Tool monitoring system for end-milling process using an MEMS accelerometer, PC sound card and a personal computer

Clicks: 2
ID: 286690
2006
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #2,948 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
A micro-electromechanical system (MEMS) accelerometer, an ordinary PC sound card and a personal computer were used to build a tool monitoring system to detect impending tool failure in end-milling process. The acceleration data from the machining process were acquired by the MEMS accelerometer and the sound card, and processed using Matlab in the personal computer. Using Matlab's ARYULE function to determine the autoregressive model parameters to fit the acceleration data, the modal energies of the data were isolated and plotted, and compared with the ideal tool wear curve. The energy plots of the first, second, third and fourth multiples of the tooth pass frequencies were considered in the study. A tool failure detection algorithm based on the plots was developed to monitor the tool wear and provided a means of predicting impending tool failure. Nine (9) tests were conducted to determine the applicability of the combination of the MEMS accelerometer and sound card in gathering and processing acceleration data for on-line tool monitoring. In the tests, three (3) sizes of tools were used to machine mild steel in a machining center using three cutting methods: the zig method, the follow periphery, and the follow part. The energy plots showed an increase in vibration energy as machining progresses and they showed similarity with the ideal tool wear curve. By developing and implementing a tool failure detection algorithm, the system was able to predict impending tool failure in at least two of the four energy plots monitored in the tests.
Reference Key
persistent_1760659351_68f18797e8786 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Catalan, Paul Maderal
Journal Malay Journal
Year 2006
DOI
DOI not found
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.