The Butterfly Effect: Creative Sustainable Design Solutions through Systems thinking

FAIM: Intelligent Manufacturing now, Limerick, Irelan

Analysis of the Status Quo of Intelligent Manufacturing Research Based on CiteSpace

Intelligent manufacturing is a human-machine integrated intelligent system composed of intelligent machines and human experts. Using CiteSpace as a tool, the paper analyzes and visualizes the intelligent manufacturing related literatures collected by Web of Science, summarizes the research status of intelligent manufacturing in recent years, and elaborates on the research hotspots in this field in recent years; The Advanced Manufacturing Partner Program strategy, Germany proposed the Industry 4.0 plan, the European Union promulgated the Digital European Industrial Plan and the China Made 2025 issued by China, summed up the problems faced by the intelligent manufacturing industry at this stage and made corresponding suggestions

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

Defining the Intelligent Manufacturing Enterprise

Manufacturing enterprises encounter pressure to digitalize and increase their intelligence as their environments demand increased productivity and agility. Based on existing research on intelligent enterprises, manufacturing enterprises, and data technologies, developing the definition of an intelligent manufacturing enterprise is required. Current research lacks historically derived definitions of these dynamic fields, as well as a model of their overlap. An explanatory model is proposed to define the intelligent manufacturing enterprise, its characteristics, and the capabilities needed to become such an enterprise. This model is derived through qualitative and quantitative methods utilizing content analysis. This paper describes the content analysis methodology as well as the derived definition of the intelligent manufacturing enterprise

Capabilities of the Intelligent Manufacturing Enterprise

Manufacturing enterprises encounter pressure to digitalize and increase their intelligence as their environments demand improved productivity and agility. Based on existing research on intelligent enterprises, manufacturing enterprises, and data technologies, the authors developed an explanatory model for the derivation of a definition of the intelligent manufacturing enterprise. This paper expands the formerly developed model by presenting the characteristics of the intelligent manufacturing enterprise and the capabilities needed to become such an enterprise

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

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

Upgrading Pathways of Intelligent Manufacturing in China: Transitioning across Technological Paradigms

Intelligent technologies are leading to the next wave of industrial revolution in manufacturing. In developed economies, firms are embracing these advanced technologies following a sequential upgrading strategy—from digital manufacturing to smart manufacturing (digital-networked), and then to new-generation intelligent manufacturing paradigms. However, Chinese firms face a different scenario. On the one hand, they have diverse technological bases that vary from low-end electrified machinery to leading-edge digital-network technologies; thus, they may not follow an identical upgrading pathway. On the other hand, Chinese firms aim to rapidly catch up and transition from technology followers to probable frontrunners; thus, the turbulences in the transitioning phase may trigger a precious opportunity for leapfrogging, if Chinese manufacturers can swiftly acquire domain expertise through the adoption of intelligent manufacturing technologies. This study addresses the following question by conducting multiple case studies: Can Chinese firms upgrade intelligent manufacturing through different pathways than the sequential one followed in developed economies? The data sources include semi-structured interviews and archival data. This study finds that Chinese manufacturing firms have a variety of pathways to transition across the three technological paradigms of intelligent manufacturing in non-consecutive ways. This finding implies that Chinese firms may strategize their own upgrading pathways toward intelligent manufacturing according to their capabilities and industrial specifics; furthermore, this finding can be extended to other catching-up economies. This paper provides a strategic roadmap as an explanatory guide to manufacturing firms, policymakers, and investors.This research is supported by the National Natural Science Foundation of China (91646102, L1824039, L1724034, L1624045, and L1524015), the project of China’s Ministry of Education “Humanities and Social Sciences (Engineering and Technology Talent Cultivation)” (16JDGC011), CAE Advisory Project “Research on the strategy of Manufacturing Power towards 2035” (2019-ZD-9), the National Science and Technology Major Project “High-end Numerical Control and Fundamental Manufacturing Equipment” (2016ZX04005002), Beijing Natural Science Foundation Project (9182013), the Chinese Academy of Engineering’s China Knowledge Center for Engineering Sciences an Technology Project (CKCEST-2019-2-13, CKCEST-2018-1-13, CKCEST-2017-1-10, and CKCEST-2015-4-2), the UK–China Industry Academia Partnership Programme (UK-CIAPP\260), as well as the Volvo-supported Green Economy and Sustainable Development Tsinghua University (20153000181) and Tsinghua Initiative Research Project (2016THZW)

An Evaluation Model for Financial Reporting Supply Chain Using DEMATEL-ANP

published_or_final_versionThe 9th International Conference on Digital Enterprise Technology (DET 2016): Intelligent Manufacturing in the Knowledge Economy Era, Nanjing, China, 29-31 March, 2016, In Procedia CIRP, 2016, v. 56, p. 516-51

The influencing mechanism of manufacturing scene change on process domain knowledge reuse

It is necessary for a enterprise to reuse outside process domain knowledge to develop intelligent manufacturing technology. The key factors influencing knowledge reuse in digital manufacturing scene are manufacturing activities and PPR (Products, Processes and Resources) related to knowledge modeling, enterprise and integrated systems related to knowledge utilizing. How these factors influence knowledge modeling and utilizing is analyzed. Process domain knowledge reuse across the enterprises consists of knowledge reconfiguration and integrated application with CAx systems. The module-based knowledge model and loosely-coupled integration application of process domain knowledge are proposed. The aircraft sheet metal process domain knowledge reuse is taken as an example, and it shows that the knowledge reuse process can be made flexible and rapid