Research on mechanical parts precision evaluation based on machine learning

Zhiwei Zhang

doi:10.1117/12.3050333

27 September 2024 Research on mechanical parts precision evaluation based on machine learning

Zhiwei Zhang

Proceedings Volume 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024); 1328409 (2024) https://doi.org/10.1117/12.3050333
Event: Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 2024, Hangzhou, China

Abstract

The machinery and equipment manufacturing industry are developing at an unprecedented speed, making the inspection of mechanical parts increasingly complex. In this work, we propose a neural network-based accuracy evaluation method for mechanical parts. This method involves the collection of a large number of machining and inspection data sets of mechanical parts, the removal of incomplete or erroneous data through data cleaning, the extraction of key parameters through the use of feature extraction techniques, and the implementation of standardized processing to ensure data consistency. A multi-layer feedforward neural network (MLP) was constructed, the ReLU activation function was employed to address the nonlinear transformation of the hidden layer, and the Adam optimization algorithm was utilized for training to enhance the convergence speed and stability of the model. The experimental results demonstrate that the proposed method is an effective and reliable approach for predicting the machining accuracy of mechanical parts, and it is well-suited to meet the needs of industrial processing sites.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiwei Zhang "Research on mechanical parts precision evaluation based on machine learning", Proc. SPIE 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 1328409 (27 September 2024); https://doi.org/10.1117/12.3050333

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Inspection

3D modeling

Machine vision

Manufacturing

Machine learning

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years