Mr. Jinfa Su Profile

Jinfa Su

at Xiamen Univ of Technology

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 November 2022 Paper

Research on on-machine detection method of CNC tool wear based on machine vision

Jinfa Su, Jianchun Liu, Zhongzhao Ye, Lulu Yin

Proceedings Volume 12448, 124480P (2022) https://doi.org/10.1117/12.2637330

KEYWORDS: Image segmentation, Image processing, Machine vision, Cameras, Edge detection, Image processing algorithms and systems

Read Abstract +

In view of the problem of machining stagnation and installation errors caused by the frequent unloading of the tool in the traditional offline inspection method of end mills, which seriously affects the inspection efficiency and inspection accuracy; this paper proposes a telescopic on-machine vision inspection method built on the side observation window of the machining center to realize On-machine inspection of end mill wear which is independent of machining center. For the end mill cutting edge wear detection algorithm, the improved OTSU is used to obtain the binarized image of the worn area, the canny operator and the sub-pixel edge are fused to extract the wear contour, and the original cutting edge is reconstructed by the least square method. Quick detection of end mill wear. The end milling experiment was carried out with the four-tooth carbide end mill as the object to study the nonlinear change of the wear rate of the tool under different wear degrees. The experimental results are in line with the wear change law in the tool life cycle: the wear rate in the initial and sharp wear stages has a large change range, and the change range in the normal wear stage is small; the results show that the measurement deviation of the detection system algorithm is less than 0.01mm, and the average accuracy rate reaches 95.03%, to ensure the accuracy and stability of the detection system.

Proceedings Article | 5 November 2020 Paper

Research on multi-objective optimization of decision scheme in CNC manufacturing process

Zian Liu, Kun Qin, Jianchun Liu, Jinfa Su

Proceedings Volume 11568, 115681M (2020) https://doi.org/10.1117/12.2580167

KEYWORDS: Optimization (mathematics), Manufacturing, Genetic algorithms, Surface roughness, Spindles, Energy efficiency, Data modeling, Radium, Performance modeling

Read Abstract +

From precision machining to green manufacturing, the search for an economical and effective green manufacturing chain system has gradually become the focus of the metal processing industry in order to ensure the quality of workshop manufacturing, increase the production capacity per unit of energy consumption, and reduce processing consumables. The existing research The focus is more on the establishment of the objective function model, the optimization method used and the targeted optimization direction are relatively single, and a more complete manufacturing system has not been established. There are three main optimization goals for processing process parameters in this subject: starting from the direction of processing quality, reducing the surface roughness of the workpiece as the optimization goal, establishing a cutting surface integrity (Ra) function; starting from the direction of enterprise manufacturing, to improve milling efficiency Optimize the objective and establish the cutting efficiency (SEC) function; starting from the processing cost, to reduce the amount of tool wear as the optimization objective, build a machine tool tool wear visual inspection mechanism to assist in the establishment of the tool wear (VB) function, and introduce a non-dominant sorting genetic algorithm to obtain Pareto After the frontier solution, different optimization suggestions are proposed for the refinement of rough/finish processing and general processing. The results show that the NSGA-Ⅱ model has a stronger search ability than the GA model when faced with multiple quasi-measurement decision problems. The processing efficiency can be increased by 42.7%, the tool cost can be saved by 25%, and the workpiece quality can be increased by 21.8%.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years