Foresighted digital twin for situational agent selection in production control

May, Marvin Carl; Overbeck, Leonard; Wurster, Marco; Kuhnle, Andreas; Lanza, Gisela

doi:10.1016/j.procir.2021.03.005

Cited by 30 publications

(17 citation statements)

References 25 publications

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“…To address this challenge, reinforcement learning (RL), such as DQN and deep RL, were employed as a substitute for heuristic optimization and supervised approaches in various investigations, where the major task is normally mathematically formalized as a Markov decision process (MDP), with the objective of autonomously achieving the global optimal economic and logistic KPIs in a factory or logistic simulation environment [96][97][98][99] (EG-factor). In order to incorporate humans as a critical element in smart manufacturing, May et al presented a concept for the situational selection of production control agents by forecasting human behavior modeled through a reinforcement learner [100] (ES-factor).…”

Section: Production Controlmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Production Controlmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

View full text Add to dashboard Cite

show abstract

“…Increasing this product individualization leads to changes and increases in the complexity of production systems and amplifies the necessity for more flexible production systems [1,2]. Changes in production directly affect both modeling and the simulation of production systems, as these are carried out with the purpose of providing analyses and insights into the up-to-the-minute, real production system [3]. Most notably, to provide insights into complex systems and make decisions, for instance, regarding production changes [4].…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Production Simulation with OntologySim

et al. 2022

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Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve current challenges in modeling and simulation in production science. Due to the individualization and customization of products and the resulting increase in complexity of production, a need for flexibly adaptable simulations arises. This need is exemplified in the trend towards Digital Twins and Digital Shadows. Their application to production systems, against the background of an ever increasing speed of change in such systems, is arduous. Moreover, missing understandability and human interpretability of current approaches hinders successful, goal oriented applications. The OntologySim can help solving this challenge by providing the ability to generate truly cyber physical systems, both interlocked with reality and providing a simulation framework. In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology.

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“…Modern manufacturing is characterized by digitization, high degrees of flexibility and adaptability to respond to individualized demand, increasing process complexities, and omnipresent competition. The plethora of available manufacturing data started an evolution from supply chains to digital supply networks [1], from manufacturing systems to smart manufacturing systems [2], from rule-based priority rules towards intelligent, digital twin-based production control [3], from traditional line production towards complex job shops or matrix production systems [4], from centralized approaches to decentralized [5]. Thus, operational excellence plays an increasingly vital role in manufacturing.…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, formal system modeling and the application of optimization algorithms or powerful heuristics have been used, while currently, digital twins have become predominant [3]. Introduced in 1909 by Erlang [7], queuing systems are widely used in modeling manufacturing systems with stochastically distributed variables.…”

Section: Introductionmentioning

confidence: 99%

“…The former in particular is typically based on Enterprise Resource Planning (ERP) data, Manufacturing Execution System (MES) data, and equipment internal or ad hoc sensors [1]. Analytics can be used for unsupervised tasks, such as clustering and anomaly detection [2], control strategy learning [10], such as reinforcement learning-induced production control [3], and supervised tasks, such as prediction or classification. However, in the latter, the unit of interest is multi-sided and typically focuses on remaining lifetime [11], energy consumption [12] or cycle time [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Queue Length Forecasting in Complex Manufacturing Job Shops

et al. 2021

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Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

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Foresighted digital twin for situational agent selection in production control

Cited by 30 publications

References 25 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ontology-Based Production Simulation with OntologySim

Queue Length Forecasting in Complex Manufacturing Job Shops

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