Abstract
Evolvable control systems face the demands for modularity, decentralization, reconfigurabil-ity and responsiveness pointed out by the Industrie 4.0 initiative. In these systems, the self-organization model assumes a critical issue to ensure the correct evolution of the system structure into different operating configurations. ADACOR holonic manufacturing control architecture introduces an adaptive production control mechanism that balances between two states, combining the optimization provided by hierarchical structures with agility and responsiveness to condition changes offered by decentralized structures. This paper describes the switching mechanism that supports this dynamic balance and particularly the local and global driving forces for the self-organization model. The proposed model was experimentally tested in a small scale production system.

Abstract
The proliferation of Information and Communication Technologies allowed the development of new solutions to be applied at the shop-floor and all the tools which helps the manufacturers. Hence, new solutions such as cyber-physical production systems, data analytics and knowledge management were developed and proposed to solve the wellknown issues, such as quality control in multistage manufacturing systems. However, those solutions can only have a small contribution in solving that issues compared to an optimized and fully integrated approach. To allow the development of a fully integrated environment, it is necessary to deliver a standard way to communicate and interact with the different functionalities. The proposed research aims to provide an integration layer, capable of translating the rules defined at the knowledge management level, structured as Decision Model and Notation rules, into an AutomationML based language. This allows the cyber-physical production system the ability to apply these rules near the shop-floor. This article presents the template defined to represent the rules in AutomationML as well as the infrastructure developed to receive the rules from the knowledge management, translate them and deliver to the cyber-physical production system. At the end of the article is presented a test bed where the solution is instantiated with rules focused on quality control.

Abstract
To meet flexibility and reconfigurability requirements, modern production systems need hardware and software solutions which ease the connection and mediation of different and heterogonous industrial cyber-physical components. Following the vision of Industry 4.0, the H2020 PERFoRM project targets, particularly, the seamless reconfiguration of robots and machinery. This paper describes the implementation of a highly flexible, pluggable and distributed architecture solution, focusing on several building blocks, particularly a distributed middleware, a common data model and standard interfaces and technological adapters, which can be used for connecting legacy systems (such as databases) with simulation, visualization and reconfiguration tools. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.

Abstract
Nowadays, many organizations intend to convert their existing production systems towards ones that are characterized by adaptability, openness, flexibility and modularity. This requires a redesign of existing information processing systems especially related to control, leading possibly to cyber-physical production systems (CPPS). However, the implementation of new control technologies will have a direct impact on the normal operational status of production while engineers will also face several challenges and obstacles in adopting intelligent automation systems. New step-wise migration strategies are required to holistically support industries in their journey towards CPPS taking into account technical, economic and social aspects. This paper discusses the migration state-of-the-art strategies, analyzing them and providing a first attempt to define a migration approach for innovative production systems.

Abstract
The digital transformation that is on-going worldwide, and triggered by the Industry 4.0 initiative, has brought to the surface new concepts and emergent technologies. One of these new concepts is the Digital Twin, which recently started gaining momentum, and is related to creating a virtual copy of the physical system, providing a connection between the real and virtual systems to collect and analyze and simulate data in the virtual model to improve the performance of the real system. The benefits of using the digital twin approach is attracting significant attention and interest from research and industry communities in the last few years, and its importance will increase in the upcoming years. Having this in mind, this paper surveys and discusses the digital twin concept in the context of the 4th industrial revolution, particularly focusing the concept and functionalities, the associated technologies, the industrial applications and the research challenges. The applicability of the digital concept is illustrated by the virtualisation of an UR3 collaborative robot which used the V-REP simulation environment and the Modbus communication protocol.