Open manufacturing: a design-for-resilience approach

Kusiak, Andrew

doi:10.1080/00207543.2020.1770894

Cited by 58 publications

(20 citation statements)

References 39 publications

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“…Resilience is becoming a decisive factor of production systems. In particular with the vision of adaptive cyber-physical production systems (CPPS) which are realised using loose coupled and interoperable system-of-systems as architecture principle [17,23,40,42].…”

Section: Discussionmentioning

confidence: 99%

“…Only recently, resilient production systems are investigated. This includes resilient supply chains [11,13] and resilient cyber-physical production systems [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Resilience can be implemented by increasing the robustness and the adaptivity of a system. Both are system properties, which need to be implemented on many levels of a system [17].…”

Section: Introductionmentioning

confidence: 99%

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An adaptive system-of-systems approach for resilient manufacturing

Weichhart

Mangler²,

Raschendorfer³

et al. 2021

Elektrotech. Inftech.

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To implement a resilient manufacturing system, adaptive systems are required to respond to changes and disruptions. In this article, we present a research prototype of an adaptive production system. Adaptivity is implemented here in three components and on different levels. The first component is an adaptive robotic system that can be easily trained to pick up heterogeneous parts and place them on an autonomously guided vehicle. This component implements adaptivity at the task level. The second component is a planning system that can be used to reschedule production orders on an ad hoc basis. This system also allows the simulation of machine failures to analyze resilience. This job shop planning system implements adaptivity at runtime at the level of production execution. The third component is a modular process modeling and execution system that enables adaptivity at the process level. It supports the users in redesigning production processes with the help of a graphical user interface. The overall system is modular, with the three components being run in an adaptive, agile, and decentralized way. It forms a system-of-systems that shows resilience to several disruptive events.

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Section: Discussionmentioning

confidence: 99%

“…Only recently, resilient production systems are investigated. This includes resilient supply chains [11,13] and resilient cyber-physical production systems [17,18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive system-of-systems approach for resilient manufacturing

Weichhart

Mangler²,

Raschendorfer³

et al. 2021

Elektrotech. Inftech.

View full text Add to dashboard Cite

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“…Moreover, several works have adapted the strategy of utilizing infrastructure resources to improve the resilience of relative operations [23,24]. Further, some recent works developed resilience management systems for power systems [8,[25][26][27][28], transport [29][30][31][32], urban areas [33][34][35], healthcare [36][37][38], and production systems [39][40][41] by adding intelligence to the CIAs so that the agents could make autonomous decisions by analyzing the demand-response state. In summary, all the prior works proposed the improvement of resilience either by passing local sensitive data of infrastructure agents to a central fusion center or by sharing such local sensitive data with neighboring agents.…”

Section: Literature Reviewmentioning

confidence: 99%

FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures

et al. 2021

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Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-sufficient and agree to exchange local data with the server and other agents. Such assumptions create two issues: (1) uncertainty in reaching convergence while applying learning strategies on resource-constrained critical infrastructure agents, and (2) a huge risk of privacy leakage. By understanding the pressing need to construct an effective resilience model for resource-constrained critical infrastructure, this paper aims at leveraging a distributed machine learning technique called Federated Learning (FL) to tackle an agent’s resource limitations effectively and at the same time keep the agent’s information private. Particularly, this paper is focused on predicting the probable outage and resource status of critical infrastructure agents without sharing any local data and carrying out the learning process even when most of the agents are incapable of accomplishing a given computational task. To that end, an FL algorithm is designed specifically for a resource-constrained critical infrastructure environment that could facilitate the training of each agent in a distributed fashion, restrict them from sharing their raw data with any other external entities (e.g., server, neighbor agents), choose proficient clients by analyzing their resources, and allow a partial amount of computation tasks to be performed by the resource-constrained agents. We considered a different number of agents with various stragglers and checked the performance of FedAvg and our proposed FedResilience algorithm with prediction tasks for a probable outage, as well as checking the agents’ resource-sharing scope. Our simulation results show that if the majority of the FL agents are stragglers and we drop them from the training process, then the agents learn very slowly and the overall model performance is negatively affected. We also demonstrate that the selection of proficient agents and allowing them to complete only parts of their tasks can significantly improve the knowledge of each agent by eliminating the straggler effects, and the global model convergence is accelerated.

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“…The concept of polarization and emergence of extrema is illustrated with the developments in manufacturing industry (Kusiak 2020b ). For decades, manufacturing industry has been centralized with each corporation producing many components making the final product such as a car.…”

Section: Extrema In Manufacturing Industrymentioning

confidence: 99%