Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

Estrada-Jimenez, Luis Alberto; Pulikottil, Terrin; Nguyen, Hien Ngoc; Torayev, Agajan; Rehman, Hamood Ur; Mo, Fan; Nikghadam-Hojjati, Sanaz; Barata, José

doi:10.4018/978-1-7998-6721-0.ch007

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…A vastly interconnected, integrated, and agile industrial environment will provide a competitive advantage to industrial stakeholders, sharing data, generating relevant knowledge, and efficiently and optimally controlling the manufacturing process. Those levels of integration can be summarized in [33], [34], and [35]:…”

Section: E Fourth Industrial Revolutionmentioning

confidence: 99%

Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

et al. 2023

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The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.

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Section: E Fourth Industrial Revolutionmentioning

confidence: 99%

Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

et al. 2023

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“…There is a large body of research on automation agent architectures -distributed artificially intelligent programs that collaborate to solve problems -that allow for 'on the fly' reconfiguration of manufacturing systems, exploiting asset flexibility and control to change the behaviour of the manufacturing system [7]. Multi-agent systems and the concept of cyber-physical systems (CPS) were key contributors to research in the reconfiguration of manufacturing environments at the beginning of Industry 4.0 [8,9].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Manufacturing System Reconfiguration Based on Artificial Intelligence and Digital Twin

Chaplin

Sanderson

et al. 2022

Lecture Notes in Mechanical Engineering

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The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications.

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“…manufacturing) that occurs "without the direct participation of a human worker" [1]. With the advent of the fourth industrial revolution, this concept has been enhanced with the emergence of technologies such as cloud computing or collaborative robotics with the aim of satisfying dynamic market demands and high product customization [2], [3]. As a driver of automation, self-organization can be defined as a mechanism that enables a system to change its organization without explicit external command [4].…”

Section: Introductionmentioning

confidence: 99%

Self-configuration of a Robotic Platform to support a self-organized Manufacturing Process

Jimenez

Sanderson

Chaplin

et al. 2022

IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

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Self-configuration in manufacturing is a key trend to generate adaptable production systems. Different product requirements need different machine settings and continuous software update. Existing approaches usually assume that manufacturing resources (e.g. robotic platforms) have already a predefined set of available capabilities or that there is a centralized manager able to provide configuration updates. Centralized approaches are not always a suitable solution. Dynamic changes in production require continuous maintainability of the central server, constraining the shop-floor agility. New approaches should consider emergent self-configuration i.e. carried out at run time and decoupled from a centralized unit. In this work we present a framework for self-configuration of robotic platforms, where those are not explicitly preconfigured; instead, control parameters are transferred wirelessly from raw material to resources during production (once they arrive to the resource), following an intelligent product-driven manufacturing approach. A set of templates are proposed to generalize the sequential behaviour of manufacturing operations. Thus, manufacturing resources can read, use these parameters and store them for future operations. This framework is illustrated with an assembly operation using an educational robotic platform.

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Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

Cited by 9 publications

References 46 publications

Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

A Framework for Manufacturing System Reconfiguration Based on Artificial Intelligence and Digital Twin

Self-configuration of a Robotic Platform to support a self-organized Manufacturing Process

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