Configuration and Enablement of Vision Sensor Solutions Through a Combined Simulation Based Process Chain

Abstract Machine vision solutions can perform within a wide range of applications and are commonly used to verify the operation of production systems. They offer the potential to automatically record assembly states and derive information, but simultaneously require a high effort of planning, configuration and implementation. This generally leads to an iterative, expert based implementation with long process times and sets major barriers for many companies. Furthermore the implementation is task specific and needs to be repeated with every variation of product, environment or process. Therefore a novel concept of a simulation-based process chain for both—configuration and enablement—of machine vision systems is presented in this paper. It combines related work of sensor planning algorithms with new methods of training data generation and detailed task specific analysis for assembly applications.

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EIPPM—The Executable Integrative Product-Production Model

Computers ◽

10.3390/computers10060072 ◽

2021 ◽

Vol 10 (6) ◽

pp. 72

Author(s):

Dominik Schopper ◽

Karl Kübler ◽

Stephan Rudolph ◽

Oliver Riedel

Keyword(s):

Data Model ◽

Production Systems ◽

Simulation Models ◽

Automatic Generation ◽

Production Model ◽

The Novel ◽

Automatic Translation ◽

Virtual Commissioning ◽

Simulation Based ◽

Novel Concept

In this paper, a combination of graph-based design and simulation-based engineering (SBE) into a new concept called Executable Integrative Product-Production Model (EIPPM) is elaborated. Today, the first collaborative process in engineering for all mechatronic disciplines is the virtual commissioning phase. The authors see a hitherto untapped potential for the earlier, integrated and iterative use of SBE for the development of production systems (PS). Seamless generation of and exchange between Model-, Software- and Hardware-in-the-Loop simulations is necessary. Feedback from simulation results will go into the design decisions after each iteration. The presented approach combines knowledge of the domain “PSs” together with the knowledge of the corresponding “product” using a so called Graph-based Design Language (GBDL). Its central data model, which represents the entire life cycle of product and PS, results of an automatic translation step in a compiler. Since the execution of the GBDL can be repeated as often as desired with modified boundary conditions (e.g., through feedback), a design of experiment is made possible, whereby unconventional solutions are also considered. The novel concept aims at the following advantages: Consistent linking of all mechatronic disciplines through a data model (graph) from the project start, automatic design cycles exploring multiple variants for optimized product-PS combinations, automatic generation of simulation models starting with the planning phase and feedback from simulation-based optimization back into the data model.

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Development of a Simulation-Based Process Chain – Strategy for Different Levels of Detail for the Preprocessing Definitions

SNE Simulation Notes Europe ◽

10.11128/sne.21.tn.10081 ◽

2011 ◽

Vol 21 (3-4) ◽

pp. 135-140 ◽

Cited By ~ 5

Author(s):

Toni A. Krol ◽

Sebastian Westhäuser ◽

M. F. Zäh ◽

Johannes Schilp ◽

G. Groth

Keyword(s):

Process Chain ◽

Levels Of Detail ◽

Simulation Based ◽

Different Levels

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MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

Proceedings of the Design Society ◽

10.1017/pds.2021.474 ◽

2021 ◽

Vol 1 ◽

pp. 2127-2136

Author(s):

Olivia Borgue ◽

John Stavridis ◽

Tomas Vannucci ◽

Panagiotis Stavropoulos ◽

Harry Bikas ◽

...

Keyword(s):

Additive Manufacturing ◽

Manufacturing Systems ◽

Production Systems ◽

Data Generation ◽

Production Networks ◽

Model Based ◽

Manufacturing Technologies ◽

Am Processes ◽

Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

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Efficient Training Data Generation for Phase-Based DOA Estimation

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414070 ◽

2021 ◽

Author(s):

Fabian Hubner ◽

Wolfgang Mack ◽

Emanuel A. P. Habets

Keyword(s):

Doa Estimation ◽

Training Data ◽

Data Generation

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BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽

10.3390/s21062144 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2144

Author(s):

Stefan Reitmann ◽

Lorenzo Neumann ◽

Bernhard Jung

Keyword(s):

Point Clouds ◽

Training Data ◽

Sensor Data ◽

Generation Process ◽

Data Generation ◽

Depth Sensor ◽

Depth Sensing ◽

Depth Sensors ◽

3D Point Clouds ◽

Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

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Synthetic Training Data Generation for ML-based Small-Signal Stability Assessment

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) ◽

10.1109/smartgridcomm47815.2020.9302991 ◽

2020 ◽

Author(s):

Sergio A. Dorado-Rojas ◽

Marcelo de Castro Fernandes ◽

Luigi Vanfretti

Keyword(s):

Training Data ◽

Small Signal ◽

Data Generation ◽

Stability Assessment ◽

Small Signal Stability ◽

Signal Stability ◽

Synthetic Training Data

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Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task Learning

ACM Transactions on Embedded Computing Systems ◽

10.1145/3391906 ◽

2020 ◽

Vol 19 (6) ◽

pp. 1-26

Author(s):

Luke Hsiao ◽

Sen Wu ◽

Nicholas Chiang ◽

Christopher Ré ◽

Philip Levis

Keyword(s):

Knowledge Bases ◽

Training Data ◽

Data Generation ◽

Task Learning ◽

Hardware Component

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Adaptive PPS durch simulationsbasierte Optimierung*/Adaptive PPC by simulation-based optimization

wt Werkstattstechnik online ◽

10.37544/1436-4980-2017-04-92 ◽

2017 ◽

Vol 107 (04) ◽

pp. 288-292

Author(s):

M. Kück ◽

J. Ehm ◽

T. Hildebrandt ◽

M. Prof. Freitag ◽

E. M. Prof. Frazzon

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Production Systems ◽

Semiconductor Industry ◽

Small Scale ◽

Planning Problem ◽

Optimization Approach ◽

Flexible Production Systems ◽

Simulation Based ◽

Simulation Based Optimization

Der Trend zur Fertigung individualisierter Produkte in kleinen Losgrößen erfordert hochflexible Produktionssysteme. Durch die damit verbundene Systemdynamik wird die Reihenfolgeplanung zu einem komplexen Planungsproblem. Der Beitrag beschreibt ein simulationsbasiertes Optimierungsverfahren, welches Echtzeitinformationen zur adaptiven Selektion geeigneter Prioritätsregeln verwendet. Das Potenzial des Ansatzes wird anhand eines Anwendungsfalls aus der Halbleiterindustrie demonstriert.   The trend to manufacturing individualized products in small-scale series demands highly flexible production systems. Because of the dynamic nature of such production systems, scheduling becomes a complex planning problem with frequent need for rescheduling. This article describes a data-driven simulation-based optimization approach using real-time information for adaptive job shop scheduling. The potential of the approach is demonstrated by a use case from semiconductor industry.

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