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Reshaping the Landscape of the Future: Software-Defined Manufacturing
Abstract
We describe the concept of software-defined manufacturing, which divides the manufacturing ecosystem into software definition and physical manufacturing layers. Software-defined manufacturing allows better resource sharing and collaboration, and it has the potential to transform the existing manufacturing sector.
... However, the versatility, adaptability, and efficiency must not only be fulfilled on the level of the manufacturing system, but rather directly on the level of the machine tool. Such a machine tool is for example considered one of the key enablers for SDM [4] and referred to as a 'universal manufacturing node' in [4]. ...
... However, the versatility, adaptability, and efficiency must not only be fulfilled on the level of the manufacturing system, but rather directly on the level of the machine tool. Such a machine tool is for example considered one of the key enablers for SDM [4] and referred to as a 'universal manufacturing node' in [4]. ...
The current trend in the context of Industry 4.0 towards small batch sizes and increasing product variety results in ever-changing requirements for both, the products and the production. This requires highly versatile, fully and easily adaptable, and efficient manufacturing environments that can meet these demands, ideally already on the level of the machine tool. Because of its versatility, the laser is a promising tool for such a machine tool, but there is still a considerable need for research in the field of system technology.
We consider the requirements for a versatile, laser-based machine tool for highly adaptable manufacturing, that utilizes the combination of laser-based manufacturing processes on one machine. The focus of the considerations lies on remote processes and the processing of metals. Five key research topics for the development of such a universal laser manufacturing node are identified: highly dynamic and precise kinematics (1); ‘on-the-fly’ reconfigurable, distributed control architectures (2); adaptable process diagnostics for online quality monitoring (3); technological interactions in laser-based process chains (4); and models for a fast estimation of the process parameters for each production step (5). The relevance and current needs for research for each topic are discussed and corresponding solution concepts are proposed.
... Because of its versatility, it is also well suited for use in highly versatile and efficient manufacturing systems, 1,2 which are becoming more and more important and are of high interest, especially for small batch-size manufacturing. [2][3][4][5][6] A fast and efficient development of new laser processes is important for such highly versatile manufacturing systems. In this context, a model-based prediction of process constraints plays an essential role in finding reliable process parameters with reasonable effort, and physics-informed machine learning (ML), 7-11 the combination of ML and physics, is a promising approach for this task. ...
The quantitative prediction of process constraints, such as the threshold of deep-penetration laser welding, plays a crucial role for the fast and reliable development of robust process windows for laser manufacturing processes. A physics-informed hybrid model with additional output constraints for the prediction of the threshold of deep-penetration laser welding is presented. A "residual model" approach is used, where a machine learning model, employing Gaussian processes, is used to model and compensate for the deviations between experiments and a physical model, and output warping is used to incorporate additional output constraints into the model. The main benefits that result from applying such a model are found to be (1) an increased prediction accuracy compared to only using the physical model, leading to a reduction of the mean relative error of about 76%; (2) a reduction of the number of required training data compared to only using a black-box machine learning model; (3) an increased prediction accuracy compared to only using a black-box machine learning model; (4) and an increased compliance with physical boundary conditions by applying the additional output constraints.
... The manufacturing domain is also adopting this concept to provide future manufacturing services, such as human-centered manufacturing and AIoT-based sustainable manufacturing, which require high flexibility and interoperability [7,8,9]. By applying this concept, advanced factories can intelligently reconfigure the manufacturing functions of robots with software to create manufacturing systems that support the diverse requirements of the operational engineers [10]. For example, if the factory requirements change, the software can redefine the facility's functions based on product characteristics and configure the optimal production line to enable quick response and efficient production. ...
Advanced factories strongly need autonomous control methods of manufacturing robots flexibly responding to various requirements of the operational engineers. Deep reinforcement learning is more promising technology to support the dynamic factory situations than the legacy static robot control technologies are. The machine learning model experiences the computing resource limitation of industrial robots working the complex jobs of intelligent operations including machine vision, action planning and human collaborations. Policy distillation is a kind of model compression schemes of deep reinforcement learning by means of teacher-student model which make a pre-trained teacher model transfer its knowledge to structurally simplified student models in order to enhance the computing efficiency. However, it may have some problems of anomalous knowledge and abnormal robot movements in case the teacher model has a local optimal policy. In this paper, we propose a novel policy distillation with win probability added (WPA) based knowledge filtering algorithm for efficient industrial robot control. The proposed mechanism adopts the WPA method in sports analytics to evaluate the knowledge extracted from the teacher model and to filter bad knowledge out. The filtered knowledge is reconstructed with interpolation to train the student model with high-quality data. We perform the well-designed experiments, which show 11% compression enhancement and 5% reduction in execution time and steps required for the tasks, using a robot arm and a UGV as test environments.
... The laser is already well established as a manufacturing tool in many serial production processes. Since the laser is a very versatile tool [1], it is also well suited for the use in highly versatile and efficient manufacturing systems [1,2] which are becoming more and more important, especially for small batchsize manufacturing [2][3][4][5][6]. The model-based prediction of process constraints is essential to find reliable process parameters with reasonable effort. ...
Physics-informed hybrid models, the combination of physics and machine learning, have already shown considerable benefits for quantitative predictions of process constraints, such as the threshold of deep-penetration laser welding. However, despite the improved prediction accuracy and extrapolation capability of such models, there can still be cases where the predictions of the model, including the confidence region, result in values that are not consistent with physical boundary conditions. Therefore, this paper presents the application of additional output constraints to a physics-informed hybrid model to further improve the compliance of the model with physics. Gaussian processes are used for the machine learning model and output warping is used to incorporate the output constraints directly into the model. The approach is demonstrated at the example of a hybrid model for the prediction of the threshold of deep-penetration laser welding.
... Die im Zuge der Entwicklung und Implementierung benötigten Ressourcen (und damit verbundenen Kosten) konzentrieren sich auf das Entwicklerteam. Analog [14]. Es ergibt sich hier insbesondere die Möglichkeit des Value-Based Pricing. ...
Das technische Know-How zur Entwicklung und Implementierung eigener KI-Lösungen hat Einzug in Unternehmen der Produktionstechnik gefunden. Eine Herausforderung besteht jedoch fort: Es mangelt an erprobten Geschäftsmodellen, die jenen durch die KI-Applikation generierten Mehrwert an ihre Anbieter weitergeben. In diesem Zusammenhang ist es entscheidend Strategien zu entwickeln, die den KI-Einsatz nicht nur technisch, sondern auch wirtschaftlich und organisatorisch optimieren.
... Software-defined manufacturing is an approach that enables flexible and reconfigurable systems and is therefore able to handle these challenges [4]. The successful implementation of software-defined manufacturing requires production systems that are as flexible and universal as possible [5] and that are sufficiently defined via software so that they can flexibly adapt to changing specifications [6,7]. Laser-based directed energy deposition with metal powder (DED-LB/M) offers such a flexible process, as it can be used for coating, welding, repairing and additive manufacturing without major change in hardware [8][9][10]. ...
Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases.
... In SDx the software is solely decisive for the configuration of system functionality. SDM is a concept derived from SDx. SDM enables a separation of the manufacturing ecosystem into software definition layers and physical manufacturing layers, which allows full flexibility of production through definition via software [4], [5]. Digital twins and modelbased systems engineering are indispensable technologies for SDM-systems. ...
In modern and complex production systems, the focus is shifted toward the software part. Software-Defined Manufacturing (SDM) and Cyber-Physical Production Systems (CPPS) characterize this trend. SDM and CPPS enable the concept of adaptive, flexible, and self-configuring production systems. These software-intensive robotic systems are safety- critical because they usually are applied in the same environ- ments as human workers. Therefore they require a continuous risk assessment. The uploading of a new software to the system can change its behavior drastically and therefore, the risk assessment needs to be redone. Key enabling technologies are digital twins, advanced and hybrid risk models, and Model-to- Model (M2M) transformation methods.
In this paper, we introduce a new approach to the automated and continuous risk assessment based on Robot Operating System (ROS) code of a software-defined robotic system. The approach pipelines four key elements: (i) a logger that logs the data of the digital twin, (ii) an adder algorithm that creates risk annotated code based on the given ROS code, the output of the logger, and the hardware description including risk data of robot parts, (iii) an M2M transformation algorithm that automatically generates hybrid risk models from risk-annotated code, and (iv) OpenPRA solvers for numerical evaluation of the generated hybrid risk models.
... Most of the above studies discuss edge decision-making in manufacturing scenarios from the perspective of industrial control collaboration rather than knowledge modeling. This paper aims to support the sensing and decision-making of abnormal events in manufacturing scenarios using IT-level knowledge modeling [15]. Some relatively mature model specifications have been proposed in industrial manufacturing, such as packML [16], [17], which has been promoted by many industrial automation companies serving the packaging, as well as InstrumentML, AutomationML [18], and OPC UA [19]. ...
... Software-defined manufacturing is an approach that enables flexible and reconfigurable systems and is therefore able to handle these challenges [4]. The successful implementation of software-defined manufacturing requires production systems that are as flexible and universal as possible [5] and that are sufficiently defined via software so that they can flexibly adapt to changing specifications [6,7]. Laser-based directed energy deposition with metal powder (DED-LB/M) offers such a flexible process, as it can be used for coating, welding, repairing and additive manufacturing without major change in hardware [8][9][10]. ...
... Most modern critical infrastructures heavily rely on ICTs (information and communication technologies) to support their daily operations and provide uninterrupted services [13,29]. The underlying ICT system can greatly improve the efficiency of a critical infrastructure, e.g., collecting information using IoT devices to monitor the status of major components and automating the operation in a precise manner. ...
Trusted execution environment (TEE) technology has found many applications in mitigating various security risks in an efficient manner, which is attractive for critical infrastructure protection. First, the natural of critical infrastructure requires it to be well protected from various cyber attacks. Second, performance is usually important for critical infrastructure and it cannot afford an expensive protection mechanism. While a large number of TEE-based critical infrastructure protection systems have been proposed to address various security challenges (e.g., secure sensing and reliable control), most existing works ignore one important feature, i.e., devices comprised the critical infrastructure may be equipped with multiple incompatible TEE technologies and belongs to different owners. This feature makes it hard for these devices to establish mutual trust and form a unified TEE environment. To address these challenges and fully unleash the potential of TEE technology for critical infrastructure protection, we propose DHTee, a decentralized coordination mechanism. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially the attestation service. A Device equipped with one TEE can interact securely with the blockchain to verify whether another potential collaborating device claiming to have a different TEE meets the security requirements. DHTee is also flexible and can support new TEE schemes without affecting devices using existing TEEs that have been supported by the system.
... Based on the description of the product to be manufactured, the whole production software, including machine control software, embedded software, cloud services, and part programs, can be automatically generated, instantiated, and configured. Key aspects for SDM are Model-Based Systems Engineering (MBSE) in production environments and digital twins of the cyber-physical systems, available at all times during the engineering process and operation [1,2]. ...
... Based on the description of the product to be manufactured, the whole production software, including machine control software, embedded software, and cloud services can be automatically created. Key aspects of SDM are Model-Based Systems Engineering (MBSE) and the virtual representation (Digital Twin) of the physical systems, available at all times during the engineering process and operation [1], [2]. ...
... Successful handling of these challenges requires flexible and reconfigurable manufacturing systems [3,4]. This flexibility is expected to be manageable, if manufacturing systems are both universal and can be fully controlled by software, enabling what is commonly referred to as software-defined manufacturing [5]. ...
Flexible manufacturing processes such as laser metal deposition exhibit high potential for a production solely defined by software to cope with the current challenges of production systems. The determination of suitable machine parameters for the production of novel materials and geometries however requires extensive experimental effort. Existing simulative approaches do not offer sufficient accuracy to predict the relevant machine parameters in a satisfactory way. This paper presents a new concept, in which we apply a digital twin to provide a step towards a fully software-defined and predictable laser metal deposition process. The presented concept includes relevant data of the machines as well as data-driven machine learning models and physics-based simulation models. This enables a more reliable prediction of geometries of single tracks which was validated on a laser metal deposition machine.
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Abstract
Thank you for taking the time to read this textbook on Additive Manufacturing
(AM). We hope you benefit from the time and effort it has taken putting it together
and that you think it was a worthwhile undertaking. It all started as a discussion at a
conference in Portugal when we realized we were putting together books with similar
aims and objectives. Since we are friends as well as colleagues, it seemed sensible
we join forces rather than compete; sharing the load and playing to each other’s
strengths undoubtedly means a better all-round effort and result.
We wrote this textbook because we have all been working in the field of AM for
many years. Although none of us like to be called “old,” we do seem to have around
85 years of experience, collectively, and have each established reputations as educators
and researchers in this field. We have each seen the technologies described in
this textbook take shape and develop into serious commercial tools, with hundreds
of thousands of regular users and many millions of parts being made by AM
machines each year. AM is now being incorporated into curricula in many schools,
polytechnics, and universities. More and more students are becoming aware of these
technologies and yet, as we saw it, there was no single text adequate for such curricula.
We believe that the first edition provided such a text, and based upon updated
information, the 2nd and 3rd editions were developed.
Since the advent of 3D printing in the mid-1980s, additive manufacturing has grown steadily and found numerous applications across all types of industries. More recently, the industry has seen a spur of growth as the terms of the original patents expired and new companies entered the market. While there exist several different methods of additive manufacturing, polymer-based material extrusion 3D printing (also known as fused filament fabrication) has become one of the most widely used ones due to its lower cost, ease of use, and versatility. While development has greatly expanded the material availability and improved the quality of prints, material extrusion 3D printers have often faced a challenge in physical scaling. There are inherent design hurdles to the extrusion process when the print starts to grow larger. This paper aims to study the market landscape of extrusion-based 3D printing technology for polymer-based material as well as challenges faced in upscaling this technology for industrial applications. A prototype large-scale material extrusion 3D printer has been designed, constructed, and then tested to gain experimental data on large-scale 3D printing using thermoplastic polymers as a printing material. Results of testing and experimentation verified certain key design elements and how they can improve large-scale 3D printing. Testing also revealed how large diameter nozzles for the hot end introduce challenges not seen in small-scale 3D printers. This paper also seeks to consolidate available information pertaining to large-scale 3D printing into one comprehensive document.
Cellulose is the most abundant and broadly distributed organic compound and industrial by-product on Earth. However, despite decades of extensive research, the bottom-up use of cellulose to fabricate 3D objects is still plagued with problems that restrict its practical applications: derivatives with vast polluting effects, use in combination with plastics, lack of scalability and high production cost. Here we demonstrate the general use of cellulose to manufacture large 3D objects. Our approach diverges from the common association of cellulose with green plants and it is inspired by the wall of the fungus-like oomycetes, which is reproduced introducing small amounts of chitin between cellulose fibers. The resulting fungal-like adhesive material(s) (FLAM) are strong, lightweight and inexpensive, and can be molded or processed using woodworking techniques. We believe this first large-scale additive manufacture with ubiquitous biological polymers will be the catalyst for the transition to environmentally benign and circular manufacturing models.
After reviewing the development of industrial manufacturing, a novel concept called social manufacturing U+0028 SM U+0029 and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer U+02BC s requirements of U+201C from mind to products U+201D, and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend, and is expected to become popular in more and more industries. First, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies, like Internet of Things U+201C IoT U+201C, social networks, cloud computing, 3D printing, and intelligent systems, are introduced and analyzed, and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3D printing are suggested for different user cases.
The current globalization is faced by the challenge to meet the continuously growing worldwide demand for capital and consumer goods by simultaneously ensuring a sustainable evolvement of human existence in its social, environmental and economic dimensions. In order to cope with this challenge, industrial value creation must be geared towards sustainability. Currently, the industrial value creation in the early industrialized countries is shaped by the development towards the fourth stage of industrialization, the so-called Industry 4.0. This development provides immense opportunities for the realization of sustainable manufacturing. This paper will present a state of the art review of Industry 4.0 based on recent developments in research and practice. Subsequently, an overview of different opportunities for sustainable manufacturing in Industry 4.0 will be presented. A use case for the retrofitting of manufacturing equipment as a specific opportunity for sustainable manufacturing in Industry 4.0 will be exemplarily outlined.
In an increasingly competitive globalised manufacturing environment, the necessity to develop new products and introduce innovative enhancements to existing ranges has created a critical need for the increased utilisation and sharing of organisational and employee knowledge. The capture of this knowledge within industry is of paramount importance as organisations seek to survive and remain competitive. Crowdsourcing, as a collaborative idea generation and problem solving activity, allows employees to capture explicit knowledge from large groups of colleagues and teams, and offers the potential to extract previously unknown tacit knowledge in a less formal virtual environment. This paper provides a review of recently published literature relating to crowdsourcing in the manufacturing industry and offers suggestions for the future direction of crowdsourcing research in manufacturing and product development.
This paper describes a flexible approach to manage autonomically cloud resource isolation between different layers of an IaaS infrastructure, reconciling computing and network views. The corresponding framework overcomes fragmentation of security components and automates their administration by orchestrating different autonomic loops, vertically (between layers) and horizontally (between views).
N-fold integer programming is a fundamental problem with a variety of natural
applications in operations research and statistics. Moreover, it is universal
and provides a new, variable-dimension, parametrization of all of integer
programming. The fastest algorithm for n-fold integer programming predating
the present article runs in time with L the binary length of
the numerical part of the input and g(A) the so-called Graver complexity of
the bimatrix A defining the system. In this article we provide a drastic
improvement and establish an algorithm which runs in time having
cubic dependency on n regardless of the bimatrix A. Our algorithm can be
extended to separable convex piecewise affine objectives as well, and also to
systems defined by bimatrices with variable entries. Moreover, it can be used
to define a hierarchy of approximations for any integer programming problem.
This article presents a case study of the ?roll-out? of a "world car" (the Fiat Palio). Based on original fieldwork carried on by the author in 6 countries (Italy, Brazil, Poland, Turkey, Argentina, India), it describes one of the most diverse international strategies in the recent history of the auto industry and represents an interesting terrain for analyzing how, in relationship with globalization, outsourcing and modularity play an increasing role in auto design and manufacturing. The article addresses the following research questions: 1) Does the "world car" approach represent a sustainable and robust strategy? 2) Is there a relationship between globalization, modularization and outsourcing in the auto industry? 3) Can these concepts be used to map out future developments and transformations in the contracting structure of the auto industry? This field study shows that producing and selling in many different places a car that involves absolute cross-country identity of interior/exterior design, parts, and quality standards (a "world car") represents an innovative and sustainable strategy. It also highlights that the robustness of this strategy decreases as the international scope and time span of the ?global? project increase. The Fiat Palio story also represents the first in depth analysis of what are, at the firm level, the dynamics that link globalization, outsourcing and modularization in the auto industry. The article confirms that modularization a) is a vaguely defined and ambiguously used term in the auto industry; b) is a broad concept, applicable and applied to a number of systems (product design, manufacturing, work organization, etc.); c) has only recently moved its first steps in auto design and manufacturing. The embryonic applications of modularity in design, manufacturing and organization reported in this study are used to map out future developments and transformations in the product architectures and organizational architectures of the auto industry. The article also suggests that, within a global strategy, modularization and outsourcing, though remaining conceptually distinct, tend to become, in practice, increasingly inseparable. The modularization of design, production and organization is intimately related to how, while trying to save costs, reduce risky investment, and manage the institutional constraints deriving from globalization, OEMs and suppliers partition their tasks, defining a new international division of labor.
In smart industrial systems, in many cases, a fault can be captured as an event to represent the distinct nature of subsequent changes. Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis. Most event-based diagnosis techniques rely on perfect observations of observable events. However, in practice, it is common to miss an observable event due to a problem in sensor-readings or communication / transmission channels. This paper develops a fault diagnosis tool, referred to as diagnoser, which can robustly detect, locate, and isolate occurred faults. The developed diagnoser is resilient against missed observations. A missed observation is detected from its successive sequence of events. Upon detecting a missed observation, the developed diagnoser automatically resets and then, asynchronously resumes the diagnosis process. This is achieved solely based on post-reset / activation observations and without interrupting the performance of the system under diagnosis. New concepts of asynchronous detectability and asynchronous diagnosability are introduced. It is shown that if asynchronous detectability and asynchronous diagnosability hold, the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations. The proposed technique is applied to diagnose faults in a manufacturing process. Illustrative examples are provided to explain the details of the proposed algorithm. The result paves the way towards fostering resilient cyber-physical systems in Industry 4.0 context.
Welcome to the fifth issue of the IEEE Transactions on Computational Social Systems (TCSS) this year. Seventeen regular articles and a brief discussion on social manufacturing (SM) are presented here. In addition, a special issue on “Human-Centric Cyber Social Computing” is included.We would like to take the opportunity to thank the Guest Editors for their time and effort devoted to the special issue.
Software-Defined Infrastructure (SDI) presents an approach for integrated management of virtualized heterogeneous resources. Monitoring and measurement is an essential component for effective control and management. This paper presents an architecture of a system, named MonArch, based on SDI that provides integrated monitoring and measurement functionalities. Unlike existing cloud and network monitoring systems, MonArch supports execution of user-generated monitoring tasks, offers monitoring as a service to tenants, administrators as well as management modules, and provides a framework for monitoring data analytics. We have implemented and deployed MonArch in the SAVI Testbed, and our experience shows the system is able to support a wide variety of monitoring tasks while achieving high performance and scalability.
Most AM processes require post-processing after part building to prepare the part for its intended form, fit and/or function. Depending upon the AM technique, the reason for post-processing varies. For purposes of simplicity, this chapter will focus on post-processing techniques which are used to enhance components or overcome AM limitations. These include:
In an increasingly competitive globalised manufacturing environment, the necessity to develop new products and introduce innovative enhancements to existing ranges has created a critical need for the increased utilisation and sharing of organisational and employee knowledge. The capture of this knowledge within industry is of paramount importance as organisations seek to survive and remain competitive. Crowdsourcing, as a collaborative idea generation and problem solving activity, allows employees to capture explicit knowledge from large groups of colleagues and teams, and offers the potential to extract previously unknown tacit knowledge in a less formal virtual environment. This paper provides a review of recently published literature relating to crowdsourcing in the manufacturing industry and offers suggestions for the future direction of crowdsourcing research in manufacturing and product development.
Software-defined network (SDN) has become one of the most important architectures for the management of largescale complex networks, which may require repolicing or reconfigurations from time to time. SDN achieves easy repolicing by decoupling the control plane from data plane. Thus, the network routers/switches just simply forward packets by following the flow table rules set by the control plane. Currently, OpenFlow is the most popular SDN protocol/standard and has a set of design specifications. Although SDN/OpenFlow is a relatively new area, it has attracted much attention from both academia and industry. In this paper, we will conduct a comprehensive survey of the important topics in SDN/OpenFlow implementation, including the basic concept, applications, language abstraction, controller, virtualization, quality of service, security, and its integration with wireless and optical networks. We will compare the pros and cons of different schemes and discuss the future research trends in this exciting area. This survey can help both industry and academia R&D people to understand the latest progress of SDN/OpenFlow designs.
Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.Highlights► Cloud computing is emerging as a major enabler for the manufacturing industry. ► Cloud computing technologies can be adopted in manufacturing. ► Cloud manufacturing is a pay-as-you-go business model. ► Distributed resources are encapsulated into cloud services and managed centrally.