Transforming part-sequencing problems in a robotic cell into a GTSP

W Zahrouni; H Kamoun

doi:10.1057/jors.2009.158

A Petri Net Model for Part Sequencing and Robot Moves Sequence in A 2-Machine Robotic Cell

Journal of Software Engineering and Applications ◽

10.4236/jsea.2011.411071 ◽

2011 ◽

Vol 04 (11) ◽

pp. 603-608 ◽

Cited By ~ 4

Author(s):

Mohammad Fathian ◽

Isa Nakhai Kamalabadi ◽

Mehdi Heydari ◽

Hiwa Farughi

Keyword(s):

Petri Net ◽

Robotic Cell ◽

Part Sequencing

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Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

Applied Sciences ◽

10.3390/app11125398 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5398

Author(s):

Tomáš Kot ◽

Zdenko Bobovský ◽

Aleš Vysocký ◽

Václav Krys ◽

Jakub Šafařík ◽

...

Keyword(s):

Angular Velocity ◽

Dynamic Simulation ◽

Robot Manipulator ◽

The Other ◽

Robotic Arm ◽

Robotic Cell ◽

Wear Reduction ◽

Fixed End ◽

Robot Placement ◽

Collaborative Robot

We describe a method for robotic cell optimization by changing the placement of the robot manipulator within the cell in applications with a fixed end-point trajectory. The goal is to reduce the overall robot joint wear and to prevent uneven joint wear when one or several joints are stressed more than the other joints. Joint wear is approximated by calculating the integral of the mechanical work of each joint during the whole trajectory, which depends on the joint angular velocity and torque. The method relies on using a dynamic simulation for the evaluation of the torques and velocities in robot joints for individual robot positions. Verification of the method was performed using CoppeliaSim and a laboratory robotic cell with the collaborative robot UR3. The results confirmed that, with proper robot base placement, the overall wear of the joints of a robotic arm could be reduced from 22% to 53% depending on the trajectory.

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Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s179396231850040x ◽

2018 ◽

Vol 09 (05) ◽

pp. 1850040 ◽

Cited By ~ 1

Author(s):

Chuyuan Wang ◽

Linxuan Zhang ◽

Chongdang Liu

Keyword(s):

Real Time ◽

Pattern Classification ◽

Feature Selection Method ◽

Classification Algorithm ◽

Adaptive Scheduling ◽

Gradient Boosting ◽

Production Environment ◽

Robotic Cell ◽

Extreme Gradient Boosting ◽

Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

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Evaluation of assembly routines with multitasking execution in a physical robotic cell

Computers & Industrial Engineering ◽

10.1016/0360-8352(89)90065-x ◽

1989 ◽

Vol 17 (1-4) ◽

pp. 221-226 ◽

Cited By ~ 2

Author(s):

Sergio San Martin ◽

Richard H. Choi

Keyword(s):

Robotic Cell

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Experimental analysis of the automated process of sanding aircraft surfaces

The Aeronautical Journal ◽

10.1017/s0001924000008927 ◽

2014 ◽

Vol 118 (1199) ◽

pp. 53-64

Author(s):

B. Giublin ◽

J. A. Vieira ◽

T. G. Vieira ◽

L. G. Trabasso ◽

C. A. Martins

Keyword(s):

Load Capacity ◽

Manufacturing Processes ◽

Robotic Cell ◽

Research Project ◽

Average Value ◽

Aluminum Metal ◽

Structural Assembly ◽

Riveting Process ◽

High Load Capacity ◽

Operating Method

Abstract ITA and EMBRAER are currently executing the research project Automation of Aircraft Structural Assembly (AASA) whose goal is to implement a robotic cell for automating the riveting process of aeronautical structures. The proposal described herein complements the AASA project, adds other manufacturing processes, namely sanding and polishing of aircraft surfaces. To implement the additional processes AASA project resources and facilities were used (robots and metrology systems) and devices designed and /or acquired to allow sharing of these resources. Among these, an Automatic Tooling Support for AERonautics structures (ATS_AER) was designed and built; also, a robot tool changer with high load capacity was acquired. The outcome of this research project is the evaluation of the feasibility of automating the processes of sanding and polishing metal surfaces in the aircraft manufacture using robots. The operating method adopted for surface treatment employed the ‘U’ type trajectory optimised to be run by a KUKA robot KR 500. The sanding process has been applied to aluminum metal sheet specimen sized 2•18ft2 (0•20m2) and used commercial 600 and 800 sandpaper. The automated sanding process yielded an average value of RA 0•48 ± 0•08 which is 25% more efficient when compared to the traditional, manual process whose average value of RA is 0•75 ± 0•51.

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