913 research outputs found
INDUSTRIAL DEVICE INTEGRATION AND VIRTUALIZATION FOR SMART FACTORIES
Given the constant industry growth and modernization, several technologies have
been introduced in the shop floor, in particular regarding industrial devices. Each
device brand and model usually requires different interfaces and communication
protocols, a technological diversity which renders the automatic interconnection with
production management software extremely challenging. However, combining key
technologies such as machine monitoring, digital twin and virtual commissioning,
along with a complete communication protocol like OPC UA, it is possible to
contribute towards industrial device integration on a Smart Factory environment.
To achieve this goal, several methodologies and a set of tools were defined. This
set of tools, as well as facilitating the integration tasks, should also be part of
a virtual engineering environment, sharing the same virtual model, the digital
twin, through the complete lifecycle of the industrial device, namely the project,
simulation, implementation and execution/monitoring/supervision and, eventually,
decommissioning phases.
A key result of this work is the development of a set of virtual engineering
tools and methodologies based on OPC UA communication, with the digital twin
implemented using RobotStudio, in order to accomplish the complete lifecycle
support of an industrial device, from the project and simulation phases, to monitoring
and supervision, suitable for integration in Industry 4.0 factories. To evaluate the
operation of the developed set of tools, experiments were performed for a test
scenario with different devices.
Other relevant result is related with the integration of a specific industrial
device – CNC machining equipment. Given the variety of monitoring systems and
communication protocols, an approach where various solutions available on the
market are combined on a single system is followed. These kinds of all-in-one
solutions would give production managers access to the information necessary for a
continuous monitoring and improvement of the entire production process
Towards Logistics 4.0: A Skill-Based OPC UA Communication between WMS and the PLC of an Automated Storage and Retrieval System
In order to bring intralogistics systems to the same level of interoperability as today’s modern production systems, logistics must take the essential steps towards Industry 4.0. This requires an increasing abstraction level of control logic as an enabler for horizontal and vertical integration. The abstraction will lead to the interconnection of manufacturing and logistics control with the production planning and warehouse management systems (WMS). A main enabler for these communication paths are service-oriented architectures (SoA). OPC UA has established itself as a widely used and already adopted SoA-based communication standard in industry. The paper describes the realization of an OPC UA-based approach for the communication between a WMS and a PLC of an automated storage and retrieval system (ASRS). The conceptual basis of communication design are skills of the ASRS. The work is supported by an architectural design with a subsequent prototypical implementation
Service-oriented architecture for device lifecycle support in industrial automation
Dissertação para obtenção do Grau de Doutor em
Engenharia Electrotécnica e de Computadores
Especialidade: Robótica e Manufactura IntegradaThis thesis addresses the device lifecycle support thematic in the scope of service oriented industrial automation domain. This domain is known for its plethora of heterogeneous equipment encompassing distinct functions, form factors, network interfaces, or I/O specifications supported by dissimilar software and hardware platforms. There is then an evident and crescent need to take every device into account and improve the agility performance during setup, control, management, monitoring and diagnosis phases.
Service-oriented Architecture (SOA) paradigm is currently a widely endorsed approach
for both business and enterprise systems integration. SOA concepts and technology
are continuously spreading along the layers of the enterprise organization envisioning
a unified interoperability solution. SOA promotes discoverability, loose coupling,
abstraction, autonomy and composition of services relying on open web standards – features that can provide an important contribution to the industrial automation domain.
The present work seized industrial automation device level requirements, constraints and needs to determine how and where can SOA be employed to solve some of the existent difficulties. Supported by these outcomes, a reference architecture shaped by distributed, adaptive and composable modules is proposed. This architecture will assist and ease the role of systems integrators during reengineering-related interventions throughout system lifecycle. In a converging direction, the present work also proposes a serviceoriented
device model to support previous architecture vision and goals by including
embedded added-value in terms of service-oriented peer-to-peer discovery and identification, configuration, management, as well as agile customization of device resources.
In this context, the implementation and validation work proved not simply the feasibility and fitness of the proposed solution to two distinct test-benches but also its relevance to the expanding domain of SOA applications to support device lifecycle in the industrial automation domain
Engineering methods and tools for cyber–physical automation systems
Much has been published about potential benefits of the adoption of cyber–physical systems (CPSs) in manufacturing industry. However, less has been said about how such automation systems might be effectively configured and supported through their lifecycles and how application modeling, visualization, and reuse of such systems might be best achieved. It is vitally important to be able to incorporate support for engineering best practice while at the same time exploiting the potential that CPS has to offer in an automation systems setting. This paper considers the industrial context for the engineering of CPS. It reviews engineering approaches that have been proposed or adopted to date including Industry 4.0 and provides examples of engineering methods and tools that are currently available. The paper then focuses on the CPS engineering toolset being developed by the Automation Systems Group (ASG) in the Warwick Manufacturing Group (WMG), University of Warwick, Coventry, U.K. and explains via an industrial case study how such a component-based engineering toolset can support an integrated approach to the virtual and physical engineering of automation systems through their lifecycle via a method that enables multiple vendors' equipment to be effectively integrated and provides support for the specification, validation, and use of such systems across the supply chain, e.g., between end users and system integrators
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly
CNC Machines Integration in Smart Factories using OPC UA
This work was partially developed under the project TOOLING4G (POCI-01-0247-FEDER-024516) and the project S4PLAST - Sustainable Plastics Advanced Solutions (POCI-01-0247-FEDER-046089), supported by Programa Operacional Competitividade e Internacionalização (POCI), Programa Operacional Regional
de Lisboa, Portugal 2020 and Fundo Europeu de Desenvolvimento Regional (FEDER). This project was also partially financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within projects UIDB/00308/2020 and LA/P/0063/2020, and under the Scientific Employment Stimulus - Institutional Call CEECINST/00051/2018. Special thanks to the Technological University of the Shannon: Midlands Midwest, a RUN-EU partner who also supported this workThis paper examines the idea of Industry 4.0 from the perspective of the molds industry, a vital industry in today’s industrial panorama. Several technologies, particularly in the area of machining equipment, have been introduced as a result of the industry’s constant modernization. This technological diversity makes automatic interconnection with production management software extremely difficult, as each brand and model requires different, mostly proprietary, interfaces and communication protocols. In the methodology presented in this paper, a development of monitoring solutions for machining devices is defined supporting the leading equipment and operations used by molds industry companies. OPC UA is employed for high-level communication between the various systems for a standardized approach. The approach combines various machine interfaces on a single system to cover a significant subset of machining equipment currently used by the molds industry, as a key result of this paper and given the variety of monitoring systems and communication protocols. This type of all-in-one approach will provide production managers with the information they need to monitor and improve the complete manufacturing process.info:eu-repo/semantics/publishedVersio
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Intelligent decision support for maintenance: an overview and future trends
The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions
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