Improving machining accuracy in precision line boring

M. G. Mehrabi, G. O'Neal, B. K. Min, Z. Pasek, Y. Koren, P. Szuba

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


There is an ever-growing demand for high precision machining to obtain increased accuracy and surface finish, as they are key factors in product quality and performance. Machining operations, in general, are associated with errors of varying magnitude originating from different sources. As a result, the sizes of the machined features usually deviate from their desired, nominal values. Identification of error sources, techniques of measurements (on/off line), and efficient strategies for their compensation are the steps required to minimize, and, in some cases eliminate process errors. This paper focuses on modeling and compensation of geometric errors in machining operations specific to the line boring process. It is part of an undergoing research project focused on design and development of an agile precision line boring station for machining of long bores. After a brief overview of sources of geometric errors and their components, a methodology for their calculation is introduced. In this regard, error equations reflecting the effects of machine tool geometric errors at the tool tip are derived. It is shown that these equations can be further simplified without significantly affecting computational accuracy of the results. This makes the approach more attractive for real-time applications. A set of experimental data obtained from a prototype of the machine is used to study the effectiveness of the proposed approach and the results are reported. The paper concludes with discussions and presentation of different methods and available tools for real time compensation of these errors.

Original languageEnglish
Pages (from-to)379-389
Number of pages11
JournalJournal of Intelligent Manufacturing
Issue number5
Publication statusPublished - 2002 Oct

Bibliographical note

Funding Information:
The authors would like to appreciate the financial support of the NIST and the Lamb personnel in assisting some of the experimental setups.

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


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