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Review and Perspectives on the Research and Industrial Applications of Manufacturing Systems Engineering in Korea for 40 Years Byoung Kyu Choi.Kwan Hee Han.Cha Soo Jun.Chul Soo Lee.Sang Chul Park Department of Industrial and Systems Engineering, KAIST Department of Industrial and Systems Engineering, Gyeongsang National University Department of Mechanical Engineering, Sogang University Department of Industrial Engineering, Ajou University

Flexible Manufacturing Systems provide a fast reaction possibility to the changes in production conditions. For achieving a quick responsibility of production, it is necessary to leave the traditional form of production process planning. Nowadays, most of the products are designed by using CAx software. The product design of 3D model contains not only the geometrical data of product, but it may contain a part of the process plan and technological data as well.

Manufacturing industry has been experiencing dramatic evolutions in last three decades. The boundaries of a manufacturing system are extended from factory towards various types of network relationships. The missions of manufacturing system are transformed and redefined. The output scopes are also expanded into servitisation from traditional product-focused concentration. This chapter, based upon recent observations and analysis in both globalised production and engineering networks, introduces an evolutionary picture of manufacturing system with its engineering function systems. The chapter demonstrates a theoretical framework that highlights both globalised manufacturing and engineering networks have not only very unique structures and configurations but also distinguished strategic capabilities. The chapter suggests that the classical manufacturing strategy and system design theories should be adapted into a contingency strategy to respond to the manufacturing evolutions and treat different systems interdependently. The chapter also seeks to clarify the two distinguished concepts and systems—production and engineering—based on the case analysis, and tries to achieve a synergy between those two different value creation systems on the global platform. The chapter suggests a new conceptual framework for further research on new manufacturing evolution and contingency strategy.

The proper functioning of manufacturing systems can be ensured by conducting evaluations in terms of quality, productivity, leanness, flexibility, and reliability, among others. Then, after such evaluations, it is important to apply methods and strategies to improve the aforementioned aspects. In this chapter, we define the concept of evaluation and discuss its different stages. Also, we discuss the three aspects upon which manufacturing systems have been assessed through the years.

Cyber-physical manufacturing systems are a new paradigm of manufacturing systems that integrate cyber systems and physical systems to aid smart manufacturing. Cyber-physical manufacturing systems can improve agility and responsiveness and guarantee the quality of products to meet the market requirements. Meanwhile, cyber-physical manufacturing systems also become susceptible to cyber-attacks. In order to improve the trustworthiness of cyber-physical manufacturing systems in the dynamic modeling phase, a cyber-physical manufacturing system formal model based on object-oriented Petri nets is presented from the perspective of multi-agent systems. Some mathematical methods and supporting tools of Petri nets can be utilized to analyze, verify, and validate cyber-physical manufacturing system formal model. To defense the malicious software spreading in cyber-physical manufacturing systems at run-time, a spreading dynamics model is proposed, and its dynamic behaviors are analyzed. A hybrid bifurcation control method is designed to control the Hopf bifurcation that is caused by the malicious software spreading. The simulation results show that the hybrid bifurcation control method can make cyber-physical manufacturing systems generate the expected dynamic behaviors and guarantee the trustworthiness of cyber-physical manufacturing systems at run-time.

The implementation of mass customization into a manufacturing enterprise brings with it, among other things, the introduction of new technical applications, software, through which the manufacturer gets closer to the consumer and on the other hand he can simulate the production process. Through a well-managed simulation, it can be prepare for future “unpredictable” situations in the production process and make the whole of the production economically as well. The aim of this paper is present the possibility of using simulations and managing the simulation of manufacturing processes during the study at the Faculty of Manufacturing Technologies with a seat in Presov, of the Technical University of Kosice. This paper presents a new approach to the subject of designing manufacturing systems and the management economy. The students of faculty teach the simulation problematic during their study field. Used simulation processes make changes to technology and manufacturing operations in a virtual environment. This study presents the students' view of the problem and uses the principle of a case study.

Reliable, durable, and scalable fabrication of garment-integrated electronics are the major barriers to larger-scale garment integrated applications for wearable technology. This research emphasizes developing hybrid manufacturing processes leveraging the benefits of the traditional labor-intensive apparel industry and the automated electronics industry. The proposed manufacturing process uses the existing technology, resources, and operation sequence of a typical apparel manufacturing facility. A simulation approach is utilized to increase the overall efficiency of the manufacturing method.

On the example of operations for manufacturing the “Axis” detail, the effectiveness of statistical control of the technological production process for the “Nalchik plant of highvoltage equipment” is demonstrated. Specific recommendations on the effective quality assurance of parts are given.

The problem of rational organization of the process of design and technological preparation for manufacturing with application of semantic Petri-Markov nets is considered. The method of parallelization of the stages of the works within design and technological preparation for manufacturing based on their semantic relations.

(a) Situation faced: A family-owned manufacturing company recently went through the transfer of management from the older to the younger family generation. A number of problems were uncovered during this process, such as prevalence of tacit knowledge, an inefficient decision-making process, outdated IT system support, and an urgent need for certification of production processes according to quality-assurance standards (ISO 9001). Each of these problems required thorough documentation of the as-is business processes in the organization to guide their improvement. (b) Action taken: To ensure that the created process models serve as a valid communication medium, the company’s process landscape was created during an initial workshop between the executives and external BPM consultants. Then the information on processes in the company’s various departments was gleaned from semi-structured interviews with the department employees. At the same time, process weaknesses and potential improvements were derived and discussed with the functions’ management. The succeeding depiction of the to-be process framework was achieved with the help of the icebricks modeling method and the corresponding software tool, which is a lightweight, standardized approach to ensure high quality of process models. (c) Results achieved: During the modeling phase of the project, external BPM consultants documented the process landscape, thereby explicating the company’s knowledge and good-practice processes. The process landscape served as basis for well-informed decisions regarding the implementation options of a new ERP system, which was introduced on time and on budget in the second phase of the project. The ISO 9001 recertification of production processes was achieved in the third project phase with the help of the process documentation that had been created. (d) Lessons learned: Simply deploying process models on the company’s intranet platform does not necessarily lead to their desired comprehension and use. All employees have to be trained that process models are a means of communication and are never finalized, a notion that also applies to continuous process improvement. Process owners must be defined so they take responsibility for adjustments to the process environment beyond the project’s lifecycle, but such responsibility is not solely that of a project manager. Furthermore, the project demonstrated the appropriateness of the icebricks modeling method for the manufacturing domain, although it was originally designed for the retail industry.

Prototyping is an important part of the product development process, especially for the design of the manufacturing systems in small-to-medium enterprises (SMEs). Practitioners in resource-constrained settings face unique challenges when prototyping in these contexts. This work examines the methods, constraints, and impacts on design outcome of prototypes in manufacturing SMEs in resource-constrained settings through a case study. Observations and information were gathered through a site visit and interviews with the engineers at the partner organization. One of the important findings of this case study is that the main intent of prototyping is to develop high-fidelity, functional prototypes through simple prototyping, iteration, and the emphasis on physical prototypes. The overarching resource constraints on achieving this prototyping intent were found to be a variance in prototyping inputs, limited access to appropriate manufacturing capabilities, and limitations of modeling predictions.

Efforts to reduce product environmental impacts such as energy consumption and carbon footprint have received attention for many years, often driven by consumer pressure on companies to produce more environmentally friendly products. As the next generation of engineers who will take responsibility for advancing the sustainability of products, processes, and systems, engineering students need to become more familiar with the concepts of sustainable product design and manufacturing. Yet, educators are disadvantaged in training these students, and tools are deficient in assisting product sustainability assessments for manufacturing decision making by other non-experts. A manufacturing analysis module is introduced, which was developed under collaborative research titled, Constructionism in Learning: Sustainable Life Cycle Engineering (CooL:SLiCE). This CooL:SLiCE manufacturing analysis module provides an opportunity for non-expert students and engineers to investigate the impacts of product design changes on manufacturing processes and supply chain network configurations, e.g., selection of upstream processes, transportation routes, and transportation modes, from environmental responsibility perspective. One popular consumer product, a multicopter, is selected to demonstrate the module. The production of three hexacopter components are evaluated: the upper shell, lower shell, and propeller. The manufacturing analysis module enables non-experts to gain a better understanding of sustainable product design and manufacturing.

Multistage manufacturing processes (MMPs) are networked manufacturing systems consisting of multiple operational stations that have characteristics of mechanical and control systems. Common challenges in the design of MMPs are the selection of sensors and tools as this not only affects the dimensional quality of the finished product, but also influences the computational complexity in representing and analyzing the problem. Imprecise or incomplete information results in uncertainty in the models used to represent the MMP and limit the use of traditional design approaches. In this paper, an exploration method for the concurrent design (CDEM) of MMPs under uncertainty is presented wherein the attributes of tools and sensors are treated as design variables, thereby allowing flexibility in a design process. The proposed method is illustrated using an example of automotive panel stamping process. Our focus in this paper is on the method rather than the results per se.

Growing competition and increasing market dynamics force manufacturing companies to increase flexibility in their manufacturing systems. Thus, new approaches, which exceed the limitations of traditional manufacturing systems, are needed. This also requires rethinking the simulation modeling behind those systems. However, simulation software tools used in industry, referred to as industrial grade software tools, and actually used simulation models are not sufficient for this purpose. Therefore, this paper aims at building up an agentbased simulation model of a flexible manufacturing system in an industrial grade software tool. For that aim, a general three-step approach for implementing an agent-based logic into an industrial grade discrete-event simulation tool is presented. An exemplary application is based on a matrix-structured manufacturing system that provides by its structure and by the individual acting entities a flexible and scalable approach without a fixed cycle time. The example is modeled within the industrial grade discrete-event simulation software Tecnomatix Plant Simulation and compared to a reference model, recreating the same results with small deviations.

This paper presents an approach to enabling ontology-based web service integration for flexible manufacturing systems based on industry 4.0 demonstration production line. The mass personalized customization requires increased agility and flexibility in manufacturing systems to adapt incessant changes in manufacturing environments and requirements. The fundamental for this method lies in the incorporation of knowledge expressed by ontologies concerning order, products, industrial equipment, manufacturing process, event and service. It is a significant method to use ontology for representing manufacturing knowledge in a computer-interpretable way. This knowledge model can be used by automated decision-making to configure the control component that monitors and harmonizes the whole manufacturing system. The semantic model of the flexible manufacturing systems can be automatically updated based on event notifications sent by the specialized web services. The architecture and knowledge model have been demonstrated in a pilot case named industry 4.0 demonstration production line and can be applied to other flexible manufacturing systems.

With the rapid development of new information technology, industrial change of intelligent manufacturing is arising around the whole world, which alters the manufacturing process, industrial chains and division system. Most developed countries speed up laying out industry and make new national strategies to revitalize manufacturing in order to promote the development of intelligent manufacturing and rebuild competitive advantages of manufacturing. ‘China Manufacturing 2025’ aims at promoting manufacturing transformation and upgrading which focuses on intelligent manufacturing. In the following stage, pilot demonstration cities should be chosen which are concerned with new intelligent manufacturing modes such as discrete intelligent manufacturing, process intelligent manufacturing, networked collaborative manufacturing, mass customization, remote operation and maintenance service in order to advance development levels and boost the upgrading of industry.

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

This article introduces a project in which researchers from across different disciplines including semantic web, materials science, manufacturing engineering, and software engineering collaborate to develop ontologies and a data management system for additive manufacturing processes.

Today, the manufacturing industry is under pressure to be able to rapidly come up with innovative designs and produce them in much shorter timeframes in order to keep up with growing customer demands and quickly gain new business. One of the ways used to achieve shorter time to markets for new product developments is by using design for manufacturing (DFM) methods to reduce time and energy going into resolving manufacturing based defects. It is also more vital in today’s manufacturing industry to make use of DFM methods much earlier in the product development lifecycle in order to prevent potentially known quality defects from happening and save on costs associated with late design changes. This requires enabling a more systemic feedback cycle of production data for the creation of DFM knowledge repositories as well as overcoming some wider knowledge sharing barriers across the organization. This investigation focuses on how the communications of manufacturing knowledge from production data is affected by factors within the overall organization. The investigation is one of the few that consider the influence of organizational factors on the communication of engineering knowledge as well as the related knowledge sharing barriers. The project is carried out empirically with a large UK based aerospace manufacturing company by gathering data primarily from observations and interviews. This paper presents the findings followed by discussions for improving DFM Knowledge Management in the aerospace industry.

Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why’s, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.

Life cycle assessment software packages such as SimaPro, GaBi, and Umberto have become well-established tools for conducting environmental impact analysis. However, applications for broader sustainability assessment are limited. Recent research has developed an information modeling framework to compose models of unit manufacturing processes for sustainability assessment and has led to the definition of unit manufacturing process information modeling concepts. An engineer can use the framework to conduct manufacturing system-level sustainability assessments by composing models of unit manufacturing processes. Assessment results can aid engineers in selecting the superior manufacturing process flow for a given product. To demonstrate usefulness of the information framework, a prototype desktop application has been developed. The application was implemented in Windows Project Foundation (WPF) using C# as the coding language to create a graphical user interface. Mathworks MATLAB serves as the calculation engine. Unit manufacturing process models follow the framework and are read by the application, which produces a sustainability assessment for the manufacturing process flow. A manufacturing process flow for an automobile-like metal product acts is used to demonstrate the software application.

Dramatic advancements and adoption of computing capabilities, communication technologies, and advanced, pervasive sensing have impacted every aspect of modern manufacturing. Furthermore, as society explores the 4th Industrial Revolution characterized by access to and leveraging of knowledge in the manufacturing enterprise, the very character of manufacturing is rapidly evolving, with new, more complex processes and radically new products appearing in both the industries and academe. As for traditional manufacturing processes, they are also undergoing transformations in the sense that they face ever-increasing requirements in terms of quality, reliability and productivity, needs that are being addressed in the knowledge domain. Finally, across all manufacturing we see the need to understand and control interactions between various stages of any given process, as well as interactions between multiple products produced in a manufacturing system. All these factors have motivated tremendous advancements in methodologies and applications of control theory in all aspects of manufacturing: at process and equipment level, manufacturing systems level and operations level. Motivated by these factors, the purpose of this paper is to give a high-level overview of latest progress in process and operations control in modern manufacturing. Such a review of relevant work at various scales of manufacturing is aimed not only to offer interested readers information about state-of-the art in control methods and applications in manufacturing, but also to give researchers and practitioners a vision about where the direction of future research may be, especially in light of opportunities that lay as one concurrently looks at the process, system and operation levels of manufacturing.

In a service-oriented networked manufacturing (SONM) environment, geographically distributed manufacturing resources are encapsulated as various manufacturing services. These manufacturing services release via the Internet and can provide services on the demand of manufacturing tasks. Usually one manufacturing task needs several different services belonged to different organizers to work together. Hence, effective cooperation among services is the foundation of the efficient operation of SONM. In this paper, a bipartite network model is presented to describe the relationship of two different kinds of nodes in SONM, and also is projected as a weighed network for further exploring the behaviors of service nodes. Furthermore, an agent-based model is built for modeling the interactive behaviors of service nodes in a cooperative network and an agent-based simulating system is developed with Repast. The simulation results show that the emergence of cooperative behaviors among service nodes is related to both the cost of cooperation and initial trust of services in the SONM environment.

We provide a cloud manufacturing based manufacturing planning framework for small and medium-sized enterprises. Manufacturing planning is conducted by separate units in the cloud instead of in corporations or manufacturing platforms. Disorders can be removed by the adoption of our newly-introduced units. To retain the workability of our new framework, three assumptions are imposed. A concrete case on process planning and scheduling is used for illustration of the necessity of our assumptions and operational mechanism of our design. Finally, a preliminary discuss on how intellect resources as well as small and medium-sized enterprises are involved to create a sustainable environment for small and medium-sized enterprises is placed.

A Cyber-Physical Manufacturing Cloud (CPMC) is an Internet-of-Things (IoT) enabled manufacturing framework that facilitates monitoring and operation of manufacturing machine tools remotely through the integration of IoT, cyber-physical systems, cloud computing, and advanced manufacturing technologies. To achieve this, it is necessary to develop a communication method of manufacturing services over the Internet in order to access and manage manufacturing resources from the cloud. This paper presents a semantic ontology based Internet scale communication method of manufacturing services named Machine Tool Communication (MTComm) in a CPMC to enable manufacturing with various physically connected machines from geographically distributed locations over the Internet. This paper presents design of an agent-adapter architecture of the communication method of exchanging manufacturing services across Internet, a semantic ontological representation of manufacturing machines and services, and specification of the ontology based MTComm method. It is the first Internet scale manufacturing service communication method of enabling manufacturing operations over the internet in addition to monitoring manufacturing services. MTComm is a significant improvement over the MTConnect, a widely popular communication method of manufacturing services which allows only monitoring machine tools over the Internet. It is implemented and tested in a testbed of the CPMC to analyze its performance in multiple manufacturing application scenarios. The experiments demonstrate excellent feasibility of monitoring and performing manufacturing operations over the Internet.

An electronic version of the final, peer-reviewed manuscript upon acceptance for publication made publicly after an IEEE embargo period of 24 months from the date of publication. Link to publishers version:http://doi.org/10.1109/ICITM.2017.7917904 In order to survive in this competitive and ever changing market, manufacturers have to improve and enhance the competitiveness, flexibility, responsiveness and sustainability with the application of the cutting-edge technologies and innovative management methods. New concepts, i.e., Intelligent manufacturing, flexible manufacturing, agile manufacturing, network manufacturing, green manufacturing and Industry 4.0, etc., have been proposed and developed in recent years based upon the newest and most advanced manufacturing technologies and Information and Communication technologies (ICT). This paper presents a new concept: Small-scale Intelligent Manufacturing System (SIMS), and the comparison with previous concepts and the benefits of SIMS are discussed in this paper. Different from the previous research works which mainly emphasize the technological integration for improving the flexibility and intelligence of an individual manufacturing system, this paper, however, focuses on and discusses the supply chain problems arisen from a holistic perspective. The features of the supply chain for realizing small-scale intelligent production and responsive distribution are discussed, and the limitation and future works are also discussed and suggested latter in this paper.

Modern enterprises tend to grow fast and at a global scale. Access to world-wide manufacturing resources could help address this challenge. High-level of automation and customization in the manufacturing environment enables production of various products and sub-products by different collaborating factories. Cloud manufacturing is a concept inspired by the Cloud Computing paradigm. It assigns the manufacturing systems, machines, products and processes to the appropriate “manufacturing resources” in such a way that users can utilize these resources in order to program and execute the production of custom products. This paper presents the architectural approach of VISETAK CM, which takes advantage of the vertical integration of manufacturing processes through the utilization of multi-agent systems that coordinate the production at MES level. VISETAK CM exposes the manufacturing resources to a cloud manufacturing platform where users can design their own manufacturing process.

The manufacturing enterprises are currently under great pressures to shorten innovation cycles, grow importance of high-efficiency manufacturing and accommodate new products by building more factories. Discrete manufacturing sectors face a huge array of problems because of its specificity. Knowledge representation is one of the most highlighted troubles. In this case, ontology is a significant knowledge model for representing discrete manufacturing knowledge in a human and machine interpretable way. This knowledge can also be used for automatic design-making to reconfigure the control software that supervises and coordinates the production process of discrete manufacturing. This paper addresses the important issues in developing specific ontology for discrete manufacturing. A generic ontology named DeMO is developed considering all the aspects about product from customized order to resulting production.

Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data are used varies based on the function/role in the product lifecycle that is interacting with the data. Holistically, the data across the product lifecycle must be considered an unstructured data set because multiple data repositories and domain-specific schema exist in each phase of the lifecycle. This paper explores a concept called the lifecycle information framework and technology (LIFT). LIFT is a conceptual framework for lifecycle information management and the integration of emerging and existing technologies, which together form the basis of a research agenda for dynamic information modeling in support of digital-data curation and reuse in manufacturing. This paper provides a discussion of the existing technologies and activities that the LIFT concept leverages. Also, the paper describes the motivation for applying such work to the domain of manufacturing. Then, the LIFT concept is discussed in detail, while underlying technologies are further examined and a use case is detailed. Lastly, potential impacts are explored.

Additive Manufacturing (AM) offers capabilities for producing accurate features in a genuine three dimensional mode in a growing number of technological disciplines, such as regenerative medicine, medical devices, bionics, micro- and opto-electronics. Biochips, MEMS, micro-fluidic devices, photonic crystals, scaffolds for tissue engineering and carriers for drug-delivery devices are some common applications of growing interest. This chapter critically discusses five AM methodologies that were developed initially for deployment in general manufacturing applications and that have demonstrated promise for manufacturing at the micro/nano scale: photopolymerisation processes, powder bed fusion processes, 3D printing via binder jetting, 3D printing via material jetting, filament deposition.

In China’s new normal, a strategy named “Made in China 2025” was proposed to speed up the manufacturing’s transformation and upgrading. The hardship of China’s manufacturing industry transformation and upgrading are the limit capability of independent innovation, the complexity and variability for competitive environment and the existence of institutional hurdles. At present, China is facing the powers of the industry from manufacturing to major, The paper will use factor analysis method to construct the evaluation index system of innovation-driven ability of Chinese manufacturing industry, and established evaluation model of innovation-driven ability of Chinese manufacturing industry. Based on the analysis of the model, the paper will propose the innovation driving strategy to speed up manufacturing transformation and upgrading.

Fit manufacturing and Green manufacturing are a new trend principle and concept. They are getting popular in industrial. This paper is identifying the impact between Fit manufacturing and Green manufacturing. Besides Fit manufacturing, Lean manufacturing, Agile manufacturing and Sustainable manufacturing gives big impacts to Green Manufacturing. On top of that, this paper also discuss the benefits of applying Fit manufacturing and Green manufacturing in industrial as well as environment. Hence, applications of Fit manufacturing and Green Manufacturing are increasing year by year.

The complex structure of flexible manufacturing systems and rapid changes currently taking place with the evolution of the market and increase product diversity involves a detailed analysis of manufacturing processes. Manufacturing systems analysis can be done using methods based on analytical models and simulation. By applying these techniques one can view, analyze and forecast the performance of a dynamic system or of a new implemented system. Performance is one of the main factors affecting the design, development and configuration of any flexible manufacturing system. In this context, the present research will be directed to flexible manufacturing system structure analysis, to assess its performance, as well as simulating and validating these systems. It will generate and analyze key performance parameters, for the designed system by the use of specific software. Thus, it will study: the influence of the initial conditions or parameters on the behaviour of the flexible manufacturing system, the influence of the developing commands and system states on output or the influence of changes in the system structure on its behaviour.

Additive Manufacturing (AM) technologies allow developing and manufacturing very complex shaped parts and functional products with a high level of customization, being a great alternative to Traditional Manufacturing (TM) methods like injection molding, die-casting or machining. Due to the importance of cleaner production in the field of manufacturing processes, sustainability of AM processes needs to be assessed in order to make easier its acceptance and implementation in the industry. Furthermore, the manufacturers can improve their competitiveness and profitability by considering the ecological aspects during the manufacturing step of a product. This paper gives a survey on sustainability issues related to AM.

In order to achieve systematic change in pursuit of sustainable manufacturing, both a strategic long-term perspective employing methods from future studies and a concrete implementation of the knowledge gained in sustainable business models are necessary. In this chapter, the concepts and exemplary methods for sustainable business model innovation are introduced with a special focus on sustainable manufacturing. Circular Economy-based business models and Product Service Systems are explained as examples of sustainable business models, along with a deduction of sustainability factors for both examples. The fruitful combination of future studies and sustainable business model development is illustrated in the example of a so-called living factory, a modular and adaptive production environment which integrates aspects of Circular Business Models and Product Service Systems.

Current trends in production e.g. the shift to mass personalization lead to significant changes in manufacturing IT. The traditional automation pyramid is dissolving and manufacturing IT is moving towards service-orientation and app-orientation. To support especially small and medium-sized enterprises (SMEs) in mastering this challenge, an appropriate IT infrastructure and secure cloud platform have been developed. Based on this cloud platform, apps have been developed which provide the interface between humans and manufacturing IT as well as the integrated cyber-physical systems. Challenges concerning app development for manufacturing environments are illustrated using the example of an app collecting sensor data and sending this data to a cloud service for further processing.

Recent happenings are surrounding the manufacturing sector leading to intense progress towards the development of effective distributed collaborative manufacturing environments. This evolving collaborative manufacturing not only focuses on digitalisation of this environment but also necessitates service-dependent manufacturing system that offers an uninterrupted approach to a number of diverse, complicated, dynamic manufacturing operations management systems at a common work place (hub). This research presents a novel telefacturing based distributed manufacturing environment for recommending the manufacturing services based on the user preferences. The first step in this direction is to deploy the most advanced tools and techniques, that is, Ontology-based Protege 5.0 software for transforming the huge stored knowledge/information into XML schema of Ontology Language (OWL) documents and Integration of Process Planning and Scheduling (IPPS) for multijobs in a collaborative manufacturing system. Thereafter, we also investigate the possibilities of allocation of skilled workers to the best feasible operations sequence. In this context, a mathematical model is formulated for the considered objectives, that is, minimization of makespan and total training cost of the workers. With an evolutionary algorithm and developed heuristic algorithm, the performance of the proposed manufacturing system has been improved. Finally, to manifest the capability of the proposed approach, an illustrative example from the real-time manufacturing industry is validated for optimal service recommendation., This work has been supported by by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013., info:eu-repo/semantics/publishedVersion

In order to deal with global competition, industries have undertaken many efforts directed to improve manufacturing efficiency. From a broad perspective, two possible approaches are the adoption of lean manufacturing methodologies or the implementation of information tools: for several years, these two approaches have been assumed to be mutually exclusive. The present work aims to define a methodology to support developers and practitioners in the integration of Manufacturing Execution Systems with the lean manufacturing approach. A case-study in the field of aeronautics is presented to validate the method.

This chapter describes the development of the manufacturing process since the mid-1960s when management turned to a more scientific approach. The introduction of computers found ready ground for their implementation. Computers enabled the use of complex algorithms to regulate and control the manufacturing process, and thereby introduced better planning and control. These manufacturing control systems dominated the industry up until the early 80s. The dominant methods and concepts were: Management Information System (MIS) ; Production and Inventory Control Systems (PICS); Material Requirement Planning (MRP) ; and Computer Integrated Manufacturing (CIM) . The fact that these systems could not deliver the required control and benefits created a need for a new paradigm for manufacturing methods . In addition, the competitiv e market of the late 1980s and early 1990s imposed new demands and objectives on the manufacturing process that required a similar paradigm. More than 140 proposals were presented, none of which became “the” manufacturing method. Most of them made use of “routine,” which makes the system complex and does not satisfy the cost control needs of the system.

This paper proposes a way to describe the capability of software of manufacturing systems that can be mapped to the functional requirements of target manufacturing application for interoperability. For this objective, this paper specifies how to develop and manage software catalogue in terms of capability properties, and defines mapping rules from capability profiles to software catalogue.

The deployment of additive manufacturing (AM) processes had a rapid and broad increase in the last years, and the same trend is expected to hold in the near future. A way to better exploit the advantages of such technology is the use of appropriate information tools. However, today there is a lack of software applications devoted to this innovative manufacturing process. To overcome this issue, in the present work the application of manufacturing execution systems (MES), a tool commonly used in traditional manufacturing processes, is extended to AM. Furthermore, a framework for the deployment of shop-floor data, acquired through a monitoring system, in the design phase is presented: hence, MES should cooperate with design for additive manufacturing (DFAM), a set of methods and tools helpful to design a product and its manufacturing process taking into account AM specificities from the early design stages. In order to better understand the advantages of such cooperation, a case study for a proof of concept has been developed: the obtained results are promising, thus an online implementation would be recommended.

Nowadays, the introduction of digital technologies in the manufacturing industries paved the way to the Fourth Industrial Revolution, also known as Industry 4.0, fostering the evolution of traditional industrial systems to the concept of smart manufacturing. In last years, many reference models to describe the features of the smart factories have been proposed in literature. This paper aims at presenting an overview of these models in order to find the gaps in the research about the Industry 4.0 concepts. In particular, the business transformation required to evolve the traditional manufacturing systems into Industry 4.0-ready ones is discussed. Finally, future research topics are proposed.

Typical challenges that manufacturing systems are facing now are: (1) lack of timely, accurate, and consistent information of distributed manufacturing resources of a manufacturing system such as shop floor; (2) lack of an overall solution to track, trace, share the real-time manufacturing information among manufacturing system layer, workshop floor layer, and machine layer, and then provide an optimal decision for the manufacturing system. Recent developments in wireless sensors, communication and information network technologies have created a new era of the Internet of things (IoT). In this chapter, an overview of IoT-enabled manufacturing system (IoT-MS) is presented to provide a new paradigm by extending the techniques of IoT to manufacturing field. And the overall architecture, real-time information sharing and integration model, worklogic, and core components are described in details.

Realizing the benefits of a manufacturing technology is a key challenge that manufacturing engineers and designers face that exceeds conventional aspects of manufacturability and manufacturing compliant solutions. The goal is to comprehensively utilize manufacturing potential through manufacturing-induced properties to find new opportunities for innovative product and process solutions.