Influence on the Positioning Accuracy of an Industrial Robot During Operation with a Lathe

Abstract

The aim of this study is to improve the positioning accuracy and operational stability of gantry robots integrated into machine-tool systems by developing a systematic approach that combines algorithmic error compensation techniques with structural and layout optimization solutions. A comprehensive investigation of a gantry robotic system integrated into the technological system of a metal-cutting machine tool was conducted. The study included a detailed visual inspection of the working area, the end-effector (gripper), and the control system. The obtained results not only characterize the actual operating conditions of the robotic complex but also provide a scientifically substantiated basis for assessing the influence of an aggressive production environment on the positioning accuracy and operational stability of the robot's actuating mechanisms. This paper addresses current issues related to ensuring the reliable operation of robotic technological complexes (RTCs) based on CNC turning machines, with particular emphasis on evaluating the variation in positioning accuracy of robotic actuators under real industrial operating conditions. The destructive effects of production environment factors – including the aggressive action of cutting fluids (CFs) and the adhesion of fine metallic chips – on the kinematic pairs and clamping elements of the industrial robot gripper are comprehensively analyzed. These factors lead to progressive positioning errors, deterioration of gripping performance, and a reduction in the reliability of workpiece fixation. To mitigate these adverse effects, a comprehensive engineering solution is proposed. The proposed approach involves modernizing the robot control algorithms by incorporating dedicated adaptive manipulator operating cycles, as well as integrating an additional feedback sensor into the control system for continuous monitoring of the condition and actuation parameters of the clamping mechanism. The combined implementation of these measures enhances the autonomy of the robotic technological complex while reducing the defect rate of manufactured products. Further improvement in the operational efficiency of the robotic system is proposed through the enhancement of its control architecture. In particular, the integration of additional condition-monitoring sensors, the application of adaptive positioning error compensation algorithms, and the implementation of machine learning techniques for predictive diagnostics of critical mechanical assemblies are recommended. The implementation of these solutions will enable early detection of equipment wear, reduce unplanned downtime, increase the reliability of the manufacturing process, and ensure stable positioning accuracy and repeatability of technological operations even under severe industrial operating conditions.