Abstract
Programmable Logic Controllers (PLCs) still are the state-of-the-art regarding the industrial automation control, but the Industry 4.0 advent is imposing new requirements, e.g., related to the capability to acquire and process data on real-time at the edge computational layer. On the other hand, the current availability of cheaper and more powerful processors opens new windows to develop low-cost and more advanced industrial controllers aligned with the Industry 4.0 principles. In this context, an important challenge is to improve the current state-of-the-art PLCs by taking into consideration the low-cost but powerful computational boards that will allow to embed IoT technologies and data analytics. This work describes the development of a low-cost but powerful industrial controller based on the use of the single-board computer Raspberry Pi, which allows executing logic control programs codified in IEC 61131-3, IEC 61499, or even in Java or Python, while maintaining the industrial requirements. The proposed platform was experimentally used to control an automation process based on a Fischertechniks platform.

Abstract
A challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab $$^{\tiny {\textregistered }}$$ and NetLogo and applied to a real-world job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimization. © 2020, Springer Nature Switzerland AG.

Abstract
Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based onMulti-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.

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.

Abstract
The digital transformation in the manufacturing world raised an additional interest in data collection and connectivity, preferably to the one performed in real time. Internet of Things (IoT) and Machine to Machine (M2M) technologies allow the collection of the huge amount of generated data in the shop floor, at the desired rates and without any human intervention. This paper describes the application of IoT technologies to create an automatic data collection solution for an industrial metal stamping unit, supporting its posterior processing, aiming to develop monitoring, prediction and optimization in an industrial intelligent and predictive maintenance system.