Search

Serbia is rapidly working on the development and implementation of digital manufacturing models in SMEs, through the national Industry 4.0 Platform. The aim is to create a pilot intelligent workshop which would be used to develop and showcase examples of best practice for digital manufacturing. Currently, most SMEs use CAD, CAM, ERP models, which form the basis for the development of the concept of digital manufacturing through cloud computing, BDA, IIoT and smart supply-chains, as elements of Industry 4.0. This paper gives a practical example of an SME with all the above-mentioned elements of digital manufacturing.

The mass production of metal molds for glass bottles is a highly automated and technologically demanding sector of activity. Mold manufacturing involves several operations and one of the most critical is the one related to mold surface welding. The main goal here is to insert a more durable metal into specific areas of the mold to create regions with enhanced characteristics (e.g. improved wear resistance) in order to improve the quality and durability of the mold. Such an operation typically involves three steps, namely: i) mold preheating, ii) manipulation and iii) welding. Overall, this is a rather complex process, demanding tight control and monitoring of its variables. Our industrial partner, a medium-size manufacturer of molds for glass bottles, already had an extensively automated manufacturing process. However, there were frequent reports of processing flaws during the welding stage (e.g. quality of metal deposition, fissures, welding pores) with the mold being lost in some cases. The reasons behind most of the reported flaws were difficult to discern from the available sensors and methods. From our early assessment it became clear that the range of sensors available during the welding operation was insufficient and that the communication between the machines inside the production cell was either too simple or disconnected from the rest of the factory. This resulted in an absence of an integrated record of the production process. In view of this, it was necessary to develop and implement a digitalization plan, which we describe in this paper, of the production cell responsible for mold surface welding. As a result of this planning, additional sensors were introduced to the production cell, related to pressure, temperature, humidity, flow, and voltage. The intent here was to have monitoring variables that could be used to diagnose and/or prognosticate problems in the process. Also, since the sensors lacked integration, a network was established using IoT technologies supported by the PlugThings Framework together with Universal Plug and Play (UPnP) technology. The PlugThings, an IoT platform developed by our partner iTime, acted as a mediator between the sensor network and a Web server. The IoT platform also allowed to perform the vertical integration of the process, from sensors on the production cell to the Enterprise Resource Planning (ERP) system. Finally, this initiative enabled access to charting tools and to data in the form of XML files. Currently, the system in undergoing exhaustive tests before expanding to other production cells in the shop floor. Future steps will involve the use of analytics and artificial intelligence to diagnose and predict failures of the manufacturing process and to better streamline production.

Companies digitized with the fourth industrial revolution should create an ecosystem of innovation that provides opportunities for dialogue with all the parties involved. Technological developments and transformations should be reflected in innovation processes based on Open Innovation principles. Hence, the purpose of this paper is to analyse and to discuss the Industry 4.0 in Open Innovation perspective. The operational approach consists in identifying and in ranking the determinants that characterize the phenomenon for studying the interrelations between them, using AHP and ISM methods. As a result, Dynamic Capabilities and Know-how emerged as relevant drivers. Anyway, Industry 4.0 paradigm in Open innovation perspective is led by other factors, even though they are weak drivers. Enhancing the understanding of these factors and their interrelations provides valuable insights for each stakeholders and policy maker in the I4.0 environment, thus facilitating this paradigm in Open Innovation perspective.

Always looking for innovation and prominence in the labor market, companies from different areas are always aiming to improve issues of development and monitoring of production. Virtualization of existing resources is being widely discussed and solicited by companies as a whole, involving both design and manufacturing sectors. The choice to emphasize the application of Digital Twin is due to the fact that it is a digital replica, a virtual version of a system, helping companies to develop, improve and evaluate their products, optimizing the process, improving production, reducing costs and helping the improvement of the final product. This article presents a bibliometric analysis of the subjects related to Digital Manufacturing, focusing on the application of Digital Twin and also researches involving systems monitoring with the transfer of information in real time. For the construction of knowledge, the realization of this research was chosen using the ProKnow-C method (Knowledge Development Process) assisting the author in the theoretical basis of the subject in question. The development of this method consists of the first moment in the selection of the articles to form a bibliographic portfolio, searching for publications by keywords relevant to the research; later the bibliographic analysis of these articles found in databases respected, quantitatively evaluating those of greater relevance, as well as recurrent authors and periodicals, focused on the subject; in a third moment the practice of a systematic analysis of this portfolio is important, raising more recurring problems, possible proposed solutions and existing gaps in this area so that; in the end it is possible to define the research question as well as the general and specific objectives forming the knowledge base on the topic addressed. This article presents a survey on the subject in question, demonstrating research involving real-time monitoring with the use of sensors and actuators, as well as emphasizing in Industry 4.0 with Internet of Things (IoT) mechanism and cyber-physical systems. Works seeking the improvement of these systems, as well as the use of data analytics to improve the virtual model generated by Digital Twin, show a great tendency for future research.

Manufacturing companies across all major industries are facing serious challenges trying to competitively design and manage modern products, which are becoming increasingly complex multi-domain systems or “systems of systems”. Model-based systems driven product development (or SDPD, for Systems Driven Product Development) has been proposed as a solution based on driving the product lifecycle from the systems requirements and tracing back performance to stakeholders’ needs through a RFLP (Requirement, Functional, Logical, Physical) traceability process. The SDPD framework integrates system behavioral modeling with downstream product design and manufacturing process practices to support the verification/validation of the systems behavior as products progress through all phases of the lifecycle, as well as the optimization of trade-offs decisions by maintaining the cross-product digital twin and thread for global decision optimization in an efficient and effective way. We have developed an innovative digital manufacturing curriculum (designed around the SDPD paradigm) that is based on the digitalization of the SE (Systems Engineering) process through the integration of modelling and simulation continuum, in the form of Model-based Systems Engineering (MBSE), with Product lifecycle management (PLM). At the core of this curriculum is a shift of focus from theory to implementation and practice, through an applied synthesis of engineering fundamentals and systems engineering, that is driven by a state-of-the-art digital innovation platform for product (or system) development consisting of integrated software (digital) tools spanning the complete lifecycle. The curriculum consists of three key components, namely, modelling and simulation continuum, traceability, and digital thread. The curriculum provides a foundation for implementing the digital twin and supports the training of the next generation of engineers for Industry 4.0. The digital manufacturing (or SDPD) framework is applied in the design and optimization of an electric skateboard. The implementation demonstrates: 1) The benefits of digitalization/model-based engineering when developing complex multi-domain products or systems; 2) The ability of students to effectively complete a real-life modern product development within the time line of one semester; 3) The provision of MBSE curriculum for Engineering Education 4.0, characterized by key, integrated skills for the digital enterprise and Industry 4.0.

In the Industry 4.0 era, cyber physical manufacturing systems (CPPS) has started to change activities of manufacturing system engineers into CPS based ones. In typical Japanese factories, manufacturing system engineers are always stationed at the shop floor and continuously improve manufacturing systems with workers. For supporting such engineers’ activities, we are developing the concept of ‘Digital Triplet’ as an extension of Digital Twin. Digital Triplet consists of intelligent activity world in addition to the cyber world and the physical world and supports manufacturing system engineers in developing engineering processes with the cyber and physical worlds. Based on this, we are developing an education program. In this paper, we first describe the concept of Digital Triplet. Next, after explaining the overview of the education program, we introduce a course in which trainees (mainly novice engineers) execute ‘Kaizen’ with a prototype CPS system of a learning factory.

JSON (JavaScript Object Notation) is a prevalent data format used in cloud-based platforms that support composable digital manufacturing workflows. The current work presents a method to translate the logics found in JSON Schema into OWL axioms, in order to facilitate ontology-based unified data validation with JSON data. The specific contributions of this paper include the demonstration of using a formal ontology for the logic translation and data validation, a technique for disambiguating implicit relations found in JSON Schema as explicit OWL properties, and mapping JSON Schema validation keywords to equivalent OWL expressions.

Companies are adopting several new technologies that form the pillars of Industry 4.0 production framework, of which Digital Manufacturing (DM) stands out by combining conventional manufacturing technologies with digital techniques. These are used to assist in the design and analysis of the product and manufacturing processes. The adoption of digital manufacturing is partly about technological change, but it also entails significant organizational issues, which often are overlooked by managers. The purpose of this study is to identify the key factors that enable or prevent DM implementation, considering the production paradigm of Industry 4.0. Based on a literature review that identified a preliminary list of key factors, the appropriateness of these factors is empirically tested and refined in a two-fold approach: an in-depth pilot case in a multinational automotive company that is adopting DM technologies, and a survey of 113 users, managers, implementers and researchers working on digital manufacturing and Industry 4.0. The study identified 24 key factors to be considered when firms implement DM. These are categorized into technical, organizational, project based and external factors. The findings also indicate how each factor should be considered, and that they cannot be generalized due to cultural differences inherent to each individual company. As such, this research contributes to the current research debate by identifying the critical factors to be considered when conceiving and applying models for planning and executing DM implementation.

Technology advancements of real time connectivity and computing powers has evolved the way people manage activities triggering heavy reliance on smart devices. This has reshaped the ability to memorize crucial information, instead accumulate the information into devices allowing real-time fingertip access when needed. Inability to access such information when needed is routinely assumed with device malfunctioning bypassing the probability of compromise, but what if the information is now being accessed by adversaries depriving the data-owner access to crucial information? Cyber manufacturing systems are not immune from these issues. It is possible to approach this problem as generating SoS meta-architecture. In this paper a model is developed using SoS Explorer tool developed by Engineering Management and Systems Engineering that demonstrates the concern on a notional banking cybersecurity problem. Solution that generates a meta-architecture is achieved through Genetic Algorithm (GA) using fuzzy assessor as a fitness function to select combinations of defense mechanism constituents.

Highly qualified labour force is a key resource for growth. In modern manufacturing, the competent use of advanced measuring equipment for inspection and digitization of parts is an essential competence that is needed for both advanced product/process engineering and quality control. Coordinate Metrology (including 3D digital measuring technologies) is by far the most important tool for these specialized activities. As widely reported, the individuals operating the measuring systems - with their decisions - are frequently one of the most relevant error sources in Coordinate Metrology operations, especially when dealing with new measuring technologies supporting Digital Manufacturing (e.g. Computed Tomography, Fringe-projection, Reverse Engineering). The paper reports the intermediate results of an initiative aiming at innovating training in Coordinate Metrology, focused on supporting the needs of SMEs in the supply chain of the automotive industry. The main target group are industry employees operating in SMEs that are newcomers on 3D measuring technologies. An integrated concept for training in Coordinate Metrology has been developed using a blended learning approach, based on a 10-steps structure and incorporating the learning outcomes required to operate different measuring systems in a consistent way.

The purpose of this paper is to understand the evolution of manufacturing execution systems (MES) in the digital transformation era. Theoretical propositions made on MES (based on literature survey) were empirically examined using three case studies in Danish companies. Findings gave an overview of Industry 4.0 ready MES and identified its role in factories of the future. It is a first attempt to analyze the concepts behind next-generation MES to give a primer on ‘MES as a digital twin’, via first iteration of results from cross-case synthesis of collected data. The paper also maps the current MES research pertaining to Industry 4.0 into key groups to highlight its significance in digital manufacturing.

This paper presents a bibliometric and systemic analysis of product development, digital manufacturing, and product manufacturing information. The ProKnow-C (Knowledge Development Process - Constructivist) method was used to achieve the aim of this study, for it allows researchers to acquire knowledge and detect relevant studies that can support the theoretical foundation of scientific research. After selection, a collection of relevant papers was subjected to quantitative analysis for identifying the most important authors, journals, and keywords. Thereafter, a systemic analysis was accomplished in order to verify the content of articles in a bibliographic portfolio and identify opportunities, trends, and research gaps. Finally, the main research opportunities in the proposed area of interest of were recognized and hereby presented.

Cloud application services are being implemented in all areas of engineering. Manufacturing engineering could benefit from the implementation of cloud-technologies that lead to an improved production and product-cost reduction. This paper proposes the development of a flexible, scalable, versatile, highly affordable and open-source cloud-platform for implementing sensor networks in support of advanced manufacturing. In particular, a two-layer-network architecture is present and explained through real applications in the area of advanced manufacturing. Two case studies present the collection, storage, and sharing of data over the network. Results show the successful integration of communication protocols across the cyber physical system as well as the reliability of the platform and its ability to log data. This architecture has the potential of generating great impact on the new area of digital manufacturing.

In this paper an industrial project insight of the ongoing research and implementation activities of a German co-founded and cross-sectoral applied research project, called “Industry_Integrator”, will be delivered. This project merges the disciplines mechanical engineering, modern IT, tool manufacturing, polymer processing, food technology as well as academia and fosters Industry 4.0 solutions for SME. The conceptual design of the industry integration device itself serves as a quite handy and easy to use enabler for smart production, even in existing and outdated production systems or in rough environments. No integration towards DIN rail or even wiring is necessary, as essential data aggregation and evaluation is done decentralized at the spot of data origin. The Integrators themselves are connected via a unique communication protocol and are performing in a dynamic meshed network.

The digital factory of the future will be driven by the integration of physical smart machine tools and cyber-enabled software, working seamlessly to increase manufacturing intelligence, flexibility, agility and production efficiency. The objective of this study is develop and demonstrate a middleware software architecture to interface physical machines on a shop floor with client manufacturing applications. We have connected both legacy and modern ‘smart’ machines to a highly scalable database capable of storing streaming time-series data generated by on-board sensors and machine controllers. Three client applications were developed to demonstrate the mechanism through which third-party apps can be written without direct physical communications with machines on the shop-floor. The first, is an application that resides within the Digital Manufacturing Commons (DMC) which demonstrates the ability to query data from any physical machine on the floor; the 2 nd application demonstrates a python app which compares digital product data with machine generated data; and the 3 rd application demonstrates building a LabView app built to interface with the middleware service. This proposed architecture enables an ecosystem of smart manufacturing applications to be built and deployed on the shop-floor through open-sourced software and hardware devices thereby reducing cost of manufacturing software development.

The AAU Smart Production project is a research initiative at Aalborg University, Denmark running 2015-2019. The objective of this program is to research and demonstrate emergent digital technologies and to adapt these to the needs and characteristics of Danish industries. A Smart Production Laboratory (Smart Lab) has been developed as part of the project. The Smart Lab is a small Industry 4.0 factory containing a number of digital technologies which are integrated into a physical Industry 4.0 demonstrator. Since its establishment in August 2016, a number of projects have been initiated involving more than 50 students and researchers from various departments at Aalborg University. Most of these projects have been made in collaboration with large end-user companies and a number system integrators and technology providers (most of which are SMEs). There has been a great industrial interest in the Smart Lab. Over the last six months, more than 150 companies have visited the lab, and the interest in engaging in projects by far exceeds our capacity. In the future, we are going to focus on setting up collaboration with SME end-users. Furthermore, we will investigate more systematic approaches for improving the knowledge sharing between the on-going projects and enabling better knowledge co-creation. The paper will present the Smart Lab, and demonstrate how we are teaching and researching using a collaborative approach engaging with end-user, vendors and system integrators.

The manufacturing sector is at the core of the American economy. With American leadership in advanced manufacturing at risk due to changes in the global economy and new competitors rising across the globe, the U.S. President announced an investment of $1 billion in a National Network for Manufacturing Innovation (NNMI). The key focus is on new technology paradigms that can improve the competitiveness of American manufacturing – with a focus on small and medium enterprises (SMEs) – through digitization of design and manufacturing, democratization of technology, and collaborative design and analytics to sustain leadership across the U.S. manufacturing ecosystem. Funded through the newly established Digital Manufacturing and Design Innovation Institute and supported through a Cooperative Agreement with the Department of Defense, the Digital Manufacturing Commons (DMC) is an open-source platform based on previous Massachusetts Institute of Technology (MIT), Defense Advanced Research Projects Agency (DARPA) and General Electric (GE) projects and built to democratize development and access to the tools for manufacturing innovation across small and large companies, universities, institutes, and entrepreneurs. The DMC will bring together one hundred thousand or more users, thousands of SMEs, large manufacturing companies, and universities to collaborate on manufacturing and to accelerate technologies that integrate the digital thread from design to manufacture and service. The initiative to build the DMC is led by GE Global Research Center. The DMC will be transitioned to the Digital Manufacturing and Design Innovation Institute and made available to the U.S. manufacturing base to enable a digital revolution in manufacturing innovation.

From a business perspective, cloud computing has revolutionized the information and communication technology (ICT) industry by offering scalable and on-demand ICT services as well as innovative pricing plans such as pay-per-use and subscription. Considering the economic benefits of cloud computing, cloud-based design and manufacturing (CBDM) has been proposed as a new paradigm in digital manufacturing and design innovation. Although CBDM has the potential to reduce costs associated with high performance computing (HPC) and maintaining ICT infrastructures in the context of engineering design and manufacturing, it is challenging to justify the potential cost savings associated with HPC in the cloud because of the complexity in the cost-benefit analysis of migrating to CBDM. In response, this paper provides important insights into the economics of CBDM by identifying key cost factors and potential pricing models that can influence decision making on whether migrating to the cloud is economically justifiable. Specifically, the cost breakdown of adopting CBDM is presented. The general key benefits are demonstrated using real case studies. In addition, a hypothetical application example is presented to compare costs in CBDM with that of traditional in-house design and manufacturing. Finally, some of the key issues and road blocks are outlined.

A new digital-based model is presented for the prediction cutting forces in 3-axis CNC milling of surfaces. The model uses an algorithm to detect the work-piece/cutter intersection domain automatically for given cutter location (CL), cutter and work-piece geometries. The algorithm uses a voxelization framework to voxelize the cutter helix as well as the work-piece and based on voxel intersections between them can detect the tool engagement. Furthermore, an analytical approach is used to calculate the cutting forces based on the discretized model. The results of model validation experiments on machining PMMA and Aluminum 6061 are also reported. Comparisons of the predicted and measured forces show that this digital approach can be used to accurately predict forces during machining.