Year: 2026
Type: article
Abstract: The object of this research is the process of inspecting the camshafts of internal combustion engines in the context of vehicle repair production. As the inspection of a camshaft is a highly labor-intensive process due to the need to measure a large number of its components, there is a pressing need to improve the efficiency of inspection work. An approach is proposed for inspecting components with multiple worn surfaces, which involves determining the wear on one surface and calculating the wear on another using a regression model that establishes a quantitative relationship between the wear on the surfaces. This allows for a significant reduction in the labor intensity of defect detection work without compromising the reliability of the technical condition assessment of the component. This approach has been implemented using the example of defect detection on the camshaft of a KamAZ lorry engine. A hierarchical diagram of the camshaft structure as a system, where its individual elements are subsystems, is examined. It is noted that, among the surfaces of the camshaft, the cams and journal necks are subject to the most intense wear. A statistical model of camshaft wear has been constructed in the form of a linear regression equation, establishing a quantitative relationship between the wear of the journal necks and the cams. The practical significance of the proposed approach lies in improving the efficiency of defect detection work by reducing the volume of measurements by a factor of 2.6, which reduces the labor intensity of defect detection by 40%. When inspecting a batch of 100 camshafts, this results in a saving of 3,200 measurements, or over 36 hours. The results of this research have practical significance and are important for automotive repair enterprises engaged in the repair of internal combustion engine components.
Institution: Central Ukrainian National Technical University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7159904708
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