Human factor in intelligent manufacturing systems - knowledge acquisition and motivation

Abstract People play a central role in intelligent manufacturing systems because of two reasons: their knowledge is indispensable to create and improve intelligent manufacturing systems; and their motivation is very important to identify and solve causes of the problems which may occur in order to prevent them in the future. Therefore, adequate learning methods are required to accomplish these two goals: empower and motivate people. In this paper innovative methods such as learning by doing, simulations and virtual reality will be presented as the ways to transfer the knowledge about intelligent manufacturing systems and to increase motivation concerning their improvements

Standards Framework for Intelligent Manufacturing Systems Supply Chain

ISBN 979-953-307-708-5 http://www.intechopen.com/articles/show/title/standards-framework-for-intelligent-manufacturing-systems-supply-chain#referenceThe global market is striving to increase competitiveness among organizations and networks. Nowadays, management of supply chains does not only consider business processes in the traditional value chain, but also processes that penetrate networks of organisations. Indeed, the formation of cooperation and collaboration partnerships between several small organizations can be, in multiple cases, more efficient by comparison with big companies (Rudberg et al., 2002). This way, the research on supply chain management has turned from an intra-enterprise focus towards an inter-enterprise focus with companies looking for enhanced interoperability between computer systems and applications. Supply chain networks are characterized by different structures such as, business processes and technological, organizational, topological, informational, and financial structures. All are interrelated but following their own dynamics. Thus, in order to ensure a high responsiveness level, the supply chain plans must be formed robustly and extremely quickly in relation to all the structures (Gupta & Maranas, 2003). In fact, with regards to supply chain in the advent of globalization, one of the difficulties enterprises are facing is the lack of interoperability of systems and software applications to manage and orchestrate the different structures involved (Jardim-Goncalves et al. 2006; Panetto et al., 2006; Farinha et al., 2007). The increasing need for cooperation and collaboration together with the rapid advances in information and communication technology (ICT) have brought supply chain planning into the forefront of the business practices of most manufacturing and service organizations (Gupta & Maranas, 2003). Moreover, there has been a growing interest and research in e-business solutions to facilitate information sharing between organisations in the supply chain network

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

An engineering framework for Service-Oriented Intelligent Manufacturing Systems

Nowadays fully integrated enterprises are being replaced by business networks in which each participant provides others with specialized services. As a result, the Service Oriented Manufacturing Systems emerges. These systems are complex and hard to engineer. The main source of complexity is the number of different technologies, standards, functions, protocols, and execution environments that must be integrated in order to realize them. This paper proposes a framework and associated engineering approach for assisting the system developers of Service Oriented Manufacturing Systems. The approach combines multi-agent system with Service Oriented Architectures for the development of intelligentautomation control and execution of manufacturing systems.Giret Boggino, AS.; Garcia Marques, ME.; Botti Navarro, VJ. (2016). An engineering framework for Service-Oriented Intelligent Manufacturing Systems. Computers in Industry. 81:116-127. doi:10.1016/j.compind.2016.02.002S1161278

Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation.This research was partially funded by the Ministry of Economy, Industry and Competitiveness in the project with reference RTC-2014-3070-5. In addition, the work has been partially funded by the project Strategic Action in Robotics, Computer Vision and Automation financed by University Carlos III of Madrid

Mathematical control of complex systems 2013

Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)

Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

\In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, limited effort has been devoted to the measurement of reconfigurability in the resultant systems. Hence, it is not clear (1) to which degree these designs have achieved their intended level of reconfigurability, (2) which systems are indeed quantitatively more reconfigurable and (3) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. Recently, a reconfigurability measurement process based upon axiomatic design knowledge base and the design structure matrix has been developed. Together, they provide quantitative measures of reconfiguration potential and ease. This paper now builds upon these works to provide a set of composite reconfigurability measures. Among these are measures for the key characteristics of reconfigurability: integrability, convertibility, and customization, which have driven the qualitative and intuitive design of these technological advances. These measures are then demonstrated on an illustrative example followed by a discussion of how they adhere to requirements for reconfigurability measurement in automated and intelligent manufacturing systems

How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review

The Fourth Industrial Revolution has introduced innovative technologies to manufacturing, resulting in digital production systems with consequences on workers’ roles and well-being. From the literature emerges the necessity to delve into the work-related stress phenomenon since it affects workers’ health status and performance and companies’ productivity. This review summarises the stress indicators and other influential factors in order to contribute to a stress assessment of human workers in smart and intelligent manufacturing systems. The PRISMA methodology is adopted to select studies consistent with the aim of the study. The analysis reviews objective measurements, such as physical, physiological, and subjective measurements, usually driven by a psychological perspective. In addition, experimental protocols and environmental and demographic variables that influence stress are illustrated. However, the investigation of stress indicators combined with other factors leads to more reliable and effective results. Finally, it is discovered that standards regarding stress indicators and research variables investigated by experimental studies are lacking. In addition, it is revealed that environmental and demographic variables, which may reveal significant suggestions for stress investigation, are rather neglected. This review provides a theorical summary of stress indicators for advanced manufacturing systems and highlights gaps to inspire future studies. Moreover, it provides practical guidelines to analyse other factors that may influence stress evaluation

Computing the topology of configuration space

Includes bibliographical references.In this work, an algorithm is developed for generating the connectivity graph for a class of articulated manipulators. The algorithm is based upon the ability to determine whether two distinct obstacles in configuration space intersect. The efficiency of the test which is developed lies in the ability to determine the intersection relation by evaluating the curves which describe the configuration space obstacles at only a small number of points.This work was supported by the National Science Foundation under grant CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems