Robotic Technological Processes Optimization in the Context of Digital Transformation of Industry

Markel Melnichenko, Mikhail Gorkavyy, Yuri Ivanov and Aleksandr Gorkavyy
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Markel Melnichenko: Komsomolsk-on-Amur State University
Mikhail Gorkavyy: Komsomolsk-on-Amur State University
Yuri Ivanov: Komsomolsk-on-Amur State University
Aleksandr Gorkavyy: Komsomolsk-on-Amur State University

A chapter in The Future of Industry, 2024, pp 249-269 from Springer

Abstract: Abstract Modern industrial enterprises equipped with robotic production complexes rely on highly efficient models of technological processes, including energy consumption, to reduce production costs. The paper presents a method for enhancing the energy efficiency of robotic technological processes characterized by a limited class of complexes of movement trajectories, using the optimization algorithms developed by the authors. The presented studies were based on methods of identification, analysis and synthesis, as well as mathematical, in particular, simulation modeling to create models of energy consumption of robotic technological complexes. Automation of the proposed solutions was carried out using intelligent tools in the MATLAB environment. The study investigates robotic technological processes for laying blocks and mechanical processing, identifies the parameters of the processes under consideration, and develops reduced models of energy consumption of industrial robots as part of robotic technological complexes. We present algorithms for optimizing trajectory movements for processes with a predominance of long-stroke movements (cargo stowage) and with a predominance of short-stroke movements (mechanical processing) according to the criteria of minimizing energy consumption and time for performing a technological operation. The paper delivers the results of testing the proposed optimization solutions and the calculation of energy benefits from implementation in production.

Keywords: Neural network model; Digitalization of production; Energy efficiency; Technological processes optimization; Trajectory movements (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-66801-2_17

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DOI: 10.1007/978-3-031-66801-2_17

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