Inspection Data to Support a Digital Twin for Geometry Assurance

2017 ◽  
Author(s):  
Kristina Wärmefjord ◽  
Rikard Söderberg ◽  
Lars Lindkvist ◽  
Björn Lindau ◽  
Johan S. Carlson
Keyword(s):  
Input Data ◽  
Production Systems ◽  
Full Potential ◽  
Real Time Control ◽  
Digital Twin ◽  
Time Control ◽  
Geometrical Quality ◽  
Inspection Strategy ◽  
Full Production ◽  
Inspection Data

Geometrical variation is a problem in all complex, assembled products. Recently, the Digital Twin concept was launched as a tool for improving geometrical quality and reduce costs by using real time control and optimization of products and production systems. The Digital Twin for geometry assurance is created together with the product and the production systems in early design phases. When full production starts, the purpose of the Digital Twin turns towards optimization of the geometrical quality by small changes in the assembly process. To reach its full potential, the Digital Twin concept is depending on high quality input data. In line with Internet of Things and Big Data, the problem is rather to extract appropriate data than to find data. In this paper, an inspection strategy serving the Digital Twin is given. Necessary input data describing form and shape of individual parts, and how this data should be collected, stored and utilized is described.

Individualizing Locator Adjustments of Assembly Fixtures Using a Digital Twin

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Abolfazl Rezaei Aderiani ◽  
Kristina Wärmefjord ◽  
Rikard Söderberg ◽  
Lars Lindkvist
Keyword(s):  
Optimization Algorithm ◽  
Mean Deviation ◽  
Real Time Control ◽  
Simulation Tools ◽  
Digital Twin ◽  
Time Control ◽  
Geometrical Quality ◽  
Full Production ◽  
The Mean ◽  
Scan Data

AbstractImplementing the concept of a digital twin in full production provides enough data on each individual assembly for real-time control of production processes. Taking advantage of this opening, this paper proposes individualized locator adjustments as a new method to improve the geometrical quality of assemblies. In this method, all locators in the assembly fixture can be adjusted for each individual assembly based on the scan data of the mating parts of that assembly. The optimal adjustment of every locator for each individual assembly is obtained using an optimization algorithm and nonrigid variation simulation tools (computer-aided tolerancing tools). This method is applied to three industrial cases and geometrical variations and the mean deviation from nominal positions are compared to nonindividualized adjustments and also when there are no adjustments. The results show that applying this method, an improvement of up to 81% in geometrical variation and 78% in the mean deviation of assemblies can be obtained compared to assemblies without adjustments. These improvements are 60% and 57% higher than nonindividualized adjustments of locators for the variation and the mean deviation, respectively. Moreover, a modification on the optimization algorithm has been proposed that reduces the amount of required adjustments.

scholarly journals Digital Twin-Driven Human Robot Collaboration Using a Digital Human

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8266
Author(s):  
Tsubasa Maruyama ◽  
Toshio Ueshiba ◽  
Mitsunori Tada ◽  
Haruki Toda ◽  
Yui Endo ◽  
...  
Keyword(s):  
Flexible Manufacturing ◽  
Production Management ◽  
Dynamic Scheduling ◽  
Production Systems ◽  
Physical Load ◽  
Real Time Control ◽  
Digital Twin ◽  
Time Control ◽  
Digital Human ◽  
Human Robot Collaboration

Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems.

Data Flow and Communication Framework Supporting Digital Twin for Geometry Assurance

2017 ◽  
Author(s):  
Robert Bohlin ◽  
Jonas Hagmar ◽  
Kristofer Bengtsson ◽  
Lars Lindkvist ◽  
Johan S. Carlson ◽  
...  
Keyword(s):  
Real Time ◽  
Production Systems ◽  
Virtual Manufacturing ◽  
Real Time Control ◽  
Digital Twin ◽  
Sheet Metal Parts ◽  
Time Control ◽  
Project Smart ◽  
Smart Assembly ◽  
Individualized Production

Faster optimization algorithms, increased computer power and amount of available data, can leverage the area of simulation towards real-time control and optimization of products and production systems. This concept — often referred to as Digital Twin — enables real-time geometry assurance and allows moving from mass production to more individualized production. To master the challenges of a Digital Twin for Geometry Assurance the project Smart Assembly 4.0 gathers Swedish researchers within product development, automation, virtual manufacturing, control theory, data analysis and machine learning. The vision of Smart Assembly 4.0 is the autonomous, self-optimizing robotized assembly factory, which maximizes quality and throughput, while keeping flexibility and reducing cost, by a sensing, thinking and acting strategy. The concept is based on active part matching and self-adjusting equipment which improves geometric quality without tightening the tolerances of incoming parts. The goal is to assemble products with higher quality than the incoming parts. The concept utilizes information about individual parts to be joined (sensing), selects the best combination of parts (thinking) and adjust locator positions, clamps, weld/rivet positions and sequences (acting). The project is ongoing, and this paper specifies and highlights the infrastructure, components and data flows necessary in the Digital Twin in order to realize Smart Assembly 4.0. The framework is generic, but the paper focuses on a spot weld station where two robots join two sheet metal parts in an adjustable fixture.

Modeling and Performance Identification for General Production System Using Sensor Data

2016 ◽  
Author(s):  
Jing Zou ◽  
Qing Chang ◽  
Yong Lei ◽  
Jorge Arinez ◽  
Guoxian Xiao
Keyword(s):  
Real Time ◽  
Production System ◽  
Complex Dynamics ◽  
Production Systems ◽  
General Structure ◽  
Sensor Data ◽  
Production Loss ◽  
Real Time Control ◽  
Time Control ◽  
And Performance

The productivity and efficiency of production systems are greatly influenced by their configuration and complex dynamics subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. In this paper, we develop a mathematical model of production systems with general structure (tandem line, parallel, and etc.) to estimate the status of the system (production counts and processing speeds of the stations, buffer levels and production loss) by using sensor data of disruption events. Real-time production system performance such as effective disruption events, opportunity window, and permanent production loss are identified, which is very useful in real-time control to increase overall system efficiency.

A Dynamic Decision Model for the Real-Time Control of Hybrid Renewable Energy Production Systems

2010 ◽  
Vol 4 (3) ◽  
pp. 323-333 ◽  
Author(s):  
Hanane Dagdougui ◽  
Riccardo Minciardi ◽  
Ahmed Ouammi ◽  
Michela Robba ◽  
Roberto Sacile
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Energy Production ◽  
Decision Model ◽  
Production Systems ◽  
Real Time Control ◽  
Time Control ◽  
Renewable Energy Production ◽  
Hybrid Renewable Energy ◽  
Dynamic Decision Model

Maneuvering and Seakeeping Performance of a Generic Tug Based on Numerical Simulations

2020 ◽  
Author(s):  
Yingying Zheng ◽  
Yuting Jin ◽  
Lucas J. Yiew ◽  
Allan R. Magee
Keyword(s):  
Numerical Simulations ◽  
Hydrodynamic Performance ◽  
Scale Model ◽  
Navier Stokes ◽  
Published Data ◽  
Real Time Control ◽  
Hydrodynamic Behavior ◽  
Digital Twin ◽  
Time Control ◽  
Seakeeping Performance

Abstract Autonomous tugs may play an important role in future ports, due to the shortage of qualified mariners. A digital twin (mathematical model incorporating a vessel’s hydrodynamic behavior and response, suitable for real-time control) would be needed for autonomous operations. Yet, partly because tugs are generally high-powered and very maneuverable compared to conventional vessels, there is little published data on the hydrodynamic performance of such vessels. As a first step in the development of the tug’s digital twin, the present work studies the maneuvering and seakeeping performance of a generic tug at model scale. Numerical simulations are performed for an approximately 1:10 scale model for standard resistance, static and dynamic captive and seakeeping cases. Reynolds-Averaged Navier-Stokes (RANS) k-ω model is employed for the simulations including the free surface through the Volume of Fluid approach. The hydrodynamic forces and moments on the tug model in the simulations of the standard resistance and the static and dynamic captive cases, as well as the tug model’s motions and the added resistance in headseas, are investigated. The simulation results provide data to build a mathematical maneuvering model for the tug based on 4-DoF MMG manoeuvring model, which serves as the digital twin in this case.

scholarly journals 5G Based Machine Remote Operation Development Utilizing Digital Twin

2020 ◽  
Vol 10 (1) ◽  
pp. 265-272 ◽  
Author(s):  
Pekka Isto ◽  
Tapio Heikkilä ◽  
Aarne Mämmelä ◽  
Mikko Uitto ◽  
Tuomas Seppälä ◽  
...  
Keyword(s):  
Hardware In The Loop ◽  
5G Networks ◽  
Real Time Control ◽  
Digital Twin ◽  
Time Control ◽  
Remote Operation ◽  
Order Of Magnitude ◽  
Mobile Machinery ◽  
Remote Operations ◽  
Magnitude Improvement

AbstractRemote operations of mobile machinery require reliable and flexible wireless communication. 5G networks will provide ultra-reliable and low latency wireless communications upon whichremote operations, real-time control and data acquisition can be implemented. In this paper we present a demonstration system and first experiments for remote mobile machinery control system utilizing 5G radio and a digital twin with a hardware-in-the-loop development system. Our experimental results indicate that with a suitable edge computing architecture an order of magnitude improvement in delay and jitter over exiting LTE infrastructure can be expected from future 5G networks.

scholarly journals The Digital Twin Realization of an Ejector for Multiphase Flows

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5533
Author(s):  
Giovanni Mazzuto ◽  
Filippo Emanuele Ciarapica ◽  
Marco Ortenzi ◽  
Maurizio Bevilacqua
Keyword(s):  
Multiphase Flows ◽  
Pressure Control ◽  
Computational Effort ◽  
Grey Wolf Optimizer ◽  
Experimental Plant ◽  
Real Time Control ◽  
Digital Twin ◽  
Time Control ◽  
Faults Diagnosis ◽  
The Impact

Despite the extensive use of ejectors in the process industry, it is complex to predict suction and motive fluids mixture characteristics, especially with multiphase flows, even if, in most cases, mixture pressure control is necessary to satisfy process requirements or to avoid performance problems. The realization of an ejector model can allow the operators to overcome these difficulties to have real-time control of the system performance. In this context, this work proposes a framework for developing a Digital Twin of an ejector installed in an experimental plant able to predict the future state of an item and the impact of negative scenarios and faults diagnosis. ANNs have been identified as the most used tool for simulating the multiphase flow ejector. Nevertheless, the complexity in defining their structure and the computational effort to train and use them are not suitable for realizing standalone applications onboard the ejector. The proposed paper shows how Swarm Intelligence algorithms require a low computational complexity and overperform prediction error and computational effort. Specifically, the Grey Wolf optimizer proves to be the best one among those analyzed.

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