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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

Due to their intense dynamics, business environment relationships in the market drive constant changes within business systems and continuous accommodation to the newly arisen circumstances. As business operations have become too complex and dynamic, adaptability has arisen as one of the major features of business systems. A possible solution to the problem of increased flexibility and the ability for quick adaptation is the development of a business system states model as well as an alternative states management system model. It must be noted that the state and alternative solutions management system needs to respond timely and appropriately, taking into consideration the integrity of business processes and available resources. Contemporary information and communication technologies (ICT) have established themselves as a means of providing business systems support primarily due to their technological capability of processing vast amounts of information in a short period of time. Such processing is aided by scientific methods and techniques of business system modelling and design. Consequently, ICT and the solutions generated by them have become essential forms of business systems management support as well as the basis for the improvement of the efficiency of business systems themselves.

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

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.

Dynamic reconfiguration techniques can greatly improve the flexibility and reliability of manufacturing systems. However, different from static reconfigurable systems, system behavior during dynamic reconfiguration processes is quite complex due to possible concurrence of system structure changes and events inside unaltered components. This increases the difficulty in designing and developing dynamic reconfigurable systems. The current paper deals with the analysis and control of dynamic reconfiguration process of manufacturing systems from the perspective of discrete event systems. To this end, the authors improve the reconfigurable timed net condition/event systems formalism by assigning reconfiguration functions with extra permeating time, action ranges, and concurrent decision functions. As a consequence, nondeterministic behavior of a dynamic reconfigurable system during dynamic reconfigurations can be specified, while the system correctness, coherence, and safety during reconfigurations can be guaranteed. A reconfigurable manufacturing plant is used as a running example to illustrate the contribution of this paper.

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...

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.

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.

Artificial Intelligence (AI) systems applications are widespread due to its domain independent characteristics. In this work, an attempt has been made for review on AI applications in Computer Aided Process Planning (CAPP) and manufacturing. Primarily, uniqueness of present review work addressed by analysis of existing review articles. The review work comprise of three main elements; 1. Feature based design, a primary input for a CAPP system, 2. Expert System (ES) usefulness in Process Planning (PP) and manufacturing and 3. Evolutionary approach applications. The review begins with an overview and the use of AI systems in decision making. Research works exemplified for the past three and half decades (1981–2016) are analyzed in terms of feature based modeling, Standards for Exchange of Product Model data approach, ES in PP, scheduling, manufacturing and miscellaneous applications. Role of Evolutionary Techniques (ET) in intelligent system development, execution of PP activities and manufacturing are described. A statistical analysis on existing review articles, number of publications, domain specific articles and percentage contribution of each area are carried out. Finally, research gaps are identified and the possible future research directions are presented.

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.

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.

The use of additive manufacturing technologies in different industries has increased substantially during the past years. Henry Ford introduced the moving assembly line that enabled mass production of identical products in the 20 th century. Currently, additive manufacturing enables and facilitates production of moderate to mass quantities of products that can be customized individually. Additive manufacturing technologies are opening new opportunities in terms of production paradigm and manufacturing possibilities. Manufacturing lead times will be reduced substantially, new designs will have shorter time to market, and customer demand will be met more quickly. This article identifies additive manufacturing implementation challenges, highlights its evolving technologies and trends and their impact on the world of tomorrow, discusses its advantages over traditional manufacturing, explores its impact on the supply chain, and investigates its transformative potential and impact on various industry segments.

Simulation of the business process in manufacturing is considered. The main attributes and stages of the business process are described. For the example of automated machining, the business process is broken down into its components. A logical relation is established between the subprocesses (functions). Their inputs and outputs controlling the implementation of the process are described. A process model of digital manufacturing is developed in the methodology of the IDEF0 standard.

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.

[EN] Universities are one of the fundamental actors to guarantee the dissemination of knowledge and the development of competences related to the Industry of the Future (IoF) or Industry 4.0. Computer Aided (CAX) and Product Lifecycle Management (PLM) technologies are key part in the IoF. With this aim, it was launch a project focused on Manufacturing and partially funded by La Fondation Dassault Systèmes. This communication presents a review on CAX-PLM training, four initiatives already in place in universities participating in the project, the project scope, the approach to integrate with the industrial context, the working method to consider different competence profiles and the development framework., The authors express their gratitude to the other project colleagues and to La Fondation Dassault Systèmes for its funding support.

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.

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 ...

The purpose of this paper is to analyse the integration of discrete event simulation (DES) in operations management manufacturing tools. Due to the movement of the fourth industrial revolution (Industrie 4.0), the integration of manufacturing is a topic constantly discussed in many areas. Moreover, it presents great research and innovation opportunities. To achieve the objective of this study, a search was conducted using the main keywords found in papers related to manufacturing systems and operations management manufacturing tools. Also, academic databases were literature research to identify the keywords relevant to the study added to DES. We considered only articles from the last 8 years. At the end between the search, the integration between tools such as manufacturing execution system, enterprise resource planning, radio frequency identification, core manufacturing simulation data, e-Kanban with DES were analysed. Furthermore, it was observed that the tools cannot always be used separately, but in some cases, these tools should be used jointly to solve problems related to production systems. Another aspect observed was how the data collected in production systems are fed to the DES models. Through, it was possible to analyse an existing gap regarding how the data is used between DES and manufacturing systems, thereby enabling research development in this area.

This paper is aimed at considering supply chain and related data management within an integrated vision of the product lifecycle management (PLM) implemented through the unified approach which is proper to the Industry 4.0 initiative. In particular, with the proposed manufacturing system architecture, decision support tools can use a unified repository fed by a factory replication application, powered by data from the field, even from remote production units. Such data allow to monitor the behaviour of the digital twin of the real machine and produces a digital twin of the real product, incorporating its actual characteristics measured by means of suitable acquiring systems (in the treated example: a 3D laser scanner). Moreover, it is provided a description of the plant technological subsystems that allow to share designing and manufacturing activities across multiple similar units located in remote areas. In this context of virtual enterprise, the supply chain management results as a key factor in enabling a cooperative approach.

As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufacturing industries. Aimed at addressing the above-mentioned two issues, in this letter, a five-stage approach for enhancing and assuring yield is proposed. Also, a smart manufacturing platform- Advanced Manufacturing Cloud of Things (AMCoT) based on Internet of Things, cloud computing, big data analytics, cyber-physical systems, and prediction technologies is designed and implemented to realize the proposed five-stage approach of yield enhancement and assurance. Finally, AMCoT is applied to a bumping process of a semiconductor company in Taiwan to conduct industrial case studies. Testing results demonstrate that AMCoT possesses capabilities of conducting total inspection in production, providing prognosis, and predictive maintenance on equipment, finding the root cause of yield loss, and storing and handling big production data, which as a whole is promising to achieve the goal of zero defects.

Efficiency and quality are major factors contributing to profits in manufacturing systems. Production downtime occurs during commissioning of a new system, adoption of new processes, system faults, or (un)planned maintenance; all of which result in reduced production and profit loss. Current techniques for evaluating change to a manufacturing system rely on simulation and modeling to verify processes, but ignore the physical interactions of the work parts on the system. Implementation techniques to evaluate commissioning focus on identifying issues with the cyber interfaces, ignoring the physical interfaces. To validate the cyber and physical interfaces simultaneously, physical work are sent through the system, resulting in significant costs from scrapped work parts and loss of production time. This research proposes a virtual fusion environment where the physical interfaces between a virtual work part and a manufacturing system can be investigated in real-time, on the physical system, without the...

The supply-demand matching and optimal-allocation of manufacturing resource and manufacturing capability, is always one of the key scientific issues to be addressed and the common objectives to be pursued for various advanced manufacturing systems (AMSs). Especially to the service-oriented manufacturing (SOM) systems (e.g., cloud manufacturing), which is a kind of typical and hot AMSs nowadays , it is extremely difficult to achieve the optimal allocation of large scales of different manufacturing services and the collaboration of manufacturing activities and business among massive manufacturing enterprises (no matter manufacturing service providers and consumers). Thus, under the current environment of social competition and collaboration, the most important challenge is how to integrate a variety of distributed manufacturing resources and capabilities in the form of manufacturing services efficiently and cost-effectively, as well as various demands or manufacturing tasks. In response to this challenge, the concept of supply-demand matching hypernetwork ( Matching_Net ) of manufacturing services in SOM system is put forward firstly in this paper. The proposed Matching_Net is constructed with manufacturing service network ( S_Net ), manufacturing task network ( T_Net ), and hyper-edges between those two networks which are revealing the matchable correlations between each service (supply) and each task (demand). Secondly, by comparing with the input or output information of manufacturing services and tasks from the functional view, the intelligent modeling of Matching_Net is illustrated and divided into those three constituent parts respectively. Finally, the simulation is carried out to validate and verify the proposed models and the modeling method. The model of hypernetwork (i.e., network of networks) is introduced into the supply-demand matching issues in service-oriented manufacturing systems.The concept and model of supply-demand matching hypernetwork ( Matching_Net ) of manufacturing services in SOM system are put forward considering both manufacturing services and manufacturing tasks.By comparing with the input or output information of manufacturing services and tasks, the intelligent modeling flow of Matching_Net is proposed and illustrated.The simulation is carried out to validate and verify the proposed models and the corresponding modeling method.

Nowadays, with the introduction and application of new information technologies in manufacturing, various advanced manufacturing modes and national strategies have been put forward and paid more and more attention, such as Industry 4.0, Industrial Internet, Cyber-Physical System or Cyber Manufacturing, Made in China 2025, Internet Plus Manufacturing, Cloud Manufacturing, etc. For these modes and strategies, how to realize the effective and intelligent supply–demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of service is one of the common issues and aims. In order to provide a uniformed research platform for related researchers both in academic and industry, the concept of manufacturing service SDM simulator (SDMSim) is proposed in this paper. A hypernetwork based architecture for the simulator is designed, as well as its seven key functions and subsystems, including manufacturing service management, manufacturing task management, manufacturing service SDM hypernetwork, manufacturing service SDM problem formulation and configuration, matching and scheduling algorithms/strategies selection and design, statistical analysis, and visualization. It illustrates that SDMSim has the potential to serve the users of manufacturing service provider, manufacturing service consumer, manufacturing service operator in the field of SoM, as well as the related researchers.

Modern manufacturing industry calls for a new generation of production system with better interoperability and new business models. As a novel information technology, Cloud provides new service models and business opportunities for manufacturing industry. In this research, recent Cloud manufacturing and Cloud robotics approaches are reviewed. Function block-based integration mechanisms are developed to integrate various types of manufacturing facilities. A Cloud-based manufacturing system is developed to support ubiquitous manufacturing, which provides a service pool maintaining physical facilities in terms of manufacturing services. The proposed framework and mechanisms are evaluated by both machining and robotics applications. In practice, it is possible to establish an integrated manufacturing environment across multiple levels with the support of manufacturing Cloud and function blocks. It provides a flexible architecture as well as ubiquitous and integrated methodologies for the Cloud manufacturing system. A ubiquitous manufacturing system based on Cloud manufacturing.Industrial robots utilized as implementation applications in the Ubiquitous manufacturing environment.Systematic analysis and guidelines for adpoting advanced manufacturing technologies.Multiple deployment models for private, community and public scenarios.Proposed system is implemented and evaluated in the near-real manufacturing environment.

Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④ life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.

With the bottlenecks of traditional manufacturing technology becoming increasingly prominent, manufacturing industry need to adjust and upgrade the industrial structure, and intelligent manufacturing is the future development direction of manufacturing industry. According to the characteristics of complex, open system hierarchy structure and distributed multi agent of intelligent manufacturing, in order to solve the problem of information integration and collaborative processing of the organic elements of intelligent manufacturing collaborative perception process, it is necessary to give the autonomy of intelligent manufacturing entities to make it to form a fully functional agent, and these agents are connected to the communication network as a network node, so as to realize the data sharing and collaborative awareness processing function between the intelligent manufacturing body. Therefore, this article build the intelligent manufacturing system based the multi Agent and Storm-Hadoop distributed cluster framework through the research and analysis of distributed artificial intelligence and distributed cluster mechanism, and provides a collaborative framework based on multi Agent and Storm-Hadoop for information integration, data sharing, collaborative processing of collaboration awareness problem for multi manufacturing subjects of intelligent manufacturing. The simulation results shows that the cooperative sensing framework proposed in this paper can provide an effective information, data and knowledge processing mechanism and method for realizing multi subject intelligent manufacturing collaborative awareness.

A prime objective of this project is to develop an Automatic Non-Destructive Testing System for a shaft of the wheel axle of a railway carriage. MASTER OF ENGINEERING (MAE)

This chapter discusses a number of issues relating to scheduling in the flexible manufacturing system (FMS) domain. It focuses on the performance parameters that are appropriate for a FMS and FMS technology and the issue of flexibility. The chapter describes both static and dynamic scheduling along with a number of methods for dealing with these scheduling problems. It explores a new approach to these issues called mix oriented manufacturing control. One of the main features of an FMS is its flexibility. This term, however, is frequently used with scarce attention to current research studies, whereas its characteristics and limits should be clearly defined according to the type of FMS being considered. Considering the high level of automation and cost involved in the development of an FMS, all development phases of this technology are important in order to achieve the maximum utilization and benefits related to this type of system.

The increasing growth of digital technologies in manufacturing has provided industry with opportunities to improve its productivity and operations. One such opportunity is the digital thread, which links product lifecycle systems so that shared data may be used to improve design and manufacturing processes. The development of the digital thread has been challenged by the inherent difficulty of aggregating and applying context to data from heterogeneous systems across the product lifecycle. This paper presents a reference four-tiered architecture designed to manage the data generated by manufacturing systems for the digital thread. The architecture provides segregated access to internal and external clients, which protects intellectual property and other sensitive information, and enables the fusion of manufacturing and other product lifecycle data. We have implemented the architecture with a contract manufacturer and used it to generate knowledge and identify performance improvement opportunities that would otherwise be unobservable to a manufacturing decision maker.

IntroductionThis paper presents the development stages of a model of a distributed system in which the primordial abstract elements are the process and the connection (link) between processes. This paper deals with the problem of communication between the processes within the distributed system, with the concept of message exchange between processes, and also presents the problem of distributed agreement.This paper presents the importance of two concepts: the logical time of a distributed system and the causal order of messages. All of these concepts are necessary to define a model of a distributed system in which some processes have the role of management of the operations carried out by other processes which only have an execution role.After presenting of the processes of the system components, there is an abstraction of the exchange of messages between the processes of the distributed system. In the next step, an abstract model of a distributed system is proposed and analyzed, in which there are modelled several worker-processes controlled by a manager-process, which runs on different host than the worker's. The model described here forms the basis for the development of a distributed system tolerant of faults, which uses the replication of operations.Based on the abstract model of the distributed system previously presented, a replication model is proposed, for operations and data based on the structure of a distributed system containing several worker-processes. After the presentation of a list of objectives for the fault-tolerant system, a logical separation of management operations from the execution operations and data storage is proposed.Web services are increasingly used in distributed systems, applications and services of outmost importance. For distributed systems, the architecture oriented on services, SOA - Service Oriented Architecture is already accepted as being the architecture able to interconnect applications running on different operating systems and facilitates the complex interactions between autonomous systems and heterogeneous ones, either within organizations or between organizations in relations of B2B (business-to-business) type. Web services allow software applications found in different organizations (company) to interact with each other, even if those organizations use different hardware systems, or operating systems and even different programming languages. Web services are able to standardize and improve the effectiveness of business activities on the Internet by automatically invoking operations which otherwise should be invoked manually by a human operator. So, Web services allow direct interactions between computers located in different organizations.Based on Web services it is possible to build technologies to support interoperability with Decision Support Systems. Important aspects for healthcare systems are described in [1], where the proposed technology is meant to raise the interoperability degree between different medical information systems is described. Web Services ensure communication between medical units automatically with minimum human intervention, which is an essential requirement when designing applications for users in the medical domain.The design of applications based on Web services can take into account important aspects in creation of new levels of abstraction, giving a special importance to scalability and adaptability. Due to a growing importance given to resource-oriented Web services, the authors must implement the principle of organizing the algorithmic resources in multi-level hierarchy and the principle of uniform access to resources. In order to implement these principles, an architectural model of Web applications is promoted in [2].2Availability and Reliability of Distributed SystemsThe failures and damages of web applications may lead to incorrect processing or even to the system failure in the business of e-commerce type, e-banking or other systems based on transactions. …

A novel lifecycle framework for managing manufacturing resources within a company is presented to support the timely, systematic identification of technological need for action. In contrast to the established bathtub curve, the equipments’ age, maintenance costs, downtimes, and technical condition are considered quantitatively taking uncertainties into account. The developed methodology therefore comprises contingency analysis and applies a dynamic set of fuzzy rules for classifying the cycle-stage of multiple manufacturing resources. Integrating these functionalities within one holistic framework, this approach far exceeds existing methods for proactively managing the manufacturing resource lifecycle. Cooperating with one of the leading manufacturer of commercial vehicles, a database comprising 221 relevant manufacturing resource datasets was built as a foundation for statistical analysis. Besides offering general recommendations facilitating a proactive management approach, the developed lifecycle model concentrates further activities on elements that show major improvement potential.

Recently, sustainable development has obtained increasing attentions from governments, industry, and academia owing to the limited natural resources. In the area of energy consumption, manufacturing accounts for a major portion of the total energy usage in industry. There is a clear necessity for energy-efficient manufacturing by optimizing manufacturing activities. Condition monitoring is the technology that provides runtime information for optimization. This paper aims to provide a better understanding of past achievements and future trends of condition monitoring towards energy-efficient manufacturing. Since there are a variety of sensors and technologies that can be used for condition monitoring towards energy-efficient manufacturing, this paper divides manufacturing activities into three levels, namely unit process level, shop-floor level, and supply chain level, and summarizes and discusses the sensors and technologies required to enable energy-efficient manufacturing on each level. With the advancement of technology, condition monitoring shows the characteristic of intelligence. Intelligent sensors that can be applied to condition monitoring in energy-efficient manufacturing are also reviewed. This paper can be helpful to manufacturers who are willing to improve energy efficiency in own manufacturing practice.

Information integration and interoperability among different design and manufacturing application software systems are important in smart manufacturing implementation and product life cycle management. Information models are foundational and critical to the specification of common terms, interfaces, and software architectures for information interoperability. The manufacturing process is one of the key processes in a product life cycle. This paper presents a methodology of using the metamodels as the modeling basis in manufacturing process information modeling. A manufacturing process information metamodel (MPIMM), which consists of object classes, relationship classes, and relationships, defines the information and relationships of processes, operations, resources used, and product/part manufactured of manufacturing process planning. The MPIMM is built by unified modeling language (UML) while the semantics of the process elements and their attributes are specified referencing the process specification language. A four-layer framework is proposed to construct manufacturing process information model (MPIM) following the rules of metamodeling, and including meta-metamodeling, manufacturing process information metamodeling, and manufacturing process domain information modeling; thus, manufacturing process plan data can be represented. The UML profile extension mechanism realized by defining Stereotype, TaggedValue, and Constraint is used in extending MPIMM to MPIM. A case study of applying MPIM in the product life cycle data management system for port hoisting equipment manufacturing is provided to demonstrate and verify the modeling capability and efficiency of the proposed methodology. This work provides a systematic and standardized modeling methodology and information expression mechanism for manufacturing process information.

This paper presents an effective product design and manufacturing process based ontology for manufacturing knowledge reuse. While a number of related efforts exist in the literature, there lacks a granular, interconnected product design and manufacturing process based ontology that can lead to greater industry adoption and knowledge reuse. In particular, the proposed ontology leverages an established industry standard to connect product design and manufacturing process knowledge in a logical and effective manner. The resulting effort is a general ontological framework that can be widely employed by the manufacturing industry. Additionally, the feasibility of implementing the ontology enabled knowledge reuse framework is demonstrated through a real-world case study.

Many developed countries are making various efforts to innovate their own manufacturing industries, through initiatives such as Manufacturing innovation 3.0, Industry 4.0, and Manufacturing 2025. Innovation in the manufacturing industry, represented by the so-called “smart factories,” is being developed through the latest technologies such as Internet of things (IoT), cloud, and big data. However, as the application of these technologies requires a lot of cost and time, small and medium-sized enterprises (SMEs) are often hampered in their efforts to take full advantage of them. For an enterprise that operates a manufacturing information system, the integrated management of information between systems is essential to the application of a new technology. If the enterprise lacks the relevant experts, it will have difficulty applying a new technology in the field. This study suggests the application of a cloud-based applications’ integration and operation platform (AIOP) in order to resolve those problems. The AIOP must accept a large volume of data at IoT-based manufacturing fields, interconnect between manufacturing fields and AIOP, and provide application contents in the form of service. The suggested study contents are applied to a company that produces electric components using plastic injection molds to verify their effects. It is expected that this study can be used as a reference model for applying smart factory technologies to other SMEs in the future.

The next generation of manufacturing will be focused of utilizing lean tools within production industries. Internet of Things (IoT) has made its progress evolvement into the manufacturing industry and has led to improved control when implemented to monitor manufacturing resources. Smart production systems can integrate the virtual and physical worlds and accomplish improved transparency of production processes. These smart production systems will go beyond the traditional means of collaboration to move companies from good to great. In this study a visual management system was developed for resource management research for the Stellenbosch Technology Centre's Laboratory for Advanced Manufacturing (STC-LAM). This system gathers shop floor data and display it in a dashboard. Results and future work was also discussed.

The paper provides a study of the manufacturing system flexibility principles. Flexibility is conceived as a manufacturing system ability to transfer to the manufacture of new products that are characteristic for the multipart, small-, or medium-batch production. Product, operation, and route flexibility, as well as the starting-up and development flexibility assurance methods are described. The manufacturing systems classification based on the combination of different flexibility forms and its level is provided. In order to choose a system flexibility ensuring option, it is proposed to divide the whole set of manufacturing systems into classes, for each of which a dimensionless weight coefficient is introduced taking into account the degree of utilizing the capabilities of processing units and machines, and to use geometrical average of the volumetric equipment flexibility and the part geometry flexibility for the flexibility assessment.

Currently many multinational companies have manufacturing plants with similar processes, but they suffer from barriers to share knowledge. Knowledge Management (KM) techniques may help to capture and reuse knowledge generated during processes execution. Literature shows Case-Based Reasoning (CBR) as a technique for implementing KM, and Product Lifecycle Management Systems (PLM) as the main data repository of Product-Processes-Resources data. This paper proposes a Continuous Improvement Process (CIP) approach to facilitate the capture and reuse of knowledge, integrating CBR and PLM technologies. It aims supporting production technicians during the resolution of manufacturing daily problems directly at shop floor level.

The cloud manufacturing paradigm has been known as one of the best solutions for today's competitive manufacturing environments. This paradigm insists on using a manufacturing service oriented approach for fulfilling the manufacturing enterprise resource management by the access to global availability of manufacturing services. The proposed service oriented approach covers a wide area of manufacturing resource management from infrastructures to platforms and applications. However, due to wide global distributions of manufacturing clouds, the service oriented architectures which are designed in the clouds cannot communicate with each other. This is mainly due to concentration of cloud manufacturing paradigm to the application of the detailed manufacturing resource acquisitions through a services oriented approach. To solve this problem a wide area of researches have been established which can be named as the interoperability of manufacturing clouds. In this paper, a novel solution for enabling the interoperability among the manufacturing clouds has been proposed. This solution applies the service oriented approach of the cloud manufacturing to enable the interoperability services. These services are proposed by Information Technology related service provider companies. These firms can provide the different manufacturing clouds with the interoperability services. These services are acquired by the manufacturing cloud members to communicate with other cloud members. The proposed solution enables two important mechanisms. The first mechanism uses service integration functions for enabling an integrated service data model and the second mechanism uses service mapping functions. Finally, the paper has elaborated the architecture of the platform and outlined future works.