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Optimization techniques for machining processes performed with an ABB anthropomorphic robot equipped with Force Control

Stefania Gabutto

Optimization techniques for machining processes performed with an ABB anthropomorphic robot equipped with Force Control.

Rel. Alessandro Rizzo. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021


In modern industrial processes there is an increasing involvement of anthropomorphic robots, which are normally controlled in position and programmed to execute predefined trajectories. However, in some specific processes such as machining processes, the use of this technology does not allow to obtain a quality, in terms of accuracy and finishing of the machining, comparable to that obtained by performing the operations manually. This made it necessary to introduce new technologies to make the machines capable of reacting to their surroundings. One of the most innovative technologies introduced in the robotics field is force control. The use of a 6 degrees of freedom load cell sensor integrated in the control loops of an anthropomorphic robot allows to provide the robot with an almost immediate feedback on the forces and torques exerted at the end-effector level. This dissertation, carried out at ABB Italy's Global Solution Center (GSC), focuses on the optimization of machining processes through the use of ABB's integrated Force Control (FC). The goal of this thesis work is the optimization of mechanical processing performed with robots and the analysis of the limits, potentials and advantages of using this technology in machining operations. For this purpose, laboratory tests were carried out on a certain number of concrete cases carefully selected as representative of many production needs. In particular, the thesis work focused on weld seams removal process, deburring and assembling. Numerous tests were also carried out to highlight and try to solve the limits of the force sensor. To perform these tests, various software solutions were developed, including a function for tool compensation and a graphical user interface for the robot FlexPendant. The latter allows the user to activate the force control for lead-through programming, to configure the force control parameters and to have the robot perform the processing taught to it thanks to the self-generation of the program modules during the learning process. In order to optimize the machining processes and minimize the cycle time, it was also necessary to study and test the different types of abrasive materials. Through the tests, it was possible to define the precautions to be adopted for the optimal configuration of the robot with force control, in relation to the type of processing, the type of material and the cycle time. The tests also highlighted the limits and possible improvements to be proposed in the field of robotic machining processes with the aid of force control. The results obtained show that not all the processes can be improved using force control, but for most machining processes, through an accurate tuning, this technology improves quality and reduces programming times, making the problem of alignment of the piece secondary.

Relators: Alessandro Rizzo
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 109
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: ABB SpA
URI: http://webthesis.biblio.polito.it/id/eprint/19219
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