Model-Based Assembly Optimization for Unbalance-Minimized Production Automation of Electric Motors

Step-based evolution support among networked production automation systems

at - Automatisierungstechnik ◽

10.1515/auto-2018-0047 ◽

2018 ◽

Vol 66 (10) ◽

pp. 849-858

Author(s):

Christopher Haubeck ◽

Heiko Bornholdt ◽

Winfried Lamersdorf ◽

Abhishek Chakraborty ◽

Alexander Fay

Keyword(s):

Production Systems ◽

Cyber Physical Systems ◽

Peer To Peer ◽

Evolution Process ◽

Physical Systems ◽

Model Based ◽

Automation Systems ◽

Production Automation ◽

Isolated Systems

Abstract Production systems are no longer rigid, unyielding, and isolated systems anymore. They are rather interconnected cyber-physical systems with an evolution process that needs to be supported. To enable reusability in evolution, a change-first cooperative support is proposed that relies on model-based evolution steps. The approach establishes a network-wide evolution process in a peer-to-peer networked community. Thus, moving towards decentralised marketplaces for evolution steps.

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Analysis of metamodels for model‐based production automation system engineering

IET Collaborative Intelligent Manufacturing ◽

10.1049/iet-cim.2020.0013 ◽

2020 ◽

Vol 2 (2) ◽

pp. 45-55 ◽

Cited By ~ 1

Author(s):

Suhyun Cha ◽

Birgit Vogel‐Heuser ◽

Juliane Fischer

Keyword(s):

System Engineering ◽

Automation System ◽

Model Based ◽

Production Automation

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Review of Model-Based Testing Approaches in Production Automation and Adjacent Domains—Current Challenges and Research Gaps

Journal of Software Engineering and Applications ◽

10.4236/jsea.2015.89048 ◽

2015 ◽

Vol 08 (09) ◽

pp. 499-519 ◽

Cited By ~ 25

Author(s):

Susanne Rösch ◽

Sebastian Ulewicz ◽

Julien Provost ◽

Birgit Vogel-Heuser

Keyword(s):

Research Gaps ◽

Model Based ◽

Model Based Testing ◽

Production Automation

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Meta-Modeling for Enhancing Design Optimization of Electric Motors

Volume 2B: 42nd Design Automation Conference ◽

10.1115/detc2016-59356 ◽

2016 ◽

Author(s):

Wei Ji ◽

Xiaolong Feng ◽

Jonas Larsson ◽

Alexander Stening ◽

Freddy Gyllensten ◽

...

Keyword(s):

Design Optimization ◽

Low Voltage ◽

Network Models ◽

Electric Motors ◽

Neural Network Models ◽

Meta Model ◽

Model Based ◽

Design Variables ◽

Objective Space ◽

Meta Modeling

In this work, meta-models for use in design optimization of low voltage motors are investigated. The idea is to develop an automated and efficient methodology for design optimization of a family of electric motors. A few widely adopted meta-modeling algorithms are examined with concerns of their accuracy and applicability for design optimization of the motors. Meta-model based optimization is conducted for a case of single motor with two objectives, and another case of a group of motors with shared design variables of cross-section dimensions and with an overall objective of total material cost. Meta-model based optimal designs are verified with that from real solver based optimization. Computational expense for optimization simulations can be greatly reduced by using meta-models, especially for the family design case. Neural network models give the most satisfactory optimization result among all tested meta-models, in terms of accuracy and variety of the outcome designs in the objective space. This work demonstrates great potential as well as challenge of meta-modeling technique for use in design optimization of industrial products and processes, where requirement on accuracy and reliability of the surrogate models being high.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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