Next Article in Journal
A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing
Previous Article in Journal
Drilling Rig Hoisting Platform Security Monitoring System Design and Application
Previous Article in Special Issue
A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages
Article Menu

Export Article

Open AccessArticle
Machines 2017, 5(3), 20; doi:10.3390/machines5030020

Root Cause Identification of Machining Error Based on Statistical Process Control and Fault Diagnosis of Machine Tools

Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Received: 3 April 2017 / Revised: 1 September 2017 / Accepted: 4 September 2017 / Published: 6 September 2017
(This article belongs to the Special Issue Advances in Process Machine Interactions)
View Full-Text   |   Download PDF [5265 KB, uploaded 6 September 2017]   |  

Abstract

The essence of the machining process is the interaction that occurs between machine tools and a workpiece under certain conditions of cutting parameters. Root cause identification (RCI) is critical to the quality control and productivity improvement of machining processes. The geometric error caused by fixture faults can be identified in most RCI methods; however, the influence of machine tool degradation on workpiece quality is usually neglected. In this paper, a novel root cause identification scheme of machining error based on statistical process control and fault diagnosis of machine tools is proposed. With the pattern recognition of control charts, quality fluctuations can be detected in a timely manner. Once the machining error occurs, the fault diagnosis of machine tools are carried out. The relationship between machine tool condition and workpiece quality is established and the root cause identification of the machining error can be achieved. A case study of the machining of a complex welded box-type workpiece is presented to illustrate the feasibility of the proposed scheme. It is found that the coaxiality error of the two holes in two sides of the box’s wall is caused by the wear of the worm gear in the rotary work table of the machine tool. View Full-Text
Keywords: root cause identification; machining error; statistical process control; fault diagnosis root cause identification; machining error; statistical process control; fault diagnosis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Cao, H.; Li, D.; Yue, Y. Root Cause Identification of Machining Error Based on Statistical Process Control and Fault Diagnosis of Machine Tools. Machines 2017, 5, 20.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Machines EISSN 2075-1702 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top