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Fixture design and planning is one of the most important manufacturing activities, playing a pivotal role in deciding the lead time for product development. Fixture design, which affects the part-quality in terms of geometric accuracy and surface finish, can be enhanced by using the product manufacturing information (PMI) stored in the neutral standard for the exchange of product model data (STEP) file, thereby integrating design and manufacturing. The present paper proposes a unique fixture design approach, to extract the geometry information from STEP application protocol (AP) 242 files of computer aided design (CAD) models, for providing automatic suggestions of locator positions and clamping surfaces. Automatic feature extraction software “FiXplan”, developed using the programming language C#, is used to extract the part feature, dimension and geometry information. The information from the STEP AP 242 file is deduced using geometric reasoning techniques, which in turn is utilized for fixture planning. The developed software is observed to be adept in identifying the primary, secondary, and tertiary locating faces and locator position configurations of prismatic components. Structural analysis of the prismatic part under different locator positions was performed using commercial finite element method software, ABAQUS, and the optimized locator position was identified on the basis of minimum deformation of the workpiece. The area-ratio (base locator enclosed area (%)/work piece base area (%)) for the ideal locator configuration was observed as 33%. Experiments were conducted on a prismatic workpiece using a specially designed fixture, for different locator configurations. The surface roughness and waviness of the machined surfaces were analysed using an Alicona non-contact optical profilometer. The best surface characteristics were obtained for the surface machined under the ideal locator positions having an area-ratio of 33%, thus validating the predicted numerical results. The efficiency, capability and applicability of the developed software is demonstrated for the finishing operation of a sensor cover – a typical prismatic component having applications in the naval industry, under different locator configurations. The best results were obtained under the proposed ideal locator configuration of area-ratio 33%.

As digital technology and connectivity advance rapidly, the premise of bringing supply chain (SC) visibility across multiple tiers of supply, whilst facilitating the velocity to achieve strategic business objectives, is gaining interest. The feasibility and timing for successful adoption and implementation of such technology depend primarily on the readiness level and specific needs of each organisation, making it imperative to exercise insightful judgement as it can be expensive to acquire, develop and master. This research study examines the market pull versus technology push components of the functionalities enabled by digital SC control towers and buildings on the outcome of an extensive survey and expert interviews and proposes an assessment tool to aid decision making for the consideration of their adoption.

Nowadays most of the communication technologies used in the manufacturing industry are still wired, including various fieldbuses and dedicated industrial Ethernet technologies, though wireless communication technologies including WiFi and ZigBee are recently being adopted. This study is to investigate the integration of 5G wireless communication technologies with collaborative intelligent manufacturing (CIM) processes and systems. 5G technologies and typical scenarios including enhanced mobile broadband, massive machine type communications, and ultra-reliable low latency communication are introduced. Various possible applications or business slices of 5G in CIM are analysed, including human–machine interfaces and production IT, process automation, factory automation, logistics and warehousing, monitoring, and maintenance. These applications are analysed by the functions as modules with the extended 5C architecture. A modular collaborative production approach is suggested to combine modules to achieve on-demand resource allocation and complete flexible tasks in CIM systems. Related research challenges and opportunities are also discussed.

Distributed computer integrated manufacturing is increasingly adopting cloud computing, software-as-a-service (SaaS) and multi-agent systems as steps towards “design anywhere, build anywhere” strategy. In this scenario, ontologies not only serve as common message exchange structure among distributed agents but also provide reasoning capability to extract implicit knowledge from explicit information already stored in the knowledge base. Foundation ontologies (upper-level), comprised of most general concepts of a domain, provide a common semantic structure to the domain-level ontologies, which capture details of multi-disciplinary manufacturing knowledge. In this paper, novel upper-level ontology, called SIMPM (Semantically Integrated Manufacturing Planning Model), is proposed in order to model three fundamental constraints of manufacturing process planning: variety, time, and aggregation. The philosophical underpinning of the proposed ontology – presented as OWL-DL axioms – is derived from a three dimensional planning model, developed during our past research on computer-aided process planning. As part of the evaluation of SIMPM ontology, we first expound on the interoperability issues with other upper-level manufacturing ontologies. Next, we present a case study on process planning for prismatic part design. In this way, we demonstrate how the generic set of proposed axioms may be used to address various manufacturing process planning concerns, such as alternative manufacturing resources, the temporal order among operations and granularity in the details of a process plan.

When the entire system was put together, adequate control of the robot was possible. Overall the system performed well enough to get controlled motion of the six joints of the PUMA robot arm. The system was also subsequently used to control a CNC lathe. There were, however, a few problems with the final system. The system also requires some further enhancement to achieve better control of joint positioning. The main problem occurred with the logic power supply. Every now and then the logic power, which supplies all the encoder circuits and microprocessors, would fail to power the most essential parts of the system. This would result in unpredictable behaviour from the robot arm and make it unsafe. It was found that the grounding of the logic power supply was floating causing differences in the ground of the system components. By using the ground of a bench-top power supply, this problem was solved. The rest of the problems with the final system were a result of the PID control method. The PID control was not able to account for the effects of inertia and gravity because the feedback gains of the PID algorithm were fixed. This meant that the system had a very low level of repeatability and accuracy. The Encoder PID mode of the BrainStem Moto 1.0, although working well for the wrist joints, did not perform very well for the larger joints. When a new setpoint was entered into the PID control loop, the control algorithm would output maximum voltage to the motor until it neared the new setpoint based on the feedback from the encoders and the PID gains. It did this without accounting for the effects of inertia and gravity. In practice this meant, for the shoulder and elbow joints especially, that the joint would move more rapidly in the down direction that it would in the up direction. To correct this problem, a velocity control method needs to be explored. Another problem that exists with the BrainStem Moto 1.0, is that it only has a 16-bit setpoint number and this is reduced to 15-bits as the most significant bit is a directional bit. Due to the resolution of the 250 line incremental encoders, only limited movement of a joint can be completed with each command sent to the BrainStems. This leads on to another problem with the BrainStem program. It is not able to detect when a movement is complete so that the next move of the robot can take place, i.e. there are no flags set to indicate a new setpoint has been achieved. This problem was overcome, in Visual Basic, by introducing a timer delay between selected movements. A problem also occurs when the motors are powered at high speeds. At high motor speeds the PIC fails to read the encoder pulses. This results in uncontrolled motion of the joints. In some cases the encoder circuit that was developed for the incremental encoders fails to supply the pulse train signal to the BrainStems. This may be because there was a frequency limitations on the ICs used in the encoder circuit. This problem could also arise from aliasing. This would depend on the rate at which the PIC samples the input signal from the encoder circuit. The effect of this problem is that robot must operate at reduced speeds in order to maintain control. The only other item of note is that the robot joints require calibration every time the robot is turned on. This is the purpose of the potentiometers in each motor. The potentiometers are input directly to the analogue inputs of the BrainStem Moto 1.0 that provides a 10-bit analogue-to-digital conversion of the potentiometer value. This value gives an indication of the position of the robot when it is powered up. As these problems are overcome, further planned development of the system is to integrate it with RobotWorks, a computer aided manufacturing (CAM) package. RobotWorks is an add-on package for SolidWorks, which is a commonly used CAD program. The idea being that path planning for the manufacture of various parts and 783

Manufacturing and control procedures for automation require many different technologies. The last decade has seen computer technology applied widely in industrial production, particularly in manufacturing processes not generally associated with high technology. The technology used in the automated production of moulded concrete elements for the architectural and building industry has changed dramatically in recent years. The introduction of Computer Integrated Manufacturing (CIM) between 1960 and 1980 (Trybula & Goodman, 1989), e.g., numerically controlled machines, followed in the 1980 to 1990 developments in robotics, e.g., advanced robotics (Shell & Hall, 2000) had a significant impact on automating the production of moulded concrete elements. The types of system that produce concrete elements are mainly machines using hydraulic compression and extraction. This is the most common method used to form the complex product shapes required by the architectural and building industry. These machines can be either stationary, or they can be mobile, with automated mobile machines being the machine of preference these days. Other relevant ancillary equipment used in the automated production of concrete elements are aggregate mixers, material storage silos and conveyors. The concrete elements required by architects and builders often include complex geometric shapes; it is for this reason that the central process in their production is the hydraulic press. Complex geometric shapes in concrete and their quality control can only produced using automated compression machinery (Isayev, 1987; Reinhart, 1987). The various phases of this process are (1) material mixture; aggregates of variable form tightly compressed into a mould and (2) compression to form and extract the desired product. The application of computer aided systems to hydraulic press machines has increased the range and variety of concrete elements that these machines can produce for the architectural and building industry (e.g., ZENITH, KNAUER, BESSER, etc.). The consistency and quality control of landscape and architectural products, such as concrete blocks, curb stones, palisades, paving stones, etc., has improved through introducing computer aided systems. Computer-control systems, teleoperation and automation technology, modelling and simulation tools are some of the technologies and techniques used to acquire the desired Source: New Approaches in Automation and Robotics, Book edited by: Harald Aschemann, ISBN 978-3-902613-26-4, pp. 392, May 2008, I-Tech Education and Publishing, Vienna, Austria O pe n A cc es s D at ab as e w w w .in te hw eb .c om

Manual work is the key source of skill and ingenuity in industrial manufacturing. At the same time, it is the most expensive resource factor. Often machines can replace humans for more effective manufacturing because machines outperform humans in terms of strength, precision and endurance. Humans, however, perform better than machines when flexibility and intelligence is required. Flexible Manufacturing Systems are capable of manufacturing wide variety of products with low volume size and minimal lead time. These manufacturing cells are usually equipped with high level computer based control and include highly flexible industrial robots also. Robots can partially resolve contradiction of flexibility and intelligence due to their more humanlike structure and programmability. To a large extent, robots have already relieved workers of many of the tedious, hard and unhealthy parts of industrial work. But the universal application of robots in small-batch highly customized production is hindered by the time consuming robot programming. This thesis shows efficient methods and processes for teaching and optimizing complex robot tasks by introducing cognitive robot programming, flexible robotics, and middleware. Intelligent user interfaces combining information from several sensors in the manufacturing system will provide the operator with direct knowledge on the state of the manufacturing operation. Thus, the operator will be able to determine the system state quicker. Sensory system calculates and proposes the optimal process settings. The key element is the new cognitive human-machine communication channels helping the operator to comprehend the information from the sensory systems. Novel middleware technology is facilitated for integration of elements from different system platforms into a coherent robot system and scaling it for different hardware complexity levels. The contributions of the thesis could be summarized as follows: 1. Introduction of a new scientific concept of task dependent, software component based controller for flexible manufacturing cells. 2. Development of a new paradigm of robot teaching and supervising, which opens a new dimension of robotization in the area of small and medium sized enterprises. 3. Introduction of a new concept for Coginitve Telemanipualtion.

The management of maintenance activities extremely affects the useful life of the equipments, product quality, direct costs of maintenance and consequently production costs. Thus, a reliable maintenance system is critical to maintain an acceptable level of profit and competition. Studies over 20 years have indicated that around Europe the direct cost of maintenance is four to eight percent of total sales turnover (Muller 2007). The indirect cost of maintenance is likely to be a similar amount. Thus, the potential savings from modern maintenance would be massive. Neural Management Maintenance System (NMMS) is a new technique yet to be further developed to reduce the involvement of analysts/engineers in data processing and thus adding quality in decision-making process. NMMS is based on Artificial Neural Networks (ANNs). The attractiveness of ANNs comes from their remarkable information processing characteristics mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. They have the ability to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been assigned to analyze. The NMMS would permanently monitor the system and suggest the most appropriate actions and strategies. This chapter explains a NMMS that integrates Corrective Maintenance (CM), adaptive Preventive Maintenance (PM) and Condition Based Maintenance (CBM) with suitable maintenance strategy addressed for each component/subsystem. The NMMS monitors the system and suggests the most appropriate maintenance actions. The main characteristics of the system includes; integration of expert opinion in a knowledge base, storing maintenance history and tracking components, alarming predetermined maintenance activities, alerting for spare parts and materials, updating schedules, considering limitation of resources, and measuring the effectiveness of the maintenance system. The easiness and intelligence of the proposed NMMS depends on keeping the maintenance data in MS-EXCEL spreadsheets and linking it to MATLAB® which in turn updates the models and makes the decisions. 15

Increasing numbers of products, shorter product life cycles, one lot sizes, fulfilling customer expectations, and increasing pressure for innovative and global supply networks are the major challenges of manufacturing companies. Computer Integrated Manufacturing and Lean Production that emerged in the third revolution are accepted as the messengers of the fourth industrial revolution. Wahlster states that there is a transformation from the era of mathematization to the era of informatization in medical, media, energy, law, automotive, computational linguistics, biotechnology, and neuroinformatics sciences. After the launch of Industry 4.0, researchers started to study this concept. Though there is a large interest on its technological side, there are fewer studies related to the human element. Some of them are listed in a chronological order to summarize the related literature to the readers. Richert et al. made an experimental study about factors that influence hybrid team development.

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. The International Journal of Computer Integrated Manufacturing was established in 1988 with the idea of advancing research in computer integrated manufacturing (CIM) technologies and promoting the application of those technologies within industry. The journal was created to facilitate the exchange of new knowledge between industry and academia derived from both research and practical application. To celebrate the 30-year journey of the journal, this study develops a bibliometric analysis of all the publications of the journal to 2017. Information was collected using the Web of Science Core Collection database. The present study has been conducted to highlight the significant contributions of the journal in terms of impact, topics, authors, universities and countries. Finally, visualisation of similarities (VOS) viewer software was used to present graphical representations of the bibliographic coupling, co-citation, citation, co-authorship and co-occurrence of keywords.

Industrial automation (IA) is a major manufacturing strategy which employs modern techniques and practices in manufacturing to achieve sustainable competitive advantage in the manufacturing industry. This research aims to categorize AI factors and examine its relative importance for better implementation in manufacturing industries. The relative importance and ranking of AI factors in manufacturing industry was done through a literature review, discussion with experts, and analytic hierarchy process (AHP) approach. In all, 13 IA factors were identified and further divided into three factor categories. Then their prioritization was done using AHP approach to assign the relative importance of these 13 factors affecting the industrial automation in manufacturing industries. The result will help the manufacturing industry mangers to work upon them based on their relative importance to improve their industrial automation performance. Finally, limitations, managerial implications, and scope of future study are presented at the end of the paper.

The emergence of the globalization paradigm in manufacturing industry has changed the markets’ circumstances, and accordingly manufacturing systems need to be compatibilized with these situations. There are many solutions to assist manufacturing systems for this purpose. Computerized systems and their related solutions were being the most attractive ones for manufacturing systems in recent decade. Developing such systems and exploiting from the advantages of such solutions requires a consistent foundation. Computer Integrated Manufacturing (CIM) emerged as a philosophy to provide such foundation, and to assist the computerized manufacturing solutions. Despite of the high potentials of the CIM philosophy, the related works lack in many aspects. The absence of a thorough look to exploit the high potentials of the CIM philosophy to consider different aspects of the manufacturing systems, and the complexity which exists in the nature of the designing of such systems are the major concerns. These concerns lead us to afford them fundamentally with an architectural view. The architectural view of the paper is because of architectures are the basis for designing and future development of the systems, and synthesizing solutions that satisfy the system requirements. Accordingly, the architecture of the system is considered as a basis for creating desired changes and resolving the mentioned concerns. Therefore, the paper proposes a layered architecture which covers five critical aspects of a CIM system. Physical, Functional, Managerial, Informational, and Control aspects are the five critical aspects which are assigned to five layers of the architecture. The manipulated architecture assists manufacturing systems to audit their Physical, Functional, Managerial, Informational, and Control aspects based on a proposed set of ISO standards in order to compatibilize and align themselves with the necessities of the globalized manufacturing industry. The layered architecture expected to be well-structured and to resolve the concerns. The expectations have been investigated through a promising tool for designing and analyzing complex systems, known as Axiomatic Design (AD) Theory. The AD-based design and analysis are according to two axioms, known as Independence Axiom and Information Axiom. Moreover, the AD Theory approaches have been used to reach a set of standard guidelines for exploring and achieving realization operands for the architecture. By analyzing the results with the AD Theory axioms, appropriateness of the design has been approved. Finally, a roadmap for future works is delineated.

Although various monitoring techniques are currently used for semiconductor manufacturing, OES is a non-contact and non-destructive plasma measurement tool that detects end points and plasma abnormality. Despite these advantages and their high utilization in plasma processing measurement, the acquisition region of OES data at various wafer sizes only allows examination of part of the plasma, owing to the characteristics of the conventional OES resulting in the limited detection capability of process abnormality. In this paper, a novel real-time monitoring method for detecting plasma process uniformity and abnormality using incident-angle-dependent OES is proposed. Using both a body tube and a wide-angle lens to adjust the incident angle of OES, the intensity of plasma light can be measured accurately using selective plasma light in semiconductor manufacturing. This real-time monitoring technique can be utilized to obtain plasma light in a process chamber and thereby analyze the uniformity and detect the abnormalities in the plasma process for computer-integrated manufacturing.

With ever-accelerating advancement of information, communication, sensing and characterization technologies, such as industrial Internet of Things (IoT) and high-throughput instruments, it is expected that the data generated from manufacturing will grow exponentially, generating so called ‘big data’. One of the focuses of smart manufacturing is to create manufacturing intelligence from real-time data to support accurate and timely decision-making. Therefore, big data analytics is expected to contribute significantly to the advancement of smart manufacturing. In this work, a roadmap of statistical process monitoring (SPM) is presented. Most recent developments in SPM are briefly reviewed and summarized. Specific challenges and potential solutions in handling manufacturing big data are discussed. We suggest that process characteristics or feature based SPM, instead of process variable based SPM, is a promising route for next generation SPM and could play a significant role in smart manufacturing. The advantages of feature based SPM are discussed to support the suggestion and future directions in SPM are discussed in the context of smart manufacturing.

While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are not pervasive in industry. A vendor-agnostic SM platform is under development that integrates information technology, models, and simulations driven by real-time plant data and performance metrics. By utilizing existing process control and automation systems, manufacturing organizations can manage systems at a much lower cost, optimizing process knowledge and improving energy productivity. Three case studies are presented: steam methane reforming to make hydrogen, optimization of a heat treatment furnace for metals processing, and a fuel cell system, all of which utilize high fidelity models as a starting point for optimization and control. The Smart Manufacturing Leadership Coalition has led the national effort in SM, and the recently established National Manufacturing Innovation Institute funded by DOE , private industry, and state governments will be described.

Digital transformation revolutionises the way people work not only in office settings but also in physical work settings such as manufacturing or construction. New ways of combining digital and phy...

The fourth industrial revolution involves the advanced topics, such as industrial Internet of Things, cyber-physical system and smart manufacturing that address increasing demands for mass customized manufacturing. The agent-based manufacturing is a highly distributed control paradigm that can cope with these challenges well. This paper gives an overview of agent-based architectures for manufacturing systems. Besides, a cloud-assisted self-organized architecture is presented by comprising smart agents and cloud to communicate and negotiate through networks. Ontological representations of knowledge base are constructed to provide the information basis for decision-making of agents, which enables dynamic reconfiguration among agents in a collaborative way to achieve agility and flexibility. Furthermore, the agents' interaction behavior is modeled to structure the agents hierarchically to reduce the complexity, because the interactions among agents in distributed system are difficult to understand and predict. The experimental results show that the presented architecture can be easily deployed to build smart manufacturing system and can improve the adaptiveness and robustness of the manufacturing system when dealing with mixed multi-product tasks.

This paper proposes new perspectives and systemic changes to the linear fashion scheme, which is currently driven by fast, cheap and low quality production that fosters easy disposal or replacement, due to the low product value for the customer/user. The authors seek to open a discussion on new value creation through social manufacturing, specifically facilitated by do-it-yourself (DIY), do-it-together (DIT) and participatory design strategies. Social manufacturing can be seen as a more open and democratic approach to manufacturing, prompting different levels of user participation in the production process. The authors will illustrate how these alternative design strategies, build within the context of social manufacturing, can offer system-level changes by activating and empowering the end user to become value creators, while forming new, more sustainable innovations in design and manufacturing of fashion. The specific questions asked in this paper are: What types of value are created in social manufacturing through opening the design and manufacturing processes with alternative design strategies? Further, can social manufacturing enable sustainable solutions by transitioning the users to manufacturers of their own garments, prompting a new value system and range of business models in the fashion industry? An alternative value framework, developed within this paper, will enable the analysis of empirical data collected in Finland and the U.S. In the discussion, the authors demonstrate strategies to create wider change in the fashion system through social manufacturing, starting at a local level through empowered consumers.

Additive manufacturing (AM) is fundamentally different from traditional formative or subtractive manufacturing in that it is the closest to the ‘bottom up’ manufacturing where a structure can be built into its designed shape using a ‘layer-by-layer’ approach rather than casting or forming by technologies such as forging or machining. AM is versatile, flexible, highly customizable and, as such, can suite most sectors of industrial production. Materials to make these parts/objects can be of a widely varying type. These include metallic, ceramic and polymeric materials along with combinations in the form of composites, hybrid, or functionally graded materials (FGMs). The challenge remains, however, to transfer this ‘making’ shapes and structures into obtaining objects that are functional. A great deal of work is needed in AM in addressing the challenges related to its two key enabling technologies namely ‘materials’ and ‘metrology’ to achieve this functionality in a predictive and reproductive ways. The good news is that there is a significant interest in industry for taking up AM as one of the main production engineering route. Additive Manufacturing, in our opinion, is definitely at the cross-road from where this new, much-hyped but somewhat unproven manufacturing process must move towards a technology that can demonstrate the ability to produce real, innovative, complex and robust products.

The purpose of this paper is 1) to analyze the potential digital manufacturing technologies and open design have to achieve a distributed economy and 2) to identify the limits and possibilities of digital manufacturing and open design in relation to the principles of Design for Sustainability applied to the clothing sector. The method used was bibliographic review. This paper emphasizes the benefits of distributed economies on attempting design for sustainability principles. Then, it discusses the relation between open design and digital manufacturing with distributed economies. We argue that open design and digital manufacturing have the potential to reduce the impacts caused by the clothing sector, but we emphasize that both strategies per se do not lead to a more sustainable scenario.

The requirements development process (RDP) is an important stage in deploying functional requirements from user requirements in the design of a manufacturing information system. As the RDP may contain subjective views or misunderstanding of user requirements, developers of a manufacturing information system should verify the effectiveness, consistency and completeness of the requirements in an RDP. Quality function deployment (QFD), a systematic process of deploy product functions the voice of customer, provides developers with a solid mechanism to check the effectiveness of the voice of customers and to ensure the consistency and completeness of product functions in the product development process. In order to utilise these advantages of QFD, this study proposes a QFD-based RDP that introduces the concept of QFD to the existing RDP. The QFD-based RDP in the design of a manufacturing information system defines the critical-to-system functions as product critical-to-quality (CTQ) of the QFD-1 proce...

The integration of cyber–physical systems and cloud manufacturing has potential to change manufacturing processes for better manufacturing accessibility, agility, and efficiency. To achieve this, i...

From Germany’s Industry 4.0 mission to Made in China 2025 and Make in India mission to British Factory of the Future in 2050, digital manufacturing (DM) is promoting in the world’s major industrial countries as a technology foundation of the future manufacturing. At the same time, in the different segments of the DM realm, different forms of information technologies (IT) are flourishing such as the following: computer-aided manufacturing, robotics control in manufacturing, and process simulation. This paper is aimed to review the latest initiatives of DM in the leading universities and major industrial countries. Along with, a critical literature review of various initiatives in the area of DM-assisted hybrid additive manufacturing (DM-HAM) has also been carried out. DM-HAM seems to be very promising for next generation multi-operational manufacturing as it is time saving and economical. The highlights of this review will provide a guide for the upcoming research activities in the area of DM-HAM.

This paper is concerned with the manufacturing strategy decision-making structure in multi-plant networks, i.e. how strategic manufacturing decision-making authority is distributed between the netw...

New business models harnessing the power of individuals have already revolutionized service industries and digital content production. In this study, we investigate whether a similar phenomenon is taking place in manufacturing industries. We start by conceptually defining two distinct forms of firm-individual collaboration in manufacturing industries: (1) social cloud manufacturing, in which firms outsource manufacturing to individuals, and (2) social platform manufacturing, in which firms provide manufacturing services to individuals. We then empirically investigate the nature of firm-individual collaboration within these forms, focusing on the role of individuals. We find that the individuals are often makers who view their participation primarily as a hobby and are driven mainly by nonmonetary benefits, that the design process often involves both parties, and that the two forms of collaboration exploit different enabling technologies. Our findings suggest that firms working with individuals can potentially reap multiple benefits, including fresh ideas, broader design support, and quick delivery times. This article contributes to an improved understanding of how firms can build potentially disruptive business models in manufacturing industries by leveraging individuals, thereby adding to the emerging stream of literature on social manufacturing.

As Additive Manufacturing becomes increasingly prevalent in commercial manufacturing environments, the need to effectively consider optimal strategies for management is increased. At present most research has focused on individual machines, yet there is a wealth of evidence to suggest competitive manufacturing is best managed from a systems perspective. Through 14 case studies developed with four long-established Additive Manufacturing companies this paper explores the conduct of Industrial AM in contemporary manufacturing environments. A multitude of activities, mechanisms, and controls are identified through this detailed investigation of Additive Manufacturing operations. Based on these empirical results a general four component Industrial Additive Manufacturing System is developed, together with the identification of potential strategic opportunities to enhance future manufacturing.

[EN] The urgent need for sustainable development is imposing radical changes in the way manufacturing systems are designed and implemented. The overall sustainability in industrial activities of manufacturing companies must be achieved at the same time that they face unprecedented levels of global competition. Therefore, there is a well-known need for tools and methods that can support the design and implementation of these systems in an effective way. This paper proposes an engineering method that helps researchers to design sustainable intelligent manufacturing systems. The approach is focused on the identification of the manufacturing components and the design and integration of sustainability-oriented mechanisms in the system specification, providing specific development guidelines and tools with built-in support for sustainable features. Besides, a set of case studies is presented in order to assess the proposed method., This research was supported by research projects TIN2015-65515-C4-1-R and TIN2016-80856-R from the Spanish government. The authors would like to acknowledge T. Bonte for her contribution to the NetLogo simulator of the AIP PRIMECA cell.

The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

PurposeThe purpose of this paper is to identify and analyze knowledge integration in manufacturing technology development projects required to build competitive advantages.Design/methodology/approachA longitudinal case study has been conducted at a Swedish manufacturing company by following a manufacturing technology development project in real time during a two-year period.FindingsThe results show that three different knowledge integration processes exist when developing unique manufacturing technology: processes for capturing, for joint learning, and for absorb learning. The findings of the current research suggest that the three knowledge integration processes are highly interrelated with each knowledge integration process affecting the other two.Research limitations/implicationsThe major limitation of the research is primarily associated with the single case, which limits generalizability outside the context that was studied.Practical implicationsThe findings are particularly relevant to manufacturing engineers working with the development of new manufacturing technologies. By using relevant knowledge integration processes and capabilities required to integrate the knowledge in manufacturing technology development projects, companies can improve design and organize the development of manufacturing technology.Originality/valuePrevious research has merely noted that knowledge integration is required in the development of unique manufacturing technology, but without explaining how and in what way. This paper’s contribution is the identification and analysis of three knowledge integration processes that contribute to the building of competitive advantages by developing unique manufacturing technology and new knowledge.

Human–machine collaborative manufacturing systems consist of human operators and automated machines. They cooperate with each other to accomplish complex tasks that are difficult for either the human or machine alone. This letter considers an optimal task-allocation problem for human–machine collaborative manufacturing systems, where various tasks are dispatched to human operators and automated machines to achieve optimal joint human-system performance. Designing such optimal task allocation is challenging because of the stochastic hybrid feature of manufacturing processes, as well as varying human performance caused by physical fatigue. To address this challenge, we first model human fatigue as a continuous-time Markov decision process, which is capable of capturing stochastic uncertainties on fatigue dynamics under different task assignments. A novel controlled stochastic petri net is then proposed to model the manufacturing process, in which both time- and event-driven dynamics can be regulated by task allocation between the human and machine. Under mild assumptions, we show that the optimal task-allocation problem under the proposed human manufacturing framework can be solved by linear programming. Simulation results of a four-part assembly process are used to verify our theoretical findings.

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include: multi-time scale, space-time scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.

Future factories will feature strong integration of physical machines and cyber-enabled software, working seamlessly to improve manufacturing production efficiency. In these digitally enabled and network connected factories, each physical machine on the shop floor can have its ‘virtual twin’ available in cyberspace. This ‘virtual twin’ is populated with data streaming in from the physical machines to represent a near real-time as-is state of the machine in cyberspace. This results in the virtualization of a machine resource to external factory manufacturing systems. This paper describes how streaming data can be stored in a scalable and flexible document schema based database such as MongoDB, a data store that makes up the virtual twin system. We present an architecture, which allows third-party integration of software apps to interface with the virtual manufacturing machines. We evaluate our database schema against query statements and provide examples of how third-party apps can interface with manufacturing machines using the VMM middleware. Finally, we discuss an operating system architecture for VMMs across the manufacturing cyberspace, which necessitates command and control of various virtualized manufacturing machines, opening new possibilities in cyber-physical systems in manufacturing.

This paper presents a new concept, Cloud Business Performance (CBP) and describes the method of measurement, data analysis, impacts to manufacturing and case studies about CPB. Three methods can be used for CBP with two case studies illustrated. The first case study presents a small and medium manufacturing enterprise that has adopted backup services for all manufacturing transactions and records. The second case study shows a software manufacturing organization’s forecasting on their business performance and risk. Methods used, results and analysis have been fully justifiable to support the case of CBP for manufacturing organizations. We demonstrate that the use of CBP calculation and prediction analysis is useful for manufacturing organizations that adopt Cloud Computing.

IF=2.623; International audience; Since the start of industrialization, machine capabilities have increased in such a way that human control of processes has evolved from simple (with mechanization) to cognitive (with computerization), and even emotional (with semi/full automation). The processes have also evolved from simple to complicated, and now complex systems, in the emerging context of Industry 4.0. This is notably the case with Intelligent Manufacturing Systems in which processes have become so autonomous that humans are unaware of the processes running, while they may need to intervene to update the manufacturing plan or modify the process configuration if a machine breaks down, or to assist process-intelligent entities when they find themselves in a deadlock. This paper highlights the lack of attention paid to the correct integration of humans in Intelligent Manufacturing Systems and provides solutions based on Human-Machine Cooperation principles to retain humans in the process control loop with different levels of involvement identified by the levels of automation. The aim of these principles is to propose a human-centered approach to design and evaluate systems, processes, and their interactions with humans. Herein, these principles are detailed and applied to Intelligent Manufacturing Systems using Artificial Self-Organizing systems (ASO) as an example. An assistance system was designed to support cooperation between ASO and human operators. Experiments were conducted to evaluate the system and its utility in improving the performance of Human-Machine Systems, as well as its acceptability with regard to human factors. The results presented highlight the advantages of the approach within the context of Industry 4.0.

A growing interest towards smart manufacturing is focused on sustainable development at both industrial and policy level. In particular, an effective sustainable development incorporates three fundamental pillars: environment, cost and society. While environment and cost have been already faced by numerous studies so far, society is still the less considered in manufacturing. Social sustainability comprises specific relapses on humans that are quantifiable through social assessment methods. This research work proposes a new Social Life Cycle Assessment (S-LCA) methodology, based on the United Nations Environment Programme and the Society for Environmental Toxicology and Chemistry (UNEP/SETAC) framework, to support enterprise modelling and knowledge management to assess company sustainability by solving current issues and uncertainties regarding the evaluation of social impacts in the context of smart manufacturing. The analysis is settled into a LIFE +2012 European Project, named LIFE GREEN SINKS. The project concerns the introduction of innovative materials for a new generation of kitchen sinks. The S-LCA methodology proposed embeds a detailed inventory method tailored on real data, collected directly from the field of application thanks to customised surveys that allow modelling the enterprise and integrating data available from the manufacturing process. This method is designed as a supporting tool for enterprise modelling and strategic company decision-making. Specifically, it intends to help companies in understanding and, consequently reducing, social impacts of all processes related to a certain product from a life cycle perspective, to achieve an effective “smart” manufacturing for a sustainable development.

Advanced manufacturing uses emerging technologies to critically enhance not only the economic competitiveness of individual manufacturers but also the sustainability of the whole industrial sector. New materials and technologies require new manufacturing processes and novel analytical models for process controls and parameter optimization regarding cost, reliability, quality, product flexibility, energy consumption, and fossil carbon emissions. The successful adoption of advanced manufacturing for sustainability can only be realized by following a systematic approach from concept development, product design and manufacturing to product delivery and service as well as in forward and reverse supply chain management. This special volume reports on progress of advanced manufacturing on sustainability improvements along the whole life cycle and covers the six themes: 1. Design theory and methodology for sustainability with advanced manufacturing; 2. Energy efficiency assessment and control of mechanical manufacturing systems; 3. Parameter optimization for advanced manufacturing and remanufacturing; 4. Low fossil-carbon process planning and production scheduling; 5. Integration of supply-chain innovations and advanced manufacturing; 6. Sustainable innovation for product-service systems. In addition, this SV introductory article, highlights future research directions, such as the need for energy consumption and emission data for advanced manufacturing processes, optimization models and control schemes, supply chain innovations, and product-service integration.

This paper proposes an extension of International Electrotechnical Commission (IEC) Common Information Model (CIM) to accomplish the interoperability requirements for the power system state estimation (SE). The proposed method utilizes a multiarea SE (MASE) approach, suitable for the power system networks, which can be divided into several independently controlled subareas. The standard IEC CIM model is extended for exchanging the SE results of the MASE from subareas to a central coordinator (CC) in the standard format, which might individually use diverse proprietary software applications and databases. The suggested schema defines the data exchange format among the subareas and the CC without modifying proprietary databases and software applications. The new MASE approach, used to demonstrate the proposed CIM extension process, requires lesser data exchange as there is no topology information exchange among the subareas and the CC, thus resulting in reduced communication bandwidth requirement. The effectiveness of the proposed approach has been demonstrated on IEEE 30 bus system and 246-bus reduced Northern Regional Power Grid Indian system.

The wide use of Internet of Things (IoT) technologies in manufacturing shop-floor creates an opportunity to turn the traditional manufacturing factories into smart ones. The synchronous operation between the real shop-floor frontline and virtual production management system is vital in the establishment of smart factories. However, managing a wide variety of sensing devices and processing the large amount of raw captured manufacturing data are difficult for general production operators. In this article, the Coloured Petri net (CPN) technology is combined with IoT technologies to establish an active sensing system of real-time and multi-source manufacturing information (CPN-MIASS). It aims to establish a systematic graphics-based modelling approach for the sensing of manufacturing information and the management of sensor, which can assist the general operators in monitoring and controlling the real-time manufacturing process easily and dynamically. Based on this system and its key components, the multiple sensors can be managed in a ‘Plug-and-play’ manner, and the high-level manufacturing information can be obtained efficiently. In addition, a prototype system is used to validate the usability of the proposed CPN-MIASS.

Many of the problems of manufacturing systems, including scheduling, are NP-hard and therefore cannot be solved and optimized in real time as required in the real world. This paper investigates the possibility of controlling manufacturing systems while maximizing the enterprise profit in a stochastic and dynamically varying environment. In this research, we developed a multi-agents system that operates on the factory floor level and directly controls the manufacturing system. The paper examines the proposed multi-agents system, demonstrates its feasibility by simulation under various experimental settings, presents important findings and excellent results, and draws conclusions.

Breakthroughs in molecular and synthetic biology are pivotal to understanding the function of cells and creating new pharmaceutical applications. These advances in biological processing present a new class of manufacturing systems, defined here as genetic manufacturing systems, which produce a final product with a genetic construct. Genetic manufacturing systems rely on molecular events for success, and this aspect is a key difference from traditional manufacturing systems. Analysis techniques for manufacturing systems have been successful in providing valuable insights for complex manufacturing environments and have the potential to transform how genetic constructs are currently produced. This paper provides an introduction to the interdisciplinary field of genetic manufacturing systems and outlines the similarities and primary differences between traditional manufacturing systems and genetic manufacturing systems. Mathematical modeling and simulation opportunities are presented as they relate to reducing cost and time as well as increasing efficiency in genetic manufacturing systems. Finally, several challenges for genetic manufacturing systems are presented.

Manufacturing companies have to implement engineering changes in manufacturing systems to remain competitive in changing conditions. Physical influences like collisions or load capacities have to be considered for a proper planning of these changes by modeling and simulating the influences. To consider these physical influences, a concept is presented for the integration of physical model into realization of engineering changes. The concept provides information for the feasibility of the work steps for the execution and enables an efficient planning. Hence, planning mistakes can be detected before changes are executed and the negative impact on ongoing manufacturing can be avoided.

In this work, the design and implementation of a set of practical activities in a flexible manufacturing cell is exposed, as well as the teaching methodology used, both in programming cell manually (guided programming) or remotely (off-line, using simulation software). These activities have been designed for a progressive learning, starting with simple tasks, like programming a robot arm movement between different points in space, and concluding with more complex tasks, such as programming of palletizing cycles or manufacturing a part by machining.

In the present competitive and demanding business markets, manufacturing industries need to improve their strength and must concentrate on their process flows by assigning the task resources carefully to improve productivity. It also focuses on continuous improvements with the total involvement of all employees with optimum utilisation of man power and machine by reducing idle time and lead time. They also provide the application for variety of tools and strategies which can help in identification of waste reduction or elimination of waste, lowering the product cost, reducing human effort and minimising product manufacturing time. In the current competitive business era, an industry unable to sustain in the global business without implementing the computer integrated manufacturing in the manufacturing environment. In order to overcome, a computer-based lean audit methodology is to be implemented in the production environment for improving the production characteristics like quality, reduce wastage and red...

Numerous ontologies have been introduced to represent and activate engineering knowledge across the development process. However, many of the ontologies in the manufacturing domain provide limited ...