Dimensional deviation affected by cutting parameters and machine tool rigidity in dry turning of S45C steel
Performance of machining processes is assessed by dimensional and geometrical accuracy which is mentioned in this paper as dimensional deviation. A part quality does not depend solely on the depth of cut, feed rate and cutting speed. Other variable such as excessive machine tool vibration due to ins...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Trans Tech Publications
2013
|
Subjects: | |
Online Access: | http://eprints.utem.edu.my/id/eprint/7271/1/dimensional_accuracy_affected.pdf http://eprints.utem.edu.my/id/eprint/7271/ https://www.scientific.net/AMM.315.749 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Teknikal Malaysia Melaka |
Language: | English |
Summary: | Performance of machining processes is assessed by dimensional and geometrical accuracy which is mentioned in this paper as dimensional deviation. A part quality does not depend solely on the depth of cut, feed rate and cutting speed. Other variable such as excessive machine tool vibration due to insufficient dynamic rigidity can be deleterious to the desired results. The focus of the present study is to find a correlation between dimensional deviation against cutting parameters and machine tool vibration in dry turning. Hence cutting parameters and vibration-based regression model can be established for predicting the part dimensional deviation. Experiments are conducted using a Computerized Numerical Control (CNC) lathe with carbide insert cutting tool. Vibration data are collected through a data acquisition system, then tested and analyzed through statistical analysis. The analysis revealed that machine tool vibration has significant effect on dimensional deviation where statistical analysis of individual regression coefficients showed p<0.05. The developed regression model has been validated through experimental tests and found to be reliable to predict dimensional deviation. |
---|