Giovanni Di Orio

An Agent Based Framework to Support Plug And Produce

The new market trends are very different, so it is crucial to the companies improve the tools and capabilities that allow themselves readjust rapidly and effectively to the news market changes and to the new requirements. In order to facilitate this process, it is proposed in this paper an agent based implementation that can provide to the existent systems the capacity to quickly adapt and reconfigure using standard technology. The proposed framework provides an intelligent tool to autonomously help the configuration when a production operator pretends to introduce a new variant of product in runtime and consult important information provided by the system to monitor execution.

A Platform to Support the Product Servitization

Nowadays manufacturers are forced to shift from their traditional product-manufacturing paradigm to the goods-services continuum by providing integrated combination of products and services. The adoption of service-based strategies is the natural consequence of the higher pressure that these companies are facing in the global markets especially due to the presence of competitors which operate in low wage region. By betting on services, or more specifically, on servitization manufacturing companies are moving up the value chain in order to move the competition from costs to sophistication and innovation. The proliferation of new emerging technologies and paradigms together with a wider dissemination of information technology (IT) can significantly improve the capability of manufacturing companies to infuse services in their own products. The authors present a knowledge-based and data-driven platform that can support the design and development of Product Extended by Services (PESs) solutions.

Enhancing device exchange agility in Service-oriented industrial automation

The industrial automation domain is known for its plethora of heterogeneous equipment encompassing distinct functions, form factors, network interfaces and 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 when handling device breakdowns or lifecycle modifications. Emerging from higher level IT domain, Service-oriented Architecture (SOA) paradigm is currently a widely endorsed approach for both business and enterprise systems integration. 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 implements a SOA-based infrastructure comprising Semantic Web concepts to enhance the process of exchanging a device in an industrial automation environment by assisting (and even automate) this task supported by service and device semantic matching whenever a device breaks down or needs to be replaced. The infrastructure was implemented and tested in an educational shop floor setup composed by a set of distributed entities each one managed and controlled by its own SOA-ready PLC. The performed tests revealed that the tasks of discovering and identifying new devices, as well as providing assistance when a device is down or disconnected offered a valuable contribution and it can increase the agility of the overall system when dealing with operation disruptions or modifications at device level.

Self-Learning approach to support lifecycle optimization of Manufacturing processes

Modern manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solutions to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the resources and an improved quality of products. The shoe industry provides a fertile ground in this direction since traditionally the production and manufacturing of shoes involves a wide variety of materials and a large number of both operations and machines characterized by a huge number of parameters as well. Thereby, the optimization of manufacturing process parameters during production activities is recognized as one of the most important task. As a matter of fact, the selection of the best set of manufacturing process parameters can improve final product quality, cost effectiveness while reducing anomalous situations that potentially may cause a line stopping. The present paper describes the research background that has driven the design and development of the Self-Learning methodology and reference architecture as the foundation for a new generation of monitoring and control solutions. Furthermore, a real application scenario from the shoe industry is also described to demonstrate the applicability of the proposed solution.

PRIME as a Generic Agent Based Framework to Support Pluggability and Reconfigurability Using Different Technologies

Presently the manufacturers are facing a challenging and important period. The markets are very different comparatively to the past, with constant changes and facing a big crisis. Hence, with these new characteristics is very important for the companies quick adaptations in order to take advantage of new business opportunities. However on the shop floor is not easy reconfigure the production lines to perform new tasks. The PRIME framework presents an agent based solution capable to rapidly reconfigure and readapt the production line using standard technology. In this paper is demonstrated the usability of this framework with three completely different technologies. With this flexibility, using PRIME it is possible perform plug and produce, reconfigurability and monitoring for all kind of technologies, not obligating the companies to reform all components in the line, avoiding external costs and stoppages.

Energy Efficiency in Machine Tools - A Self-Learning Approach

Due to the growing demand to reduce the environmental impact, the manufacturing companies of today are encouraged to adopt new green methodologies, strategies and technologies for increasing the energy efficiency of their manufacturing production lines. These solutions have a great impact on several productivity metrics including availability and costs. The continuous pursuit of productivity and particularly of machine availability has led to an increase of the total energy consumption in production plants. However, productivity gains can also be achieved by reducing the life-cycle costs of the manufacturing production systems. The research currently done under the scope of Self-Learning Production Systems (SLPS) tries to fill the gap between availability and efficiency by providing an innovative and integrated approach for ensuring the efficient utilization of the resources in machine tools.

Control System Software Design Methodology for Automotive Industry

As automated manufacturing systems become more and more complex, the need for new methodologies to improve the design and development of industrial monitoring and control solutions is becoming peremptory. Programmable Logic Controllers (PLCs) dominates the application domain meaning that they are established as the most popular industrial controllers used in factory and shop floor. Although the capabilities of these controllers have strongly improved in the last decades, their historical background as the easy understand by the electricians who had previously worked on electrical systems has meant that the de-facto standard for the implementation of control and monitoring solution for these devices remains the ladder logic. However, to face the globalization challenges, the manufacturing companies needs to improve their productivity by reducing the costs, delivering high-quality of products with high variety and improving their responsiveness to changing market condition. Moreover productivity gains can also be achieved by reducing the life-cycle costs of manufacturing production systems implying the using of more flexible and agile approaches. In this scenario, the proposed methodology aims to provide a homogeneous and optimum process that, starting from a set of mechanical specifications and behavioural models of machines, enable the generation of industrial logic automatically ensuring the same structure and naming standard in every project and the quality of the code.

Self-Learning Production Systems (SLPS) - Optimization of manufacturing process parameters for the shoe industry

The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.

Self-Learning Production Systems: Adapter Reference Architecture

To face globalization challenges, today manufacturing companies require new and more integrated monitoring and control solutions in order to optimize more and more their production processes to enable a faster fault detection, reducing down-times during production, and improving system performances and throughput. Today industrial monitoring and control solutions give only a partial view of the production systems status, what compromises the accurate assessment of the system. In this scenario, integrating monitoring and control solutions for secondary processes into shop floor core systems guarantees a comprehensive overview on the entire system and its related processes since it provides access to a greater amount of information than before. The research currently done under the scope of Self-Learning Production Systems (SLPS) tries to fill this gap by providing a new and integrated way for developing monitoring and control solutions. This paper introduces the research background and describes the generic SLPS architecture and focus on the Adapter component responsible for adapting the system according to current context information. The proposed Adapter architecture and its core components are introduced as well as the generic Adaptation Process, i.e., its “modus operandi” to face context changes. Finally, one of three distinct business-case scenarios is presented to demonstrate the applicability of the envisioned reference architecture and Adapter solution into an industrial context as well as its behavior and adaptive ability along system lifecycle.

Adapter for Self-Learning Production Systems

To face globalization challenges, modern production companies need to integrate the monitoring and control of secondary processes into shop floor core system to remain competitive and improve system performance and throughput. The research currently being done under the scope of Self-Learning Production Systems tries to fill this gap. Current work introduces the domain and a generic architecture, while focus over the responsible element for executing system adaptations according to current context: the Adapter. The Adapter architecture and its components are introduced as well as the generic Adaptation process. Early prototype scenarios applied to concrete real-world scenarios are also presented.