Another of the basic applications of force control systems in robots are assembly tasks. In the first uses of assembly applications, pliable grippers with susceptibility were used to ensure high tolerance of the movement accuracy of a robot's arm. Such systems were gradually replaced or supplemented with systems with force control [16,17]. This approach allows the avoidance of blocking components during assembly, checking the clamping pressure of the assembled components to avoid overloading, or detecting a mismatch of components due to incorrect fitting- too tight or loose. The robotization of the assembly of flexible components, as described in [18,19] is a particular challenge. In such cases, force control is particularly important, because too high interaction forces between the assembled components lead to large distortions and prevent the correct process from running.
Monitoring of the Machining Process
Monitoring the machining process is important in the industrial production of parts with a high unit cost. Any irregularity in the production of the part causing its non-compliance with the documentation is a cause of significant financial losses. Process monitoring aims to prevent irregularities during its implementation and to correct or discontinue the machining process. Process monitoring involves observing (measuring) sizes in the process in a direct or indirect way. In the case of machining, the aim of which is to achieve the appropriate geometric dimensions or surface roughness, direct measurement of these quantities is not possible during the process. Therefore, signals related to phenomena accompanying the treatment process are monitored. These accompanying phenomena are, for example, vibrations, noise, cutting forces or heat generation (Figure 3). These phenomena can be quantified using signals such as accelerations, acoustic pressure, interaction forces or temperature. Irregularities in the implementation of the process cause a discrepancy between the levels of measured values associated with the accompanying phenomena and reference levels for those quantities determined for the correctly carried out process.
Figure 3: Machining process monitoring and control.
1. Wu Y, Chen S. Adaptive Neural Motion/Force Control of Constrained Robot Manipulators by Position Measurement. 2011 Seventh International Conference on Natural Computation. 2011;1:498-502.
2. Ohishi K, Miyazaki M, Fujita M, Ogino Y. Hybrid Control of Robot Manipulator without Force Sensor. IFAC Proc. 1993;26:991-996.
3. Marvel J, Falco J. Best Practices and Performance Metrics Using Force Control for Robotic Assembly. National Institute of Standards and Technology. 2012.
4. Cherubini A, Navarro-Alarcon D. Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities. Front Neurorobot. 2021;14:113.
5. Gierlak P, Burghardt A, Szybicki D, Kurc K. Eliminating the Inertial Forces Effects on the Measurement of Robot Interaction Force. Lect Notes Electr Eng. 2019;548:67-76.
6. Murakami T, Yu F, Ohnishi K. Torque Sensor less Control in Multidegree-of-Freedom Manipulator. IEEE Trans Ind Electron. 1993;40:259-265.
7. Gierlak P. Hybrid Position/Force Control in Robotised Machining. Solid State Phenom. 2014;210:192-199.
8. Gierlak P. Hybrid Position/Force Control of the SCORBOT-ER 4pc Manipulator with Neural Compensation of Nonlinearities. Lect Notes Comput Sci. 2012;7268:433-441.
9. Muszy?ska M, Burghardt A, Kurc K, Szybicki D. Verification Hybrid Control of A Wheeled Mobile Robot and Manipulator. Open Eng. 2016;6:64-72.
10. Application Manual. Force Control for Machining. ABB Robotics. 2011.
11. Lotz M, Bruhm H, Czinki A. A New Force Control Strategy Improving the Force Control Capabilities of Standard Industrial Robots. J Mech Eng Autom. 2014;4:276-283.
12. Bruhm H, Czinki A, Lotz M. High Performance Force Control – A New Approach and Suggested Benchmark Tests. IFAC-Papers OnLine. 2015;48:165-170.
13. Pliego-Jiménez J, Arteaga-Pérez MA. Adaptive Position/Force Control for Robot Manipulators in Contact with a Rigid Surface with Uncertain Parameters. Eur J Control. 2015;22:1-12.
14. Gierlak P. Combined Strategy for Control of Interaction Force between Manipulator and Flexible Environment. J Control Eng Appl Informat. 2018;20:64-75.
15. Gierlak P, Szuster M. Adaptive Position/Force Control for Robot Manipulator in Contact with a Flexible Environment. Robot Auton Sys. 2017;95:80-101.
16. Roveda L, Pedrocchi N, Beschi M, et al. High-Accuracy Robotized Industrial Assembly Task Control Schema with Force Overshoots Avoidance. Control Eng Practice. 2018;71:142-153.
17. Kramberger A, Gams A, Nemec B, et al. Generalization of Orientation Trajectories and Force-Torque Profiles for Robotic Assembly. Robot Auton Sys. 2017;98:333-346.
18. Lee DH, Na MW, Song JB, et al. Assembly Process Monitoring Algorithm Using Force Data and Deformation Data. Robot Comput.-Integr Manuf. 2019;56:149-156.
19. Heyn J, Gümbel P, Bobka P, et al. Application of Artificial Neural Networks in Force-Controlled Automated Assembly of Complex Shaped Deformable Components. Procedia CIRP. 2019;79:131-136.
20. Pilný L, Bissacco G. Development of on the Machine Process Monitoring and Control Strategy in Robot Assisted Polishing. CIRP Annals. 2015;64:313-316.
21. Pandiyan V, Caesarendra W, Tjahjowidodo T, et al. In-Process Tool Condition Monitoring in Compliant Abrasive Belt Grinding Process Using Support Vector Machine and Genetic Algorithm. J Manuf Proc. 2018;31:199-213.
22. Sugita N, Nakano T, Nakajima Y, et al. Dynamic Controlled Milling Process for Bone Machining. J Mater Process Technol. 2009;209:5777-5784.
23. Thomas WM, Threadgill PL, Nicholas ED. Feasibility of Friction Stir Welding Steel. Sci Technol Weld Joining. 1999;4:365-372.
24. Callegari M, Forcellese A, Palpacelli M, et al. Robotic Friction Stir Welding of AA5754 Aluminum Alloy Sheets at Different Initial Temper States. Key Eng Mater. 2014;622:540-547.
25. Guo J, Gougeon P, Nadeau F, et al. Joining of AA1100–16 Vol.-% B4C Metal Matrix Composite Using Laser Welding and Friction Stir Welding. Can Metall. 2012;51:277-283.
26. Watanabe T, Takayama H, Yanagisawa A. Joining of Aluminum Alloy to Steel by Friction Stir Welding. J Mater Process Technol. 2006;178:342-349.
27. De Backer J, Christiansson AK, Oqueka J, et al. Investigation of Path Compensation Methods for Robotic Friction Stir Welding. Ind Robot. 2012:39:601-608.
28. De Backer J, Bolmsjö G. Deflection Model for Robotic Friction Stir Welding. Ind Robot. 2014;41:365-372.
29. Bres A, Monsarrat B, Dubourg L, et al. Simulation of Friction Stir Welding Using Industrial Robots. Ind Robot. 2010;37:36-50.
30. Mendes N, Neto P, Loureiro A, et al. Machines and Control Systems for Friction Stir Welding: A Review. Mater Des. 2016;90:256-265.
31. Bitondo C, Prisco U, Squillace A, et al. Friction Stir Welding of AA2198-T3 Butt Joints for Aeronautical Applications. Int J Mater Form. 2010;3:1079-1082.
32. Gesella G, Czechowski M. The Application of Friction Stir Welding (FSW) of Aluminium Alloys in Shipbuilding and Railway Industry. J KONES. 2017;24.
33. Ionescu TB, Schlund S. Programming Cobots by Voice: A Human-Centered, Web-Based Approach. Procedia CIRP. 2021;97:123-129.
34. Bi ZM, Luo M, Miao Z, et al. Safety Assurance Mechanisms of Collaborative Robotic Systems in Manufacturing. Robot Comput Integr Manuf. 2021;67:102022.
35. Malik AA, Brem A. Digital Twins for Collaborative Robots: A Case Study in Human-Robot Interaction. Robot Comput Integr Manuf. 2021;68:102092.
Corresponding Author: Dr. Piotr Gierlak, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Poland.
Copyright: © 2021 All copyrights are reserved by Piotr Gierlak, published by Coalesce Research Group. This This work is licensed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.