Self-optimizing Production Systems

Permin, Eike; Bertelsmeier, Felix; Blum, Matthias; Bützler, Jennifer; Haag, Sebastian; Kuz, Sinem; Özdemir, Denis; Stemmler, Sebastian; Thombansen, Ulrich; Schmitt, Robert; Brecher, Christian; Schlick, Christopher; Abel, Dirk; Poprawe, Reinhart; Loosen, Peter; Schulz, Wolfgang; Schuh, Günther

doi:10.1016/j.procir.2015.12.114

Cited by 49 publications

(27 citation statements)

References 7 publications

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“…Mourtzis et al described an advanced engineering educational approach where thermosiphon production line efficiency was increased by 28% [22]. Permin et al determined a self-optimizing assembly system with model-based interpretation in three steps [23].…”

Section: Possibilities To Increase Oee-values In the Manufacturingmentioning

confidence: 99%

OEE measurement at the automotive semi-automatic assembly lines

Dobra¹,

Jósvai

2021

Acta Tech. Jaurinensis

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Manufacturing companies continuously evaluate their achieved performance based on different Key Performance Indicators (KPI). This article gives an overview about the OEE values. The study aims to provide practical OEE data of semi-automatic assembly lines used in the automotive industry. Its novelty is the revealed relationship between seat assembly lines and seat subassembly lines. Firstly, a literature review shows the scientific relevance and several cases are collected to increase OEE percentage. Secondly, the connection between chassis, tracks, recliner and mechanism assembly lines is described. Each part of OEE (availability, performance, quality) are analysed in terms of their impact.

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Section: Possibilities To Increase Oee-values In the Manufacturingmentioning

confidence: 99%

OEE measurement at the automotive semi-automatic assembly lines

Dobra¹,

Jósvai

2021

Acta Tech. Jaurinensis

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“…In the context of PPC, Permin et al describe self-optimizing systems as one apporach to manage complexity. They explicitely refer to rescheduling in supply and turbulences in supply networks [12]. In general, the findings of Usuga Cadavid et al indicate that about 75% of the observed research domains in machine-learning-based PPC, such as time estimation, smart planning and scheduling or inventory and distribution control are "barely addressed or were not explored at all" [13].…”

Section: Motivationmentioning

confidence: 99%

Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation

Bauer

Böhm

Bauernhansl

et al. 2021

Prod. Eng. Res. Devel.

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In manufacturing systems, a state of high resilience is always desirable. However, internal and external complexity has great influence on these systems. An approach is to increase manufacturing robustness and responsiveness—and thus resilience—by manufacturing control. In order to execute an effective control method, it is necessary to provide sufficient information of high value in terms of data format, quality and time of availability. Nowadays, raw data is available in large quantities. An obstacle to manufacturing control is the short-term handling of events induced by customers and suppliers. These events cause different kinds of turbulence in manufacturing systems. If such turbulences could be evaluated in advance, based on data processing, they could serve as aggregated input data for a control system. This paper presents an approach how to combine turbulence evaluation and the derivation of measures into a learning system for turbulence mitigation. Integrated in manufacturing control, turbulence mitigation increases manufacturing resilience and strengthens the supply network’s resilience.

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“…such systems on the utilization of data to generate knowledge about the process and environment, which can be used to influence the system control. These systems are capable of demonstrating a level of computational intelligence and are capable of autonomous: self-awareness & prediction; [10]; self-configuration [11]; and self-optimization [12]. Combined, these self-x capabilities enable improved adaptability; efficiency; functionality; reliability; safety; and usability [13]; Cyber-Physical Systems are typically defined by their self-x capability and the degree to which they are able to generate knowledge from data, which in turn is used to provide different capabilities, with more advanced systems capable of autonomous planning, adaptation, and self-configuration [14,15].…”

Section: A Intelligent Manufacturingmentioning

confidence: 99%

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

Oliff

Liu

Kumar

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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Several decades of development in the fields of robotics and automation has resulted in human-robot-interaction being commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators have been employed in a number of robotics and automation tasks. The presence of human operators continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This work examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programmed behaviour, and a methodology developed to further explore the application of learning techniques to this area. The work is supported by an example case-study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realised with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. NTP: This paper presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated and used, within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behaviour to reduce the impact of human performance variation and improve human-machine-interaction. The paper also presents a simulation-based case study, to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine learning approach to identify the complex influence of a number of identified human factors on human performance. This knowledge can be used to adjust the robotic behaviour to match the predicted performance of a number of different operators over a number of scenarios. The adaptability reduces performance disparity, reducing idle times and enabling leaner production through WIP reduction. Future work

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Self-optimizing Production Systems

Cited by 49 publications

References 7 publications

OEE measurement at the automotive semi-automatic assembly lines

OEE measurement at the automotive semi-automatic assembly lines

Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation

Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach

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