Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-135
Manufacturing High-Performance Machine Elements for Nanomaterials Using Adaptive Convolutional Neural Networks
Associate Professor,Department of Mechanical Engineering, Jiangxi Technical College of Manufacturing, Nanchang, 330095, China, E-mail: Ruixiang30Cao@outlook.com
Production Management
Received January 26, 2026; revised March 30, 2026; accepted June 19, 2026
Available online June 30, 2026
Abstract: With the continuous advancement of science and technology, nanomaterials have made great progress in the manufacturing of high-performance machine elements. With the development of mechanical engineering, the reliability and safety requirements for mechanical components are constantly increasing. Consequently, the application of nanomaterials in this field presents significant challenges. To enhance the reliability and safety of high-performance machine elements, this article analyzes the requirements and manufacturing principles of nanomaterials and such components. It then applies an adaptive Convolutional Neural Network (CNN) to apply nanomaterials to manufacture a high-performance machine element. This article tested the hardness, wear, and fault detection of machine elements, and compared them with two traditional manufacturing methods. The results showed that in terms of fault detection, the average failure rate of high-performance machine elements of ultra-precision reference gears manufactured through adaptive CNN application of nanomaterials was only 0.79%. From the test results, it can be seen that using an adaptive CNN to manufacture high-performance machine elements from nanomaterials has certain feasibility, which can effectively ensure the health status of the parts and promote the healthy development of high-performance machine element manufacturing.
Keywords: High-performance machine element; nanomaterial; adaptive convolutional neural network; part status detection and diagnosis.
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Citation: Cao, R. (2026). Manufacturing High-Performance Machine Elements for Nanomaterials Using Adaptive Convolutional Neural Networks. Journal of Engineering, Project, and Production Management, 16(6), 2026-135.
