Enactive manufacturing through cyber-physical systems: a step beyond cognitive manufacturing

Cognitive manufacturing, as a paradigm for providing intelligence to manufacturing systems and enabling interaction with operators presents limitations. Manufacturing system requires to be adaptive to machine tools, manufacturing environments and operators. In this line, the enactive approach to cognitive science provides a paradigm for the design of new biologically inspired cognitive architectures. Likewise, the advantages of Key Enabling Technologies and the concept of Industry 4.0 reveal new opportunities for increasing industrial innovation and developing sustainable industrial environments. These technologies are appropriated to overcome the limitations of cognitive manufacturing, because they can achieve the integration of physical and digital systems focused on cyber-physical systems. In this work, an architecture for the sustainable development of enactive manufacturing systems based on holonic paradigm is proposed and its main associated informational model is described

A smart micro factory design: an integrated approach

Smart factory research is paced up in the current decade due to the development of many enabling technologies and tools available to the developers. This has led to the progress of cyber physical systems in manufacturing, now coined as cyber physical production systems. The ultimate goal of this domain is to integrate underlying technologies and connect physical plants with the virtual factory in real time for improvement in product quality, process improvements, predictive maintenance, mass customization as well as mass production. The involved technology modules include sensor network, machine learning and AI, Internet of things, human machine interface, augmented reality and collaborative robotics. For the physical element in this research, a micro factory scenario is envisaged that consists of a high precision micro/nano positioning stage installed on a tabletop sized conventional machine tool, a collaborative robot for handling of micro parts and running of machine operations, other factory devices and a human worker for supervision tasks. Due to the multi-faceted technologies involved in both the virtual and physical systems, a simultaneous design strategy is followed in both domains. First, a flexure based micro positioning, 3-axis stage device is designed that can be installed on a conventional 3-axis desktop size milling machine. Secondly, a work zone is considered for effective human robot collaboration in the production area. The work zone considered as a social space is designed in a safe and secure way with the help of integrated devices, IoT and AI

Reciprocal Learning in Production and Logistics

Integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments to human and machine agents. Thus, the question arises of what intelligent machines and human workers may be able to achieve as teammates. One answer may be guiding and training the workforce at the workplace to cope with emerging skill mismatches, emphasized by concepts of work-based learning. The extension of cyber-physical production systems towards becoming human-centered and social systems enabling human-machine interaction, creates opportunities for human-machine symbiosis by complementing each other's strengths. In this way, the concept of “Reciprocal Learning” (RL) between humans and intelligent machines has emerged, which is still rather ambiguous and lacks a profound knowledge base. Especially in production and logistics, literature is fragmented. Hence, the objective of this paper is to conduct a systematic literature review to elicit and cluster the knowledge base in RL represented by adjacent interdisciplinary fields of research, such as social and computer sciences. This work contributes to the literature by developing a comprehensive knowledge base on the concept of RL enabling to pursue future research directions towards the realization of human-machine symbiosis through RL in production and logistics

Security for a multi-agent cyber-physical conveyor system using machine learning

One main foundation of Industry 4.0 is the connectivity of devices and systems using Internet of Things (IoT) technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. This approach provides significant benefits, namely improved performance, responsiveness and reconfigurability, but also brings some problems in terms of security, as the devices and systems become vulnerable to cyberattacks. This paper describes the implementation of several mechanisms to increase the security in a self-organized cyber-physical conveyor system, based on multi-agent systems (MAS) and build up with different individual modular and intelligent conveyor modules. For this purpose, the JADE-S add-on is used to enforce more security controls, also an Intrusion Detection System (IDS) is created supported by Machine Learning (ML) techniques that analyses the communication between agents, enabling to monitor and analyse the events that occur in the system, extracting signs of intrusions, together they contribute to mitigate cyberattacks.info:eu-repo/semantics/publishedVersio

Understanding multidimensional verification: Where functional meets non-functional

Abstract Advancements in electronic systems' design have a notable impact on design verification technologies. The recent paradigms of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) assume devices immersed in physical environments, significantly constrained in resources and expected to provide levels of security, privacy, reliability, performance and low-power features. In recent years, numerous extra-functional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns of the target system. However, different from the software domain such a holistic approach remains underdeveloped. The contributions of this paper are a taxonomy for multidimensional hardware verification aspects, a state-of-the-art survey of related research works and trends enabling the multidimensional verification concept. Further, an initial approach to perform multidimensional verification based on machine learning techniques is evaluated. The importance and challenge of performing multidimensional verification is illustrated by an example case study

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version