The article presents the issue of calibration and verification of an original module, which is a part of the robotic turbojet engines elements processing station. The task of the module is to measure turbojet engine compressor blades geometric parameters. These type of devices are used in the automotive and the machine industry, but here we present their application in the aviation industry. The article presents the idea of the module, operation algorithm and communication structure with elements of a robot station. The module uses Keyence GT2-A32 contact sensors. The presented information has an application nature. Functioning of the module and the developed algorithm has been tested, the obtained results are satisfactory and ensure sufficient process accuracy. Other station elements include a robot with force control, elements connected to grinding such as electrospindles, and security systems.
In the article problems related to human labor and factors affecting the increasing use of industrial robots are discussed. Since human factors affect the production processes stability, robots are preferred to apply. The application of robots is characterized by higher performance and reliability comparing to human labor. The problem is how to determine the real difference in work efficiency between human operator and robot. The aim of the study is to develop a method that allows clearly definition of productivity growth associated with the replacement of human labor by industrial robots. Another aim of the paper is how to model robotized and manual operated workstation in a computer simulation software. Analysis of the productivity and reliability of the hydraulic press workstation operated by the human operator or an industrial robot, are presented. Simulation models have been developed taking into account the availability and reliability of the machine, operator and robot. We apply OEE (Overall Equipment Effectiveness) indicator to present how availability and reliability parameters influence over performance of the workstation, in the longer time. Simplified financial analysis is presented considering different labor costs in EU countries.
Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.