Cloud platform for learning factories

2013 ASEE Annual Conference & Exposition Proceedings

During his time at Durham, he earned a Postgraduate Certificate in "Teaching and Learning in Higher Education". He joined Durham from a Senior Research Associate position at Stuttgart University, Germany, where he earned his Ph.D. in Computer Science. Over the past 15 years, Dr. Schaefer has conducted research on product modeling, variant design, product lifecycle management, design-with-manufacture integration, standardized product data exchange, as well as digital and virtual engineering. His current research focus concerns the highly topical area of Cloudbased Design and Manufacturing (CBDM). A passionate educator, Dr. Schaefer also conducts research on Design Education, Personalized Learning, Distance Learning, and Professional Faculty Development. He has published more than 120 technical papers on Computer-Aided Engineering & Design as well as Engineering Education, and has presented his work at numerous national and international conferences, symposia, and workshops around the world. Dr. Schaefer serves as editorial advisory board member and reviewer for several international journals in his field. In addition, he is a registered Professional Engineer in Europe (Eur Ing), a Chartered Engineering (CEng), a Chartered IT Professional (CITP), and a Fellow of the Higher Education Academy (FHEA) in the UK, as well as registered International Engineering Educator (Ing-Paed IGIP).

2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech)

Fabrication-as-a-Service (FaaS) represents a method and a set of innovative solutions for the next generation fabrication laboratories (Fab Labs). It leverages cloud and IoT technologies to enable a wide learning community to have remote access to these labs' computer-controlled tools and equipment over the Internet. This paper introduces FaaS in the context of Fab Lab challenges and describes FaaS instantiation in NEWTON Fab Labs, part of an European Horizon 2020 project. The NEWTON Fab Labs architecture is described in details with a major focus on the communication protocol stack. The system has been deployed and a test scenario that simulates a real user behavior has been setup in order to stress system performance and measure the system response time in different operating conditions.

This paper presents a Learning Factory platform that is based on exploration of the freeware collaborative tools to address the education on advanced networked and service-based manufacturing systems such as social network-based manufacturing system. The paper presents (1) an architecture of learning factory with embedded freeware collaborative tools simulating the advanced networked and service-based manufacturing systems and organizations, and (2) elements of an application for manufacturing engineering course. The freeware-based Learning Factory platform, or environment, is oriented towards both internal and external learning and training, and for both communities, academe and industry and, especially, through their partnership. Also, the special feature of the platform is its “low-cost” allowing learning and training on advanced networked and service-based manufacturing systems in investment extensive regions and environment.

ACTA IMEKO, 2020

The pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway ini...

This paper describes the concept and realization of an architecture using cloud-based smart components to achieve a more responsive production system that provides a modular and re-configurable production framework. For each important part of the plant and the manufactured product, digital virtual copies are created and stored in active digital object memories, a unified structured format for data access and further processing. Cyber-Physical Systems act as intelligent nodes within the cloud-based network and guarantee the function of technical communication and data exchange. Moreover, these systems continuously perform and execute almost autonomous simple state and feature checks, up to a certain level of complexity. In various points of the production process these simple checks can ensure or even optimize the quality of the product. An assistance system makes use of these technical solutions and manages and monitors the distributed components and their responsibilities for quali...

Jurnal Rekayasa Sistem industri/Jurnal Rekayasa Sistem Industri, 2024

The manufacturing sector is grappling with the need to adapt to rapid technological changes, and leveraging cloud computing is becoming crucial for staying competitive and resilient. The presentation of knowledge in this work focuses on highlighting the specific challenges and opportunities in integrating cloud technologies into manufacturing systems. It aims to answer critical questions such as how cloud computing can enhance manufacturing automation, solve problems, and benefit to the industry. The methodology employed in this research takes a comprehensive and top-down approach, aligning and exploring the practical aspects of implementing these X-as-a-Service (XaaS) model in manufacturing setups. The research also acknowledges the shift from legacy Distributed Numerical Control (DNC) systems to modern solutions like MTConnect and Open Platform Communication (OPC) for data exchange in automated manufacturing systems. Emphasizing the important of data collection and realtime monitoring, the study highlights the role of Industrial Internet of Things (IoT) sensors deployed at various points of manufacturing system components (machine tools, spindles, cutting tools, production units, etc.). These sensors capture real-time production and condition data, enabling informed decisionmaking in manufacturing systems. This research not only presents the latest knowledge but also offers insights into the challenges, strategies, and methodologies involved in the successful integration of cloud-based technology into manufacturing automation systems. It also aims to serve as a valuable resource for manufacturers, researchers, and industry professionals navigating the transformative journey toward cloud-powered manufacturing.

Journal of Technical education and training, 2020

Journal Of Intelligent Systems, Ed. De Gruyter, 2017

The factory of the future scenario asks for new approaches to cope with the incoming challenges and complexity of cyber-physical systems. The role of database management systems is becoming central for control and automation technology in this new industrial scenario. This article proposes database-centric technology and architecture that aims to seamlessly integrate networking, artificial intelligence, and real-time control issues into a unified model of computing. The proposed methodology is also viable for the development of a framework that features simulation and rapid prototyping tools for smart and advanced industrial automation. The full expression of the potentialities in the presented approach is expected in particular for applications where tiny and distributed embedded devices collaborate to a shared computing task of relevant complexity.

International Journal on Interactive Design and Manufacturing (IJIDeM), 2020

The intense pressures in the industrial environment and the academic field to adopt technological tools and concepts like product lifecycle management, digital factories, automation, the internet of things, process innovation, and bridges between real and virtual worlds have resulted in necessary new process innovations. All these are encompassed in the term "Industry 4.0." The evolution of teaching methods toward flipped classrooms, software advancements to support engineering topics, online studies, new skill requirements in Industry, and easy, affordable access to education have pushed universities to find novel ways to meet current conditions and prepare for future challenges. The need to link academic knowledge with Industry led us in our research project to create a methodology for the development and implementation of virtual and hybrid scenarios by using highly integrated, digital manufacturing tools as a teaching platform to explain topics like the automation of programmable logic controllers, robotics, manufacturing, and 3D virtual commissioning. The methodology was implemented successfully in a manufacturing system integration laboratory at Tecnologico de Monterrey by using virtual and hybrid commissioning scenarios as a strategy to develop smart factories.

Lecture notes in mechanical engineering, 2023

The demand for enhancing the flexibility and efficiency of the manufacturing industry has rapidly increased over the years due to mass customization to cater to the needs of society. The conventional manufacturing industry could not survive these rapid changes. Though, the manufacturing sector is the forerunner to embrace technological paradigm shift, which paves the way for Industry 4.0 or the 4th industrial revolution. With Industry 4.0, now many world leaders are moving towards a new concept called "Factories of the Future" (FoF), which predominantly engages with the cyber-physical world to digitalize manufacturing while maintaining a strong link between hardware and the cyber-physical world. To perform any manufacturing, engineering teaching/learning programs should introduce these concepts with some practical exposure, which will enable students to contribute to the manufacturing industry all around the world. Therefore, this study focuses on converting an old manufacturing lab into a learning factory to promote FoF concept. This is achieved by enabling existing manufacturing machines to be digitally connected via Industry 4.0 while creating connections with the other machines to create flexible manufacturing systems (FMS). Competencies in integrated scheduling of machine centers and autonomous material handling systems were also explored. Furthermore, the study suggested that the conversion of the manufacturing lab needs to be done in many different integration platforms.

˜The œinternational journal of advanced manufacturing technology/International journal, advanced manufacturing technology, 2024

Service-provider industries have used cloud-based technologies in recent years. Information technology (IT) led the development of electronic hardware and software technologies to enable cloud computing as a new paradigm. Other vanguard industries such as communications and financial services leveraged cloud computing technology to develop cloud-based platforms for their respective industries. Manufacturing industry is a relative newcomer to cloud technologies although it has used modern technologies on factory floor to boost production efficiency. Cloud manufacturing (CMfg) is one of the key technologies of Industry 4.0 (I 4.0) and the goal of CMfg is to develop cloud-based approaches in manufacturing that provide flexibility, adaptability, and agility also, reduces challenges caused by system complexity. In recent years, researchers evaluated cloud technologies and proposed initial solutions tailored to manufacturing requirements. However, there are challenges in implementing CMfg due to complexity of technologies, different types of products and wide range of requirements from mass production of consumer products to low-volume specialty products. This paper presents the advantages, challenges and shortcomings associated with applications of the latest technologies to drive transition to CMfg. This research examined cloud technologies proposed for implementation of CMfg such as architectures, models, frameworks, infrastructure, interoperability, virtualization, optimal service selection, etc. This research also studied the role of technologies such as the internet of things (IoT), cyber physical systems (CPS) robotics, big data, radio frequency identification (RFID), 3D printing and artificial intelligence (AI) in accelerating the adoption and future direction of CMfg.

IFAC-PapersOnLine, 2016

A database-centric approach for the modeling, simulation and control of cyberphysical systems in the factory of the future.

TelE-Learning, 2002

The aim of an ongoing research project carried out by six European research and educational institutions is to establish a Virtual Institute for the Modelling of Industrial Manufacturing Systems (VIMIMS). The VIMIMS virtual institute has been designed to become an international platform for teaching and research in the field of analysis, design and performance evaluation of industrial manufacturing systems. VIMIMS is implemented as an internetbased portal that offers students and researchers from around the globe opportunities to study, teach, research and communicate. This paper aims to describe the architecture of the VIMIMS web portal and its utilisation by users from a real educational institute. This platform will be integrated into courses of project partners and will act as a supplement to ordinary 'face-to-face' lectures and practical activities.

Sustainability, 2021

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The concept of " Industry 4.0 " considers smart factories as data-driven and knowledge enabled enterprise intelligence. In such kind of factory, manufacturing processes and final products are accompanied by virtual models – Digital Twins. Simulation of real-world processes and objects in the virtual representation of the factory allows increasing the efficiency and reliability of manufacturing processes. To support Digital Twins concept, a simulation model for each process or system should be implemented as independent computational service. Such services work together for representation of real-world manufacturing processes. Today, the only way to implement an orchestration of a set of independent services and provide scalability for simulation is to use a cloud computing platform as a provider of the computing infrastructure. In this paper, we describe a Digital Twin-as-a-Service (DTaaS) model for simulation and prediction of industrial processes using Digital Twins and introduce the example of the Digital Twin implementation.