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.

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.

scholarly journals Multi-Agents Architecture for Scheduling and Control of Robotic Manufacturing Systems

2002 ◽  
Vol 7 (1) ◽  
pp. 177
Author(s):  
Brahim Bouzouia
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Prototype System ◽  
Multi Agent Systems ◽  
Real Time Control ◽  
Distributed Intelligence ◽  
Time Control ◽  
Multi Agent ◽  
And Control ◽  
Multi Agents

This paper presents an  innovative Multi-Agent approach related to an advanced real time control  of flexible manufacturing systems. The proposed architecture is based on the paradigm of Distributed Intelligence and Multi-Agent Systems. The developed  Multi-Agent prototype system integrates  the following functions: Scheduling, dispatching, monitoring and error handling. A new negotiation protocol for manufacturing systems is presented in this paper. The purpose of this protocol is to assign dynamic operations to the resources of the Manufacturing System in order to accomplish the proposed tasks. This protocol is able to deal with exceptions.  

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.

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