Actual Problems in Machine Building. Vol. 13. N 3-4. 2026 Innovative Technologies in Mechanical Engineering ____________________________________________________________________ 14 AUTOMATED CONTROL OF GEOMETRIC PARAMETERS OF PIPELINE PRODUCTS FOR LUBRICATION SYSTEMS IN AUTOMOTIVE ENGINEERING Pirozhkov V.E., Graduate students, e-mail: vadim2013p@gmail.com Zhargalova A.D., Senior Lecturer, e-mail: azhargalova@bmstu.ru Bessonova V.E., Student, e-mail: vikovikoviii@mail.ru Bauman Moscow State Technical University, 5 2nd Baumanskaya Street, Moscow, 105005, Russian Federation Abstract The paper focuses on the control of geometric parameters of pipeline products used in automotive lubrication systems. The study considers thin-walled copper and brass tubes manufactured by bending and end sections forming operations. It determines the main controlled parameters, including bend angle, bend radius, tube axis deviation, and the geometry of formed end sections. The requirements for the geometry of bends and formed end sections, as well as existing control methods, are analyzed. The limitations of traditional approaches, namely high labor intensity and operator dependence, are demonstrated. To solve this problem, the authors propose an automated inspection approach based on machine vision integrated into bending and forming equipment. The structure of the automated inspection system and the algorithm of its operation are developed. The system uses cameras, image processing algorithms, a neural network, and a database of reference values synchronized with design documentation. The practical implementation of integrating the inspection operation into forming and tube bending equipment is considered. This integration allows for improved inspection accuracy, reduced labor intensity, and the collection of statistical data on product quality. The proposed approach reduces inspection time from 10–20 s to 1–2 s per part and supports continuous quality monitoring Keywords pipeline products, geometric parameter control, tube bending, tube end sections forming, machine vision, neural networks, automated inspection, automotive engineering
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