multi-axis machine tools is proposed in this paper focusing on kinematic analysis of tool accessibility and optimal setup plan selection. In our approach, feasible Tool Access Directions (TADs) of machining features are denoted by partially sequenced unit vectors; The Tool Orientation Spaces (TOS) of different multi-axis machine tools are generated according to their configurations through a kinematic model, and represented on a unit spherical surface. Starting from a 3-axis-based machining feature grouping, all possible setup plans of a given part for different types of machine tools (3-axis, 3-axis with an indexing table, 4-axis, and 5-axis machines) can be achieved effectively by tool accessibility examination. The optimal setup plans are selected from obtained candidates by evaluating both their locating and grouping factors. A so-generated setup plan can provide not only the best coverage of machining features and the primary locating directions but the optimal orientations of the work-piece for each setup. Only prismatic parts are considered in this proof-of-concept study, and the algorithms introduced in this paper are implemented in MATLAB. A case study is conducted to validate the algorithms.
Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setupplanning.
In a machine shop, setup planning of a part for more than one available machine is a typical combinatorial optimization problem under certain constraints. Targeting adaptability of setup plans for multiple machines, an across-machine setup planning approach using genetic algorithms is presented in this paper, including (1) gene pool generation, (2) setup plan encoding and fitness evaluation, and (3) optimal setup plan selection through GA operations. The proposed approach is implemented In a GA toolbox, and tested by a sample part. The results demonstrate that this approach is not only applicable to machines with varying configurations, but also adaptive to shop floor schedule changes due to uncertainty.